Keywords: Ant Colony Intelligence, Pheromone Dynamic Scheduling, Ant Colony Intelligence, Pheromone, Multi Agent Systems, Discrete Event Simulation

Job shop scheduling problem is a NP-hard problem, therefore the objective is to create a schedule that satisfies all the constraints while taking as little overall time as possible. The paper concerns the job shop scheduling problem in cellular manufacturing systems; the schedule is created by a pheromone based approach. The proposed approach is carried out by a Multi Agent Architecture and it is compared with a coordination approach proposed in literature used as a benchmark.

A simulation environment developed in ARENA® package was used to implement the approaches and evaluate the performance measures. The performance measures investigated are: throughput time, throughput, Work In Process, machines average utilization and tardiness. Several scenarios are considered: from static to very dynamic conditions for internal and external exceptions of the manufacturing system. The simulation results highlighted that the performance of the proposed approach are comparable with the benchmark when the customer demand has a high fluctuation and the manufacturing system is less dynamic.

Introduction

The environment within which manufacturing systems operate is characterized by rapid change than ever before. The unforeseen disturbances occur frequently and the manufacturing system has to be able to react to these disturbances. The disturbances can be: machine breakdowns, demand variability and delay in processing time. The above reasons drive the manufacturing systems to adopt dynamic scheduling approaches.

Dynamic scheduling has been defined under four categories [START_REF] Mehta | Predictable scheduling of a single machine subject to breakdowns[END_REF]Vieira et al., 2000a[START_REF] Vieira | Rescheduling manufacturing systems: a framework of strategies, policies and methods[END_REF][START_REF] Aytug | Execting production schedules in the face of uncertainties: A review and some future directions[END_REF][START_REF] Leus | The complexity of machine scheduling for stability with a single disrupted job[END_REF]: predictive-reactive scheduling; a schedule is created in advance, but a rescheduling is considering to respond to exceptions in real-time.

robust predictive-reactive scheduling; a schedule is created in advance and rescheduling activity is activated when the effect on the performance measures of the exceptions is significantly.

robust pro-active scheduling; the schedule is computed in advance predicting the effect of exceptions on the manufacturing system.

The dynamic scheduling problem can be solved by using the following techniques:

mathematical programming approach; -dispatching rules approach; -heuristic approach;

artificial intelligence approach.

Moreover, most of the scheduling systems developed in industrial environments are centralized and hierarchical, but these approaches present several drawbacks. The most important drawbacks are the following:

a central computer; it constitutes a bottleneck with a limit of capacity and a failure of it leads to bring down the entire manufacturing system.

it is more difficult to extend and modify the configuration of the scheduling system; -a slow response to disturbance because the information has to flow to the high level and then a reaction is planned.

Therefore, a centralized and hierarchical scheduling is inefficient in a very dynamic environment where the exceptions are more frequently.

For the above reasons, a decentralized control method is more efficient in a dynamic environment in particular, Multi Agent System methodology is more suitable for the implementation of decentralized system. A Multi Agent System (MAS) can be defined as a network of problem solvers that work together to solve problems that are beyond their individual capabilities (O'Hare and Jennings, 1996).

MAS approaches are more suitable to develop agile and robust distributed control, but its performance is more dependent by the coordination mechanism. The coordination mechanisms proposed in literature are the Contract net protocol [START_REF] Smith | The Contract Net Protocol: high level communication and control in a distributed problem solver[END_REF], market based, auction based [START_REF] Siwamogsatham | Auction-based distributed scheduling and control scheme for flexible manufacturing systems[END_REF]) and game theory. These approaches have same limitations as: communication overhead, constantly exchange of information and therefore a minor reactivity of the agents.

Recently, many authors have developed several approaches inspired by the behavior of social insects like ants, bees, termites and wasp to propose an alternative method for coordination in complex systems. The most promising approach is based on Ant Colony; Ant Colony coordination refers to the cooperative ant foraging behavior. Ant colonies can always find shorter paths from a nest to a food source by pheromone trail laying and following. [START_REF] Dorigo | Distributed optimization by ant colonies[END_REF] first introduced Ant Colony Optimization (ACO) for solving the Traveling Salesman Problem, which is based on ant foraging. In manufacturing scheduling problem, the approach can be inspired to pheromonebased rule of Ant Colony to coordinate the MAS architecture.

The aim of this paper is to investigate the performance of a pheromone approach for cellular manufacturing systems in a very dynamic environment. The pheromone approaches proposed are two: one based on moving average and the other one on exponential moving average.

Two different types of disturbances are considered:

internal exceptions; these exceptions are caused by the resources of the manufacturing system, in particular, they are: machine breakdowns and efficiency of the manufacturing machine.

external exceptions; these exceptions are caused by external changes of the manufacturing system, specifically they are: changes in volume and mix demand products. Moreover, it is considered the effect of dispatching rule on the performance of the manufacturing system. The performance are compared with an approach proposed in literature based on classical coordination mechanism in MAS.

The structure of the paper is as follows: in Section 2 an overview of literature is presented. The manufacturing context is described in Section 3. In Section 4 the proposed coordination approaches based on pheromone are explained. The simulation environment and design of experiment conducted are given in Section 5, while in Section 6 the simulation results are presented. Finally, conclusions and future research path are presented in Section 7.

Literature review

Recently, a few researchers have developed approaches based on Ant Colony inspired scheduling in shop floor control. Most of the applications proposed concern the shop floor routing and permutation flow-shop sequencing problem [START_REF] Peeters | Pheromone based emergent shop floor control system for flexible flow shops[END_REF] has been developed a concept control system based on coordination mechanism of insect colonies, in particularly, a pheromone based control scheme is introduced. Basic principles of the pheromone concept, the control system architecture and a layered approach for decisionmaking have been discussed. Test beds of industrial scale have been used to demonstrate properties and benefits of this approach. The main advantages emerged for the pheromone approaches are: a simple coordination mechanism; the automatic guidance to the optimized solution; the capability to handle dynamic situations. [START_REF] Yu | Bio-inspired scheduling for dynamic job shops with flexible routing and sequence dependent setups[END_REF] proposed Multi Agent approach designed for dynamic job shops with routing flexibility and sequence-dependent setup. A bio-inspired strategy based on division of labour in insect societies is presented for coordination among agents. The strategy is accomplished using a computational model, which is composed of response threshold, response intention, and machinecentred reinforcement learning. The bio-inspired scheduling is compared with an agent-based approach and a dispatching rule-based approach. The experiments were performed using simulation the proposed approaches in literature are tested in manufacturing systems in which some exceptions occur and the analysis on the rapidity of alterations was not investigated.

each model proposed is tested on few performance measures (for example only throughput performance);

The main findings of the research proposed in this paper can be summarized as it follows:

the pheromone approach is deeply investigated by an adequate number of performance measures;

the simulations have been conducted in a several dynamicity levels introducing both internal and external disturbances;

the pheromone approaches have been compared with a complex MAS approach in order to find out in which environment conditions the proposed approach can be competitive.

Manufacturing system context

The manufacturing system consists of a given number of cells; each cell is able to perform any kind of manufacturing operation so that the resulting manufacturing system is a pure general-purpose one. In such a system, the scheduling decision consists in deciding in what manufacturing cell the part will perform the next operation, therefore a pure dispatching problem. The manufacturing system has the following characteristics:

-several part types have to be manufactured by the manufacturing system. Each part type has a predefined number of operations performed by the manufacturing cells. At each part is assigned a due date.

-orders for production of different parts arrive randomly with an inter-arrival that is exponential distribution; -each machine performs the manufacturing operation with an efficiency, which sets the speed of the operation.

-the queues are managed by the First In First Out policy in order to investigate only the pheromone approaches policy.

-each machine can breakdown randomly with an exponential distribution.

In this research, the transportation time of the material handling devices are included in the processing time, and the handling resources are always available.

The coordination approach based on Ant Colony is inspired by the behavior of foraging ants that leave a pheromone trail on their way to the food. In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep traveling at random, but instead follow the trail, returning and reinforcing it if they eventually find food. Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength.

Ant Colony is a natural example of highly distributed system, therefore, it can be formalized by Multi Agent System. The coordination mechanism based on Ant Colony has the following advantages:

-The simplicity of coordination mechanism. In fact, the ants do not communicate directly each other, but the communication is performed by the pheromone released on the environment. The ants have to know how to put information and how to get information from the environment.

-The evaporation of the pheromone is the methodology how this coordination approach is capable to handle dynamic situations.

The drawbacks of pheromone approaches are the following:

-The delay of the information; the ants release a pheromone based on its activity; the other ants put this information with a delay time.

-The evaporation rate is a parameter to set, therefore an opportune study on the specific problem modeled has to be conduct. In a manufacturing system the ants are the parts that flow through the manufacturing cells; the manufacturing cells are the nodes that ant must visit. When a part leaves a manufacturing cell, it deposits a pheromone based on the throughput time in the manufacturing cell.

Therefore, the problems are mainly two:

how the pheromone information are formalized, what type of information can be included;

the methodology of pheromone evaporation.

The activities of Ant Colony algorithm for scheduling in manufacturing systems are the following:

-Initializing pheromone for each manufacturing cell;

-Update pheromone for each manufacturing cell;

-Pheromone evaporation.

Pheromone approach

In this paper, two pheromone formulations have been proposed: the first is based on moving average of the information deposited by the parts; the second is based on exponential moving average. The two formulations are an adaptation of the approaches proposed, respectively in [START_REF] Scholz-Reiter | Bio-inspired and pheromone-based shop floor control[END_REF] and [START_REF] Renna | A Performance Comparison between Efficiency and Pheromone Approaches in Dynamic Manufacturing Scheduling[END_REF]. The main difference between the two approaches is the data stored; in moving average the amount of data for the computation is higher than the exponential moving average.

The information deposited by the parts for the pheromone computation is the throughput time of a part (ant) that is processed on a manufacturing cell (node).

In particular, the value deposited by a part is computed by the following expression:

1 = - value tnow tin (1)
where, tnow is the time when the part leaves the manufacturing cell;

tin is the arrival time in the manufacturing cell of the part. From the above expression, the difference (tnow-tin) concerns the throughput time of the part in the manufacturing cell. Therefore, if the throughput time is low, then the pheromone deposited by the part is high.

The value of pheromone at start time of the simulation is obtained by the following expression:

1 = sin value proces g time (2)
where,

processing time is the value of processing time performed by the manufacturing cell.

Pheromone formulation I

The evaporation strategy of this approach is obtained by the computation of moving average of the value computed in expression 2. The pheromone is computed by moving average over last N value deposited by the parts (expression 2). The older values deposited are not included, then, this emulates the evaporation of the pheromone.

1 1 N j i i pheromone ( t ) value N = = ∑ (3)
Therefore, according to above description, the activities are the following:

initialize pheromone (expression 2); update pheromone (expression 1); pheromone evaporation (expression 3).

The probability to select a manufacturing cell by the part agent, is in direct ratio to the value of pheromone j (t); specifically, the probability to select a manufacturing cell j is

∑ ( ) ( ) j j j pheromone t pheromone t
, with j=1,….,number of manufacturing cells.

This formulation of pheromone is a look back methodology, because the pheromone value is computed by past events. For example, if a machine is in breakdown status, the pheromone is reduced with a delay time that the other parts can "sniff".

Pheromone formulation II

The difference from the first formulation is the evaporation strategy based on exponential moving average. The pheromone is computed by the following expression:

1 1 j j pheromone ( t ) ( ) pheromone ( t ) value α α = -• -+ • (4)
The above expression is the average between the last value memorized (pheromone j (t-1)) and the value computed by the part that leaves the manufacturing cell (value).

The value α (it is in range [0,1]) emulates the evaporation of the pheromone. In particular, a high value of α leads to high evaporation because the value is more significant and vice versa for low value of α. Therefore, according to above description, the activities are the following:

initialize pheromone (expression 2);

update pheromone (expression 1);

evaporation pheromone (expression 4).

The efficiency based approach (benchmark Renna et al. (2001))

Briefly, in this approach, the MAS is composed by two types of agent: part agent and manufacturing cell agent. The productive function of the manufacturing cell agent consists in evaluating and providing to the part agent the following three parameters: Expected Part Throughput Time (ETT); Resource Failure Index (RFI); Resource Processing Time Index (RPTI).

The ETT is computed by the following expression:

= + ( ) ( ) k k medwtime StdFlowTime t medwtime FlowTime t
(5) medwtime is the expected processing time of the parts. resource is in idle state; it decreases with the increase of parts in queue. The RFI is computed by the following expression

t t FT t RFI k k ) ( 1 ) ( - = (6)
where, ( ) k FT t is the total time of failure status of the resource k until the negotiation time t.

The RPT is the processing time in the generic manufacturing cell, it is obtained by the eff k (t) (see

table 2) multiplied to medwtime.
Each parameter is normalized for each k-th resource, by the following expression:

- = - max ( ) ( ) ( ) max ( ) min ( ) k k k k k index t index t Normalized Index t index t index t (7)
Then, each index is normalized between the maximum and minimum values of the indexes among all the manufacturing cells.

The above indexes are composed by the following expression:

= ⋅ + -⋅ + (1 ) ( ) Eff Index w NormEPPT w NormRFI NormRPT (8)
The part agent selects the manufacturing cell that has the high value of efficiency. In this paper the value of weight w=0.4 that leads to better performance.

The above approach needs to implement a Multi Agent infrastructure with an exchange of information bi-directional and with a computation of this information. Therefore, the approach is more complex in terms of infrastructure and computational time.

Simulation environment

In order to evaluate the performance of the proposed approaches a simulation environment is developed. The author selected the Arena® discrete event simulation platform by Rockwell Software Inc. it was used to develop the simulation model of the presented approaches. The manufacturing system consists of four general-purpose manufacturing cells that are called to manufacture a set of four different parts. In order to emulate a dynamic environment the proposed approaches have been tested through a production run consisting of several alternating stages; each The processing time of a manufacturing cell is characterized by an efficiency parameter (eff); in particular, the processing time is obtained by the following expression:

= • sin j proces g time eff ePT (9)
where, ePT is the expected Processing Time (medwtime) for all the parts equal to 30 minutes.

[Insert table 1 about here]

Table 1 reports the experimental classes tested; the parameters concerning:

-Manufacturing operations; it is the number of manufacturing operations that the parts need. The classes from one to twenty four are characterized by the number of operations equal for all the parts, while from twenty five to thirty nine classes the manufacturing operations are different for the parts and the values are reported in table 3.

-Mix; it is the mix of the four typology parts reported in table 4.

-Internal disturbance; it is the static or dynamic change of machine breakdowns and efficiency parameters. The values are reported in table 2.

-Inter-arrival time dynamic; it is the dynamic change of the inter-arrival time of the parts (see table 5).

-Congestion level; it is the level of workload of the manufacturing system. In case of dynamic inter-arrival time the congestion level is variable over the simulation length.

-Dynamicity level; it is the rapidity changing of dynamic conditions (see table 6).

The evaporation parameter of the pheromone approach I is carried out by the computation of the last five value deposited. The simulations conducted for different values of N (see equation 3)

highlight that five is the number of N that leads to better results.

The evaporation parameter of the pheromone approach II α is fixed to 0.5 in order to evaluate with the same importance between the last value stored and the new pheromone deposited by the part. Table 2 reports the parameter of the exponential distribution of MTBF and Efficiency (Eff) for each manufacturing cell and each stage. As the reader can notice, the MTBF and Eff decrease the performance of the manufacturing cell in order to emulate a degradation of the machines.

The MTTR is related to the processing time by the following expression:

Mean Time To Repair =1.5*ePT.

[Insert table 2 about here]

Table 3 reports the number of visit of the four typology parts in order to investigate the performance over the number of visit and for different number of visit for the typology parts.

[Insert table 3 about here]

Table 4 reports the mix changes over the stages of the simulation.

[Insert table 4 about here]

Parts enter the system following an exponential arrival stream; the inter-arrival times (exponential parameter) are computed for the different experimental classes in order to keep the three levels of workload (congestion) reported in Then, the inter-arrival time parameter is computed in order to obtain a value of expression 10 respectively:

workload index=1.2 for low congestion level;

workload index=1 for medium congestion level;

workload index =0.8 for high congestion level.

Finally, the dynamic change of the inter-arrival time is reported in table 5, in which low and high fluctuation of the inter-arrival parameter are considered. To model a highly dynamic demand, the inter-arrival time is oscillating between two values with situation of over and under load. Table 5 reports two levels of fluctuation of the inter-arrival time.

[Insert table 5 about here]

Moreover, specific due date has been assigned to each part entering the system. Denoting with t j now the arrival time of a specific part j, its due date follows an uniform distribution in the range has given in equation ( 11). The objective is to investigated the difference among the proposed approaches and benchmark, therefore the range in equation ( 11) has minor importance.

If the part leaves the system after this due date, a penalty is computed equal to such a delay (tardiness).

[3,8]

now j j dd t UNIFORM ePT OpNumb = + • • (11)
where OpNumb is the number of visit of the specific part j.

The proposed approaches are tested in static and highly dynamic situations; the dynamicity of the manufacturing system is characterized by the stage length factor. The simulation length is fixed to 14400 time units in order to avoid transitory influence. During the stage length the characteristics of the manufacturing system are constant, therefore the rapid succession of stages control the dynamicity of the manufacturing system conditions. The length of the stage is related to the expected processing time by the stage length factor : For each experimental class ( expect for static classes) the simulations are conducted for three levels of dynamicity as reported in table 6. The level low leads to repeat each stage once, level medium leads to repeat each stage twice and level high leads to repeat each stage three times.

[Insert table 6 about here]

Furthermore, because of the parameters extracted by the exponential distribution (inter-arrival time, machine breakdown), and in order to guarantee a statistical validity of the results, for each run, the number of executed replications guarantees, for the output performance measures, that the length of confidence intervals (95% level) of the mean among replications is lower than 5% of the mean itself. The performance measures investigated are: throughput time, throughput, Work In Process, machines average utilization and tardiness.

Simulation Results

The simulation results have been reported in the following figures. The Figures report the percentage difference of the proposed pheromone approaches compared to efficiency approach (base approach for determining percentage difference).

[Insert figure 1 about here]

Figure 1 reports the average flow time in static and dynamic conditions. The proposed approaches lead to best performance when the system is in high utilization state with internal exceptions (Exp.

No. 10,11 and 12 see table 1), and the exponential approach is always the best approach.

Concerning the throughput, the difference among the approaches is always under one percent.

Therefore, the three approaches (efficiency and the two pheromone formulations) have the same level of throughput. The Work In Process measure has the same trend of the flow time performance.

The average utilization of the manufacturing system is the same among the different approaches.

[Insert figure 2 Figure 2 reports the tardiness performance. The better performance is obtained when the system is in high congestion level with internal exceptions, however the pheromone approaches lead to a great reduction of this performance.

All the performance measures get worse when the dynamicity increases and the congestion level decreases. Therefore, the proposed approaches are suitable to be used in condition of low or medium dynamicity of internal exceptions and when the congestion level is high. The exponential moving average is better than the moving average.

The above simulation results have been conducted in a manufacturing system where the parts require only one manufacturing operation. Then, the approaches proposed are tested when the manufacturing operations increasing and they are different for each typology part.

[Insert figure 3 about here]

Figure 3 shows that the flow time is the same for different number of operations and when it is introduced the mix dynamicity among the typology parts. Except the case with three manufacturing operations for all the typology parts, in this case the performance of the proposed approaches are better than the other cases (Exp. No. 20 and 21). However, the number of manufacturing operations has a low effect on the performance. The Work In Process performance has the same trend.

[Insert figure 4 about here]

The throughput performance decreases in a manufacturing system with three or four operations for all the part typology, while this performance is the same of the efficiency approach in case of manufacturing operations different for the typology part and with mix dynamicity (as showed in figure 4). Then the number of operations leads to reduce the throughput of the manufacturing system that adopts the pheromone formulations.

[Insert figure 5 about here]

Figure 5 shows that the different manufacturing operations for the product typology and the mix dynamicity lead to worst tardiness measure. Moreover, the performance get worse when the A further exception is introduced; in particular a fluctuation of the inter-arrival time between two values that lead to over and under loaded of the manufacturing system. Two levels are considered: a low range between the two values of inter-arrival; a high range between the two values of interarrival.

[Insert figure 6 about here]

From the figure 6 two issues can be drawn:

-If the oscillation of the inter-arrival time is very wide, the performance of the pheromone approaches are competitive with the efficiency approach;

-The advantage of the efficiency approach increases with the increase of the dynamicity of the system.

Therefore, the pheromone approaches improve its performance when the external exceptions are considerable. The Work In Process performance has the same trend.

[Insert figure 7 about here]

In todays competitive environment, a high level of delivery performance has become very important. For this reason, it is analyzed how the earliest due date (EDD) rule can improve the performance of the proposed approaches. Then, the queues of the manufacturing cells are managed by the EDD rule. Figure 8 shows that the pheromone approach decreases the performance index with the EDD rule. The moving average has a tremendous decrement of the performance, while the exponential moving average reduction is limited. The same trend is verified for the tardiness performance. Therefore, the EDD rule leads to reduction all the performance measures.

[Insert figure 8 about here]

The same trend is verified for the tardiness performance (see figure 9). Therefore, the EDD rule leads to reduction all the performance measures and the due date performance too (tardiness).

[Insert figure 9 about here]

Conclusions and future development

The research presented a deeply discussion on dynamic scheduling by pheromone approach. This approach is inspired by Ant Colony Intelligence; the pheromone approach is simpler than traditional approaches based on MAS coordination (contract net, market like, etc.), but the performance have to be investigated. A simulation environment has been developed to test the proposed approaches in a generic manufacturing system. The simulations have been conducted is several conditions and in a very dynamic environment with internal (machine breakdowns and machine efficiency) and external (mix and inter-arrival time) exceptions. The pheromone formulation is based on two approaches: moving average and exponential moving average. Moreover, a benchmark approach is used to evaluate the performance measures.

The results of this research can be summarized as it follows:

-The throughput and the average utilization of the manufacturing system are the performance measures that have the same values of the benchmark.

-The pheromone approach is competitive when internal and external exceptions occur and the fluctuation of the inter-arrival demand is high.

-The pheromone approach based on exponential moving average outperforms the moving average; therefore the exponential moving average reacts faster to the manufacturing system exceptions. This is important, because the exponential moving average is simpler than the moving average for the data management.

-A significant result is how the pheromone approaches react to the dispatching rules. In this research is introduced the EDD rule; the simulation results show that the moving average leads to reduce dramatically the performance, while for the exponential moving average the reduction is minor. Therefore, the dispatching rules have to be integrated in the pheromone computation. The reduction of the performance when the dynamicity increases is caused by the delay information of pheromone approach; when the dynamicity is higher than the stage length the pheromone approach is unable to compete with efficiency approach.

A further research paths can be the following:

-The influence of the manufacturing system size on the performance measure. The study of the performance measures when a high number of manufacturing cells characterizes the manufacturing system. The increase of the manufacturing cells lead to a more complex scheduling problem; therefore a deeply investigation of the pheromone approach responsiveness to a complexity of scheduling problem has been performed.

-

The extension of the research to the material handling system and the development of the pheromone approach for both scheduling and routing of the parts.

-The development of hybrid approach; in this case, the dynamic scheduling approach can be adapted to the environment conditions. Therefore, when the dynamicity is high, complex method is activated, otherwise the pheromone approach can be performed. This allows reducing the computational time and complexity to dynamic scheduling in manufacturing systems.

F

-

  completely reactive scheduling; in this case no schedule in advance is created and the job schedule is obtained in a real-time fashion.

  FlowTime k (t) is the expected manufacturing cell k-th throughput time computed by summing up the processing times of the parts waiting in the manufacturing cell queue plus the residual service time of the part being worked in the machine at the negotiation time t. This index is the measure of workload of the generic manufacturing cell. The index value is one if no parts are in queue and the

  by different internal (efficiency of the manufacturing cells, and machine breakdowns) and external attributes (mix and inter-arrival demand).

  It has been assumed that all the manufacturing cells are subject to faults, and failures occur in accordance with exponentially distributed time between failures, with mean time between failure (MTBF). Repairing times are also exponentially distributed with mean time to repair (MTTR).

  about here]

  . This confirms that the pheromone approaches are worst when external exceptions occur.

-

  Figure 1. Average flow time with one manufacturing operation -Figure 2. Tardiness with one manufacturing operation -Figure 3. Average flow time with several manufacturing operations -Figure 4. Throughput with several manufacturing operations -Figure 5. Tardiness with several manufacturing operations -Figure 6. Average flow time with inter-arrival fluctuation -Figure 7. Tardiness with inter-arrival fluctuation

  and statistical analysis. Results show that the proposed bio-inspired scheduling model performs better than the other two methods on all eight common scheduling metrics.[START_REF] Xiang | Ant colony intelligence in multi-agent dynamic manufacturing scheduling[END_REF] presented research concerning dynamic scheduling by agent coordination that is inspired on Ant Colony in MAS. The proposed approach is tested in a shop floor model considers multiple job types and parallel multi-purpose machines with dependent setup times.Moreover, the machine break down is included as a disturbance of the manufacturing system. In this research, the Ant Colony is used to find an appropriate machine agent for processing and helps the machine agent to determine the next job to be processed in the current queue.

	-a benchmark based on MAS architecture with complex coordination mechanism wasn't used in
	order to evaluate the advantages and disadvantages of Ant Colony coordination mechanisms;
	F o r F o r
	P e Scholz-Reiter et al. (2008) proposed a pheromone-based autonomous control method to a mix P model of a shop floor and tested it in different dynamic demand situations. They compared the e
	e r pheromone approach with a method based on queue length estimator. The only performance is the throughput time with a sinusoidal arrival rate of the parts. e r
	R Zhou et al. (2009) proposed an algorithm based on Ant Colony Optimization in a shop floor R
	e scenario with three levels of machine utilization, three different processing time distributions, and e
	v i e w three different performance measures for intermediate scheduling problems. The performances v i e measured are: mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest w
	O n l y processing time, and minimum slack time. The experimental results show that ACO outperforms other approaches when the machine utilization or the variation of processing times is not high. The O n l procedure proposed is a centralized approach. y
	Renna (2009) developed two pheromone approaches for the job shop scheduling problem. One is
	based on the past information of a part (throughput time of the manufacturing cell); and the other on
	the queue of the manufacturing cell. The proposed approaches are tested in dynamic environment;
	the simulation results show how the approach based on the queue of the manufacturing cell
	performs better when the environment conditions are very dynamic.
	Based on the above literature review, the following limitations can be drawn:

table 1

 1

	. The workload index allows to keep the same congestion

Table 6 .

 6 Experiment classes

	o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

  No. 31 Exp.No. 32 Exp.No. 33 Exp.No. 34 Exp.No. 35 Exp.No. 36

	-Figure 8. Earliest Due Date Rule e r e r e r e r e r e r e r e r r e -Figure 9. Tardiness -Earliest Due Date Rule F o r P e 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00% E x p . N o . 1 E x p . N o . 2 E x p . N o . 3 E x p . N o . 4 E x p . N o . 5 E x p . N o . 6 E x p . N o . 7 E x p . N o . 8 E x p . N o . 9 E x p . N o . 1 0 E x p . N o . 1 1 E x p . N o . 1 2 E x p . N o . 1 3 E x p . N o . 1 4 E x p . N o . 1 5 Figure 1. Average flow time with one manufacturing operation avg flow time mob avg flow time exp F o r P e 0.00% 100.00% 200.00% 300.00% 400.00% 500.00% 600.00% 700.00% 800.00% 900.00% 1000.00% 1200.00% 1300.00% 1400.00% 1500.00% 1600.00% 70.00% 80.00% 90.00% 0.00% E x p . N o . 1 3 E x p . N o . 1 4 E x p . N o . 1 5 E x p . N o . 1 6 E x p . N o . 1 7 E x p . N o . 1 8 E x p . N o . 1 9 E x p . N o . 2 0 E x p . N o . 2 1 E x p . N o . 2 2 E x p . N o . 2 3 E x p . N o . 2 4 E x p . N o . 2 5 E x p . N o . 2 6 E x p . N o . 2 7 E x p . N o . 2 8 E x p . N o . 2 9 600.00% 50.00% 350.00% 375.00% 400.00% 180.00% 200.00% 4500,00% 5000,00% E x p . N o . 3 0 500.00% 40.00% 300.00% 325.00% 160.00% 4000,00% 1100.00% E x p . N o . 1 E x p . N o . 2 E x p . N o . 3 E x p . N o . 4 E x p . N o . 5 E x p . N o . 6 E x p . N o . 7 E x p . N o . 8 E x p . N o . 9 E x p . N o . 1 0 E x p . N o . 1 1 E x p . N o . 1 2 E x p . N o . 1 3 E x p . N o . 1 4 E x p . N o . 1 5 tardiness mob tardiness exp Figure 2. Tardiness with one manufacturing operation F o r P e 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% E x p . N o . 1 3 E x p . N o . 1 4 E x p . N o . 1 5 E x p . N o . 1 6 E x p . N o . 1 7 E x p . N o . 1 8 E x p . N o . 1 9 E x p . N o . 2 0 E x p . N o . 2 1 E x p . N o . 2 2 E x p . N o . 2 3 E x p . N o . 2 4 E x p . N o . 2 5 E x p . N o . 2 6 E x p . N o . 2 7 E x p . N o . 2 8 E x p . N o . 2 9 E x p . N o . 3 0 avg flow time mob avg flow time exp Figure 3. Average flow time with several manufacturing operations F o r P e -12.00% -10.00% -8.00% -6.00% -4.00% -2.00% throughput mob throughput exp Figure 4. Throughput with several manufacturing operations F o r P e 0.00% 100.00% 200.00% 300.00% 400.00% E x p . N o . 1 3 E x p . N o . 1 4 E x p . N o . 1 5 E x p . N o . 1 6 E x p . N o . 1 7 E x p . N o . 1 8 E x p . N o . 1 9 E x p . N o . 2 0 E x p . N o . 2 1 E x p . N o . 2 2 E x p . N o . 2 3 E x p . N o . 2 4 E x p . N o . 2 5 E x p . N o . 2 6 E x p . N o . 2 7 E x p . N o . 2 8 E x p . N o . 2 9 tardiness mob tardiness exp F o r 0.00% 10.00% 20.00% 30.00% Exp.No. 31 Exp.No. 32 Exp.No. 33 Exp.No. 34 Exp.No. 35 Exp.No. 36 140.00% 275.00% 3500,00% 250.00% 120.00% avg flow time mob avg flow time exp Figure 6. Average flow time with inter-arrival fluctuation F o r 0.00% 25.00% 50.00% 75.00% 100.00% 125.00% 150.00% 175.00% 200.00% 225.00% tardiness exp 40.00% 60.00% 80.00% 100.00% avg flow time WIP 1500,00% 2000,00% 2500,00% 3000,00% tardiness Exp.tardiness mob Figure 7. Tardiness with inter-arrival fluctuation F o r 0.00% 20.00% mob mob EDD exp exp EDD Figure 8. Earliest Due Date Rule 1000,00% F 0,00% 500,00% o r mob mob EDD exp exp EDD E x p . N o . 3 0 e e e e Figure 5. Tardiness with several manufacturing operations P P P P Figure 9.Tardiness -Earliest Due Date Rule
	R R R R R R R R R
	e e e e e e e e e
	v i e v i e v i e v i e v i e v i e v i e v i e v i e
	w w w w w w w w w
	O n l O n l O n l O n l O n l O n l O n l O n l O n l
	y y y y y y y y y

URL: http://mc.manuscriptcentral.com/tandf/tcim Email:ijcim@bath.ac.ukInternational Journal of Computer Integrated Manufacturing


V Kalinina Ayuso
email: v.kalininaayuso@umcutrecht.nl

H A T Ten Cate

P Van Den Does

A Rothova

J H De Boer

MD V Kalinina Ayuso

Young age as a risk factor for complicated course and visual outcome in intermediate uveitis in children

Objective: To identify prognostic factors in intermediate uveitis (IU) in children. Methods: Retrospective case series of 35 patients with onset of IU ≤ 16 years and a minimum follow-up of 1 year. Demographic and numerous clinical data were documented. Visual outcomes and development of complications were analyzed in relation to age of onset and ocular signs at presentation.

Results: Forty-six percent of patients had onset ≤7 years and in 54% >7 years. The younger onset group had shorter event-free survival for secondary glaucoma (p=0.04) and vitreous hemorrhage (p=0.01). Mean age of onset in children with cataract (5.9 versus 8.7 years), glaucoma (5.0 versus 8.4) and vitreous hemorrhage (5.6 versus 8.5) was lower then in children without these complications (all p=0.03). Frequencies of other complications did not differ between both groups. The younger onset group had worse BCVA's at presentation (0.3 versus 0.6), at 1 year (0.4 versus 0.9) and at 3 years followup (0.6 versus 0.9; all p≤0.04) and they needed longer treatment (p=0.01). Children with young onset of IU reached remission less frequently (p=0.05). Development of cystoid macular edema (CME) was independently associated with papillitis (adjusted HR=3.4; p=0.02) and snowbanking (adjusted HR=3.3; p=0.03) at presentation. Other complications at onset were not predictive for future complications.

Conclusions:

Children with young onset of IU carry higher risk of complications and worse visual outcome. We would recommend considering more intensive monitoring and earlier threshold for systemic treatment in those children with risk factors as early onset, papillitis and/or snowbanking at initial presentation.

Introduction

Intermediate uveitis (IU) occurs in up to 42% of the pediatric uveitis population and is the second most common form of uveitis in childhood. [START_REF] Nagpal | Epidemiology of uveitis in children[END_REF] Idiopatic IU is characterized by a chronic inflammatory process primarily involving the vitreous, posterior ciliary body and peripheral retina. [START_REF] Jabs | Standardization of uveitis nomenclature for reporting clinical data. Results of the First International Workshop[END_REF] Pars planitis (PP) is a form of idiopathic IU characterized by snowballs and snowbanking at the pars plana and peripheral retina in the absence of systemic diseases. [START_REF] Jabs | Standardization of uveitis nomenclature for reporting clinical data. Results of the First International Workshop[END_REF] IU in adults can occasionally be associated with systemic disorders or infections; however, in most children the etiology of IU remains unknown. The understanding of the pathogenesis of IU is still limited; involvement of auto-immune processes and/or a genetic predisposition [START_REF] Alaez | Classic Pars Planitis: strong correlation of class II genes with gender and some clinical features in Mexican Mestizos[END_REF][START_REF] Raja | Pars planitis: clinical features and class II HLA associations[END_REF][START_REF] Cuccia | Genetic heterogeneity in uveitis[END_REF][START_REF] Martin | Association of intermediate uveitis with HLA-A28: definition of a new systemic syndrome?[END_REF][START_REF] Tang | The association of HLA-DR15 and intermediate uveitis[END_REF] have been suggested and several familial cases have been reported. [START_REF] Culbertson | Familial pars planitis[END_REF][START_REF] Augsburger | Familial pars planitis[END_REF][START_REF] Ku | Pars planitis in father and son[END_REF][START_REF] Tejada | Pars planitis in a family[END_REF][START_REF] Lee | Familial pars planitis[END_REF] The course of IU in children can be worsened by many sight threatening complications. 3;14-19 Nevertheless in many children and young adults IU can reach remission with maintained good visual acuity in the absence of ongoing therapy; their disease can resolve and "burn itself out". 14;19;20 Although the visual outcome in many children is favorable, approximately 19% develops unilateral legal blindness. [START_REF] De Boer | Long-term follow-up of intermediate uveitis in children[END_REF] One of the major questions in IU is whether it is possible to predict which patients are at greater risk of developing severe course of IU with visual loss before the sightthreatening complications actually occur. It has been reported that children with IU might have a more severe disease and worse outcome than patients with onset at older age. [START_REF] Aaberg | The enigma of pars planitis[END_REF][START_REF] Guest | Pars planitis: a comparison of childhood onset and adult onset disease[END_REF][START_REF] Hogan | Peripheral retinitis and chronic cyclitis in children[END_REF] , but these findings could not be confirmed in other studies. 17;19

Patients and methods

Medical charts of all pediatric patients with idiopathic IU examined at the department of Ophthalmology, University Medical Center, Utrecht, The Netherlands, were reviewed.

The patients were collected from the data-base of our institution between 1989 and 2009. All 35 patients with the onset of their IU before the age of 16 years and a minimum follow-up of 1 year were included into this study. Diagnosis of IU and PP was made in agreement with the criteria of the SUN workgroup. [START_REF] Jabs | Standardization of uveitis nomenclature for reporting clinical data. Results of the First International Workshop[END_REF] Combination with anterior segment inflammation was allowed.

All children were evaluated by a pediatrician and/or pediatric immunologist before the idiopathic nature of IU was concluded.

Numerous data from the medical charts of the patients were recorded including demographics, date of diagnosis of IU, laterality of ocular disease, extent of diagnostic procedures and duration of follow-up were noted. Presence and date of onset of all ocular signs and complications were noted. Due to difficulties in objective measurement of cataract formation, we have used cataract surgery as a measure for cataract.

Secondary glaucoma was defined as the presence of pathologic cupping of the optic disc and/or a glaucomatous visual field defect, in combination with intraocular pressure (IOP) higher than 21 mmHg or both. [START_REF] Sijssens | Ocular hypertension and secondary glaucoma in children with uveitis[END_REF] Cupping of the optic disc was defined as pathologic when there was a C/D difference of greater than 0.2 between bellow eyes or when an increase of cupping was noted compared to the previous visit. CME was defined as the presence of macular thickening with cyst formation seen by funduscopy and/or by optical coherence tomography (OCT). Vitreous involvement was classified into 2 categories: presence of cells in vitreous or presence of dense vitreous opacities (in addition to cells). Vitreous opacities were quantified according to the recommendations of SUN working group by slitlamp examination for the anterior vitreous and funduscopy of the posterior vitreous. [START_REF] Jabs | Standardization of uveitis nomenclature for reporting clinical data. Results of the First International Workshop[END_REF] Vasculitis was scored if vascular sheating/ leakage were observed on fundoscopy and/or angiography. Duration of uveitis was calculated from the date of diagnosis of IU. All surgical procedures and pharmacologic treatment options (with initiation and withdrawal date) were registered as well. Remission of IU was defined as absence of inflammatory activity in both anterior chamber and vitreous and no signs of snowballs and/or snowbanking for at least one year in the absence of antiinflammatory therapy. There were no children who were lost to follow-up within the first year after the discontinuation of the therapy.

In addition to examination by an ophthalmologist specialized in childhood uveitis, all children were examined by an orthoptist at initial visit and regularly evaluated during the follow-up. None of the study population had causes of amblyopia other than IU.

Standard frequency of follow-up examinations was approximately once every 2-3 months.

Patients were subdivided according to age at onset of IU into a younger-onset (IU onset ≤7 years of age) and older-onset group (IU onset >7 years of age). This approach was chosen to allow bivariate comparisons and survival analysis between the groups.

Best corrected visual acuities (BCVA's) were scored at onset of IU and during the follow-up. Snellen BCVA's were converted into the logarithm of the minimum angle of resolution scale (logMAR) for statistical analysis and converted back to Snellen BCVA's for data presentation. [START_REF] Holladay | Visual acuity measurements[END_REF] Legal blindness was defined as BCVA of the affected eye of 20/200 or worse. [START_REF] Tielsch | Blindness and visual impairment in an American urban population. The Baltimore Eye Survey[END_REF] In the analysis of visual outcome only the eyes with vision loss due to uveitis or related complications were included. Visual acuities were analyzed by eye (with an adjustment for paired eyes), further data were analyzed by patient. [START_REF] Katz | Appropriate statistical methods to account for similarities in binary outcomes between fellow eyes[END_REF] Statistical analysis of the data was performed with SPSS 15.0.1. The Pearson's Chi 2 or Fisher's exact test were used for univariate analysis of categorical variables. Means were compared with Student's t-test for normally and Mann-Whitney's U-test for abnormally distributed variables. Kaplan-Meier survival analysis with a log-rank test was used to analyze survival and to compare groups. Cox proportional hazard regression was applied for multivariate analysis of variables significant in univariate analysis. BCVA's were analyzed using linear regression with GEE as adjustment for paired eyes. 26 P-values of ≤0.05 were considered statistically significant. For presentation we used means if data were distributed normally (by Kolmogorov-Smirnov test) and medians if it was not. All significances are 2-tailed and nominal.

Results

General characteristics of the study group and subgroups according to the age of IU onset are presented in Table 1. Thirty-one patients (89%) fulfilled the criteria of PP 2 , four remaining patients did not show the presence of snowballs and/or snowbanking during their follow-up. In 10/35 (29%) patients, diagnosis of IU was made during routine ophthalmologic examination without ocular complaints and the others were referred to an ophthalmologist because of ocular complaint of floaters or decreased visual acuity.

Ocular signs and complications at onset of IU (initial presentation).

The ocular findings at the initial presentation for the whole group, and in the groups divided according to the age of onset are presented in Table 2. It is notable that moderate to dense vitreous opacities 2 were significantly more frequently present in children with onset ≤7 years (88% vs. 42%; p=0.01). At the initial orthoptic examination, amblyopia due to IU was present in 4 patients (11%) and in one of them the diagnosis of IU was made during treatment for amblyopia (Table 2).

Ocular signs and complications in the course of follow-up and mean time until their development are listed in Table 3. The most frequently observed complications were papillitis (60%) and CME (57%) which were bilateral in the majority of cases (Table 3).

Children from the onset group ≤7 years developed severe sight threatening complications such as secondary glaucoma and vitreous hemorrhage (p=0.04; p=0.01 respectively; Fig. 1 and2) earlier and more frequently. Additionally, mean age of onset in children in whom the course of IU was complicated by cataract surgery, secondary glaucoma and vitreous hemorrhage was significantly lower compared with children without this complication (respectively, 5.9 versus 8.7; 5.0 versus 8.4 and 5.6 versus 8.5 years; all p=0.03).

Development of CME in the course of follow-up was univariately associated with the presence of papillitis and snowbanking at onset (both p<0.01). In a multivariate analysis, adjusted for age of uveitis onset and gender, the presence at onset of both papillitis (adjusted hazard ratio [HR]=3.4; 95% CI 1.1-9.6); p=0.02) and snowbanking (adjusted HR=3.3; 95% CI 1.2-9.7; p=0.03) were independently associated with later development of CME. Other complications at onset were not predictive for future complications.

Treatment. All but one patient (97%) in our series needed some form of antiinflammatory therapy during follow-up (Table 1). Topical steroids (86%) and periocular steroid injections were used most frequently in all patients (resp. 86% and 64%).

Systemic medication in the form of systemic corticosteroids and immunosuppressives was administered in respectively 34% and 14% of the patients. Table 1 demonstrates that patients with young onset seemed to be treated more intensively in our series.

However, this difference reached the level of statistical significance only for the use of topical steroids (100% vs. 68%; p=0.02).

Over time, anti-inflammatory therapy (topical and systemic) could be stopped completely in 24/35 patients (69%). Fifteen of these 24 patients (63%) reached remission. Mean time to discontinuation of therapy was 2.0 years (range 0.1-6.9 years). Mean age of discontinuation of therapy was 11.0 years (±3.1 SD; range 6.3-18.2 years).

Continuation of any kind of therapy (topical, systemic or periocular) was significantly longer and more frequently needed in children with younger age of onset compared with the older onset group (p=0.01; Fig. 3).

Pars plana vitrectomy (PPV) was performed in 3 (9%) children in cases of vitreo-retinal traction, all of them had onset of IU before 7 years of age.

Remission. In total 15 patients (43%) of the whole group (n=35) reached remission. The mean time before reaching the remission was 5.1 years (±3.2 SD; range 1.1-11.8). Two of these 15 patients (13%) who reached remission had a flare-up of uveitis after approximately 2 and 2.5 years of remission, respectively. The other 13/15 patients (87%) stayed in remission till the end of follow-up (mean duration of follow-up in remission 3.9 years; ±2.9 SD; range 1.1-9.8 years). Mean age of reaching the remission was 13.8 years (±3.1 SD; range 8.1-19.4).

In the group with onset of IU ≤7 years remission was reached in 4/16 patients (25%) versus 11/19 (58%) patients with older onset of IU (p=0.050).

Visual outcome. BCVA's in affected eyes according to the age of onset of IU at different points of FU are presented in Table 4. Children with onset of IU ≤7 years had significantly worse BCVA's till up to 3 years of follow-up compared with the older onset group (all p≤0.04; Table 4).

Unilateral legal blindness occurred in 7/35 patients (20%). Two of them (2/7; 29%) already had one legally blind eye at the initial visit to an ophthalmologist for IU, with no improvement after treatment. Blindness was univariately associated with diagnosis made at routine ophthalmologic examination without ocular complaints (p=0.04). There were no statistical differences in the frequency of blindness between younger (4/16; 25%) and older onset groups (3/19; 16%).

The causes of unilateral blindness included CME/maculopathy (n=3), retinal detachment after vitrectomy due to epiretinal membranes (n=2), recurrent vitreous hemorrhage (n=1) and secondary glaucoma (n=1). The child with recurrent vitreous hemorrhage was not followed in our clinic anymore since these events. The data about his later visual function was unavailable. In 2/7 (29%) unilaterally blind patients, amblyopia due to IU played a role in the definitive loss of vision.

Discussion

The current study shows negative effect of young age at onset of IU on the clinical course and visual outcome up to 3 years follow-up.

To our knowledge this is the first study which shows the effect of young age of onset within a paediatric population with IU. A previous study on paediatric IU from our center had a different main goal and methodological approach with a slightly different patient selection and reported no differences at presentation between different age groups. [START_REF] De Boer | Long-term follow-up of intermediate uveitis in children[END_REF] In the current study we estimated the differences in the course of disease and visual outcome between younger and older onset of IU in childhood. It should be mentioned that in accordance with our earlier results, no differences in complications at presentation between different age groups were found.

Our study revealed that children with earlier onset of IU are at increased risk of vitreous haemorrhage, secondary glaucoma and cataract surgery. Vitreous haemorrhage in IU is usually caused by retinal neovascularisation. [START_REF] Lauer | Vitreous hemorrhage is a common complication of pediatric pars planitis[END_REF] The fact that vitreous haemorrhage was more common in children compared to adults was reported previously by others. [START_REF] Lauer | Vitreous hemorrhage is a common complication of pediatric pars planitis[END_REF] It is noteworthy that even within a paediatric population we could find higher prevalence of vitreous haemorrhage in children with onset ≤7 years of age. In our series, vitreous haemorrhage took place only in children with onset before the age of 7. The difference in prevalence of this complication in children with younger and older onset could be explained by a more severe form of disease with increased incidence of retinal neovascularisation or by late identification and treatment of neovascularisation in young children. Unfortunately, our data does not make it possible to investigate these theories, as in our series neovascularisation was observed only in two patients with older onset and none of them developed vitreous haemorrhage during their follow-up. This example illustrates the difficulty in identification of neovascularisation in young children as angiography and pars plana indentation are being performed mostly in older patients.

Worse visual outcome of children with younger onset of IU could be theoretically explained by higher frequency of the vision-threatening complications. Noteworthy is that children with younger onset had lower visual acuity at presentation. The speculative explanation for this difference could lie in the difficulty and delay of diagnosis of IU in young children, who mostly do not complain about decrease in vision. Vitreous opacities, which were more frequent in young children at presentation, could indicate more severe disease at presentation and lead to worse vision at presentation. Additional potential risk and danger in this group of young children is the development of amblyopia, which, as we show in our series, can occur in a sizable proportion of patients and contribute to their poor visual outcome.

Although almost all patients in our series needed some form of treatment during their disease course, the therapy could be completely stopped in almost 70% after the mean interval of 2 years. In accordance with earlier results from our center, we show the therapy-free remission of over 1 year in more than a third of patients. [START_REF] De Boer | Long-term follow-up of intermediate uveitis in children[END_REF] An interesting finding of this study is that children with younger onset of IU needed more prolonged and intensive treatment, which could be discontinued less frequently and their disease burned out significantly less often. Mean age when remission was reached was 13.8 years and no one of the patients reached remission before the age of 8 years. These facts could indirectly indicate the severity of disease in younger children. As we discussed above, children with young age at onset were at increased risk of complications despite the fact that and they received a more intensive treatment. On the one hand the given treatment might have been insufficient for their severe inflammation but on the other hand secondary glaucoma and cataract can also be associated with administration of corticosteroids. It is not possible to make a reliable distinction between a more severe inflammation and more intensive use of corticosteroids as a cause for these complications in our data set. More frequent use of topical steroids in children with young onset could be related to a more frequent involvement of the anterior chamber.

However, this difference did not reach the level of statistical significance in our series at onset Presence of anterior segment inflammation later in the follow-up was not scored.

Papillitis and CME were the most frequent complications of IU, which is in accordance to other reports. 14;18;23;28 We confirmed the clinical suspicion about the association of these two complications [START_REF] De Boer | Long-term follow-up of intermediate uveitis in children[END_REF] by showing that the presence of papillitis at onset is an independent prognostic factor for development of CME later. We consider this a clinically relevant finding since CME is known as a major cause of poor visual prognosis in these patients 14;20;28 and is being used as a treatment indication. 14;28 Development of CME was also independently associated with the presence of snowbanking at initial presentation. The knowledge of early risk factors for CME might help in the identification and the evaluation of patients who are at risk of this sight-threatening complication.

Several limitations should be mentioned for this study. First, the retrospective study design makes it impossible to account for all possible confounders and complicates the interpretation of cause and consequence. Cataract surgery was chosen as a measurement for cataract. Although this is not a completely objective measurement, but in a retrospective study it solves problem of frequently incomplete documentation of cataract development and observations by different ophthalmologists during the followup. As the study was performed in a tertiary centre specialized in uveitis, the milder and uncomplicated cases might be lost to follow-up earlier, so the cumulative complication rates can be overestimated due to a selection bias. Relatively limited number of patients with small number of events increases the likelihood of type II error (inability to confirm significant difference when it actually exists). The chosen cut point of 7 years is arbitrary. It was chosen due to the fact that it is being generally assumed that children before this age have more chance to develop amblyopia. The choice of this cut point resulted in two statistically comparable groups. Furthermore, the higher complication rate in young children is confirmed by the analysis of age as a continuous variable in children with and without complications.

In conclusion, our study emphasizes increased risk of a complicated and more prolonged course of disease and worse visual outcomes in children with younger onset of IU. We show that the presence of papillitis and snowbanking at onset of IU is associated with later development of CME. Our results could help a clinician to identify children with IU who carry higher risk of complications and worse visual outcome. We would recommend considering more intensive monitoring and earlier threshold for systemic treatment in those children with risk factors as early onset, disc oedema and/or snowbanking at initial presentation.

Financial disclosures:

The authors declare no financial conflict of interest. Funding entities had no role in the conduction or presentation of this study.

Contribution of authors:

design of the study (V.K.A., J.H.B., A.R.), data collection (V.K.A., H.A.T.C), data management and analysis (V.K.A.), data interpretation (V.K.A., P.D., J.H.B., A.R.), preparation of initial manuscript (V.K.A.), critical revision of initial manuscript (J.H.B., A.R.).

Other acknowledgments:

The IRB at Utrecht University has declared that this type of research does not require IRB approval. • Median BCVA a 0.8 0.4 0.9 0.03

yrs of follow-up

Number of eyes in analysis 49 29 20

• Median BCVA a 0.7 0.6 0.9 0.01

yrs of follow-up

Number of eyes in analysis 27 16 11

• Median BCVA a 0.8 0.6 0.8 0.09 a For the analysis of BCVA's all decimal Snellen BCVA's were f transformed to their logMAR equivalents and afterwards back to decimal VA's, which are presented. b p-values computed by linear regession with generalized estimating equations (GEE) as adjustment for paired eyes c excluding eyes with missing data about vision at initial presentation.

Figure 1 .

 1 Figure 1. Survival plot for secondary glaucoma in patients with onset of intermediate

Figure 2 .

 2 Figure 2. Survival plot for vitreous hemorrhage in patients with onset of intermediate

Figure 3 .

 3 Figure 3. Survival plot for discontinuation of the therapy in patients with onset of

Table 3 . Frequency and time of development of complications, during the follow-up of children with intermediate uveitis (IU) according to the age of onset.

 3

	Complications and ocular findings during the course of IU	Total (N;%) N = 35	Bilateral (%)	Age of uveitis onset 0-7 years (N;%) 7-16 years (N;%)	Mean time to development (years ± SD)
				N=16	N=19
	Papillitis (N;%)	21 (60)	90	10 (63)	11 (58)	0.7 (± 1.1)
	CME (N;%)	20 (57)	70	10 (63)	10 (53)	0.6 (± 1.3)
	Vasculitis (N;%)	16 (46)	63	8 (50)	8 (44)	1.8 (± 1.8)
	Cataract surgery (N;%)	9 (26)	22	7 (44)	2 (11)	2.6 (± 2.1)
	Secondary glaucoma (N;%)	4 (11)	NA	4 (25)	ND	2.9 (± 2.1)
	Vitreous haemorrhage (N;%)	6 (17)	17	6 (38)	ND	3.2 (± 2.9)
	Bandkeratopathy (N;%)	6 (17)	17	5 (31)	1 (5)	3.2 (± 2.4)
	Amblyopia (N;%)	6 (17)	NA	6 (38)	ND	0.5 (± 1.2)
	Posterior synechiae (N;%)	5 (14)	20	3 (19)	2 (11)	0.7 (±1.1)
	Epiretinal membranes (N;%)	5 (14)	40	4 (25)	1 (5)	1.9 (± 2.1)
	Retinal detachment (N;%)	3 (9)	NA	2 (13)	1 (5)	1.2 (± 1.4)
	Retinal neovascularisation (N;%)	2 (6)	NA	NA	2 (11)	0.6 (± 0.6)
	ND = not detected

Table 4 . Best corrected visual acuities (BCVA's) according to the age of onset in eyes affected with intermediate uveitis (IU).

 4

			Mean of BCVA a
	Visual acuity	Total			p-value b
			0 -7 years 7 -16 years
	At onset
	Number of eyes in analysis c	63	29	34
	• Median BCVA a	0.4	0.3	0.6	0.04
	1 yr of follow-up
	Number of eyes in analysis	61	26	35

Acknowledgments/ disclosure

"The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in BJO editions and any other BMJPGL products to exploit all subsidiary rights, as set out in our licence(http://group.bmj.com/products/journals/instructions-for-authors/licence-forms/)."

Funding:

The study was financially supported by following Dutch non-government scientific funds: Dr. F.P. Fisher Stichting, Stichting Nederlands Oogheelkundig Onderzoek and ODAS-Stichting.

Competing interest: none to declare

Tables Table 1. General characteristics and therapy of patients with idiopathic intermediate uveitis according to age of uveitis onset.

Characteristics

Total N=35

Age of uveitis onset p-value 0-7 years N = 16 (%)