Mahmoud Shahrokhi
email: m.shahrokhi@uok.ac.ir

Zahra Sobhani
email: zahra.sobhani7@gmail.com

Alain Bernard
email: alain.bernard@ec-nantes.fr

Supplier selection by using the goal programming approach

Keywords: Reliability block diagram(RBD), goal programming, concurrent engineering, Markov chain

Supplier selection problems have been considered widely in literature; however, considering the availability and reliability of the products provided by suppliers has been investigated less. In this regard, the presented work addresses a supplier selection problem in which the reliability of the parts is one of their main factors for supplier selection. This paper develops a multiobjective mathematical goal programming model to allocate the system's components orders to the suppliers. The model minimizes the system construction costs and maximizes the probability of working the system in nominal and half capacity. The similarity of the ordered components affects their delivery lead times and prices. The model determines the optimal solution for an industrial system composed of 4 parts. The proposed approach includes using the reliability block diagram to develop the Markov chain model. A multiobjective binary nonlinear mathematical program uses the Markov model to select the optimal components suppliers. Solving the model by goal programming approach provides the possibility of reflecting the decision-maker opinion relative to the construction cost's importance compared to system availability.

Introduction

This paper presents a multiobjective approach to determine the components suppliers for making the feedwater system (FWS) of heat-recovery steam-generator boilers used in combined cycle power plants. A nonlinear binary goal programming model uses the Markov chain results to optimizes total costs, including components price, system construction delays penalty, and the system's availability.

Many kinds of research applied optimization approaches such as integer programming, mixed integer programming, nonlinear programming, and heuristics to deal with serial-parallel and multistate systems' availability and supplier selection in supply chains. Levitin et al. determined the optimal versions of components and redundancy of different subsystems in multistate series-parallel systems [START_REF] Levitin | Joint Redundancy and Maintenance Optimization for Multistate Series-Parallel systems[END_REF]. Rui and et al. improved design configuration by developing a reliability optimization algorithm [START_REF] Rui | Reliability of multistate systems with a performance sharing group of limited size[END_REF]. Yi and et al. view a reliability optimization problem by selecting components reliability among different alternative levels when the system's overall performance is a function of each part's failure rate [START_REF] Yi | Reliability analysis of multistate systems subject to failure mechanism dependence based on a combination method[END_REF]. Montoro-Cazorla et al. studied a system subjected to shocks governed by a Poisson process, and the internal failures and the inspection times exponentially distributed [START_REF] Montoro-Cazorla | Constructing a Markov process for modelling a reliability system under multiple failures and replacements[END_REF]. Ge et al. developed an optimization model to determine critical components' reliability in a serial system [START_REF] Ge | Reliability optimization for series systems under uncertain component failure rates in the design phase[END_REF]. Carpitella et al. developed a mathematical model for calculating a k-out-of-n system's stationary availability using the Markov chain model [START_REF] Carpitella | k-out-of-n systems: An exact formula for the stationary availability and multiobjective configuration design based on mathematical programming and TOPSIS[END_REF]. Chambari et al. proposed a bi-objective simulation-based and a customized NSGA-II optimization algorithm for a redundancy allocation problem [START_REF] Chambari | A bi-objective simulation-based optimization algorithm for redundancy allocation problem in series-parallel systems[END_REF]. Es-Sadqi et al. attempted to find the optimal system configuration that maximizes the availability and minimizes the investment cost. They evaluated the genetic algorithm and the constraint programming performance and proposed a new optimization method based on forwarding checking as a solver [START_REF] Es-Sadqi | Some Efficient Algorithms to Deal With Redundancy Allocation Problems[END_REF]. Sawik investigated a multi-period supplier selection problem in a supply chain with disruption and delay risks and then proposed a dynamic portfolio approach. This approach helps decision-makers decide on the suppliers of finished products, but it does not discuss the availability of the ordered parts [START_REF] Sawik | Selection of a dynamic supply portfolio under delay and disruption risks[END_REF]. Chen et al. developed a model to consider supplier selection and project scheduling simultaneously. They studied multiple concurrent projects that were independent operationally but with similar suppliers, when these projects' activities began as their needed resources became available. Then, this model was solved using a mathematical programming-based heuristic [START_REF] Chen | Coordinating supplier selection and project scheduling in resource-constrained construction supply chains[END_REF]. Bodaghi et al. introduced a weighted fuzzy multiobjective model to address supplier selection, customer order scheduling, and order quantity allocation in supply chains. They considered the reliability of on-time delivery of customer orders and evaluated the flexibility of suppliers [START_REF] Bodaghi | An integrated weighted fuzzy multiobjective model for supplier selection and order scheduling in a supply chain[END_REF]. Yoon et al. incorporated supplier selection with risk mitigation policies and utilized multiobjective optimization-based simulation for their model [START_REF] Yoon | Models for supplier selection and risk mitigation: a holistic approach[END_REF]. They showed that the use of upstream and downstream strategies simultaneously leads to better results. Cui et al. investigated sustainable supplier selection in deferent multitier supply chain structures and combined fuzzy set theory, stepwise weight assessment ratio analysis, plus a Bayesian network to propose their model [START_REF] Cui | Modelling flexible decisions about sustainable supplier selection in multitier sustainable supply chain management[END_REF].

The previous researches did not consider the life cycle cost, reliability, and availability of the system simultaneously. This paper attempts to consider more real wolds parameters by developing a multiobjective mathematical goal programming model for assigning system component orders to the suppliers. This model minimizes system construction costs and maximizes the likelihood of the system operating at nominal and half-load capacity, while the similarity of the ordered components leads to change their delivery time and price. Using the proposed approach to select the components supplier of a typical industrial system illustrates the model application for an industrial system. The following sections explain this model and solve it for a numerical example and discuss the results.

2

The proposed model

The discussed problem concerns selecting the suppliers for critical components of FWS (Figure 1).

Fig 1. Studied series-parallel system

Component B, C, and D are feedwater pumps. Each of them provides 50% of the nominal system capacity, and component A is the instrumentation device that controls the overall system parameters. Several suppliers offer different qualities (i.e., failure and repair rates), prices, and delivery lead times. The availability of the system depends on the reliability and reparability of its components. Also, suppliers offer incremental quantity discounts; however, large orders result in higher delivery lead time. During the system exploitation phase, the system may be in one of three states, according to the failure of its components:

(l1-2, µ2-1) S2: AbCD (l2-6, µ6-2) S11: Abcd (l6-11, µ11-6) S6: AbcD (l2-5, µ5-2) S5: AbCd (l5-11, µ11-5) S4: ABcD (l1-4, µ4-1) (l4-7, µ7-4) (l7-11, µ11 -7)
(l4-6, µ6-4)

S9: abCD (l2-9, µ9-2)

S3: ABCd (l3-15, µ15-3) (l3-5, µ5 -3)
S8: abCd (l5-8, µ8-5)

S15: aBcd (l7-15, µ15-7)

S14: aBcD (l4-14, µ14-4)

S13: aBCD S12: aBCd (l3-12, µ12-3) (l1-13, µ13 -1)
S10: abcD (l2-9, µ9-2)

S7: ABcd
Subjected to:

𝐴 1 = ∑ 𝑦 𝐵𝑗 𝑦 𝐶𝑗 𝑦 𝐷𝑗 (3𝐶 𝐵𝑗 " ) 3 𝑗=1 + ∑ (𝑦 𝐵𝑗 𝑦 𝐶𝑗 (1 -𝑦 𝐷𝑗 ) + 𝑦 𝐵𝑗 𝑦 𝐷𝑗 (1 -𝑦 𝐶𝑗 ) 3 𝑗=1 + 𝑦 𝐶𝑗 𝑦 𝐷𝑗 (1 -𝑦 𝐵𝑗 )) × (2𝐶 𝐵𝑗 ′ ) + ∑ (𝑦 𝐵𝑗 (1 -𝑦 𝐶𝑗 )(1 -𝑦 𝐷𝑗 ) 3 𝑗=1 + 𝑦 𝐶𝑗 (1 -𝑦 𝐵𝑗 )(1 -𝑦 𝐷𝑗 ) + 𝑦 𝐷𝑗 (1 -𝑦 𝐵𝑗 )(1 -𝑦 𝐶𝑗 )) × 𝐶 𝐵𝑗 + ∑ 𝑦 𝐴𝑗 𝐶 𝐴𝑗 3 𝑗=1 (2
+ (∑ (𝑦 𝐵𝑗 (1 -𝑦 𝐶𝑗 )(1 -𝑦 𝐷𝑗 ) 3 𝑗=1 + 𝑦 𝐶𝑗 (1 -𝑦 𝐵𝑗 )(1 -𝑦 𝐷𝑗 ) + 𝑦 𝐷𝑗 (1 -𝑦 𝐵𝑗 )(1 -𝑦 𝐶𝑗 )) × 𝑀𝑎𝑥( 𝐿 𝐵𝑗 , 𝐿 𝐵𝑗 ′ )) (8)
𝑇 𝐶 = max ( 𝑇 𝐵 , 𝑇 𝐴 ) + ∑ 𝐹 𝐵𝑘 𝑘∈𝑘 𝐵 (9)
𝐴 2 = max (0, 𝑇 𝑐 -𝑇)𝐶 𝐷 (10)

𝐴 1 + 𝐴 2 -𝑑 1 + = 𝑏 1 (11) 𝐴 1 ≤ 𝐵 (12) µ i = ∑ 𝑌 𝑖𝑗 µ 𝑖𝑗 3 𝑗=1 ∀𝑖 ( 13
)
λ i = ∑ 𝑌 𝑖𝑗 λ 𝑖𝑗 3 𝑗=1 ∀𝑖 (14)
∑ 𝑦 𝑖𝑗 = 1 (32)

𝑅 𝑒 = 1 -𝑃 0 (33) 𝑅 𝑒 ≥ 𝑅 (34) 𝑃 100 = 𝑆 1 + 𝑆 2 + 𝑆 3 + 𝑆 4 (35)
The objective function (1) minimizes a weighted summation of deviations from the goals. Equation (2) calculates total purchasing costs according to their suppliers' components' similarity and the offered price. Equations ( 3) and ( 4) calculate the SD and HC proportion time, respectively. Equations ( 5) and ( 6) calculate deviations from the second and third goals. Equation ( 7) is the assembly time of component A by adding its delivery lead time and times of its assembly processes. According to their suppliers and order sizes, equation ( 8) calculates the delivery lead time of all B, C, and D. Supposing that larger orders increase delivery lead time, this equation can be written, simplifies this equation by removing the max operators. Constraint (9) calculates the completion time of the system construction project by adding the possible earliest time of starting assembly processes of components B, C, and D, with the time of these processes. The max operator indicates these processes can begin after delivering B, C, and D and completing A's assembly. Equation [START_REF] Chen | Coordinating supplier selection and project scheduling in resource-constrained construction supply chains[END_REF] calculates the construction delay penalty by multiplication the daily delay penalty by the difference between the project's end time and its deadline. Equation [START_REF] Bodaghi | An integrated weighted fuzzy multiobjective model for supplier selection and order scheduling in a supply chain[END_REF] calculates the total construction cost by adding the component purchasing price and construction delay penalty. The model ignores the assemblies' processes' costs because they are constant independent from decision variables. Constraint (12) limits the maximum components' purchasing cost to a predefined level. Equations ( 13) and (14) determine the components' failure and repair rates, respectively, considering their suppliers. Equation (15) indicates that the supplier for each element is unique. Constraint (16) determines the domain of binary variables. Constraints (17) to (32) are Markova processes equilibrium equations, written according to figure [START_REF] Rui | Reliability of multistate systems with a performance sharing group of limited size[END_REF], and ensure that the sum of the input streams to each state, in the long term, is equal to the sum of the output streams and the sum of all times ratio is 1. Constraint (33) calculates the entire system's reliability as the proportion of times it is not shutting down, and equation (34) ensures the requested minimum level of system reliability. Constraint (35) calculates the ratio of times that the system will work at nominal capacity states. It facilitates the analyses of the results.

Numerical results

Table [START_REF] Levitin | Joint Redundancy and Maintenance Optimization for Multistate Series-Parallel systems[END_REF] presents the problem parameter values, and table [START_REF] Rui | Reliability of multistate systems with a performance sharing group of limited size[END_REF] shows the optimal solution. The optimal reliability is 0.99, 0.9, 0.9, and 0.95 for A, B, C, and D, respectively. Component A does not have any redundant components, making component A more critical. The entire system's reliability is about 0.85 (i.e., the system works at NC and HC modes, 55% and 29.7% of the time, respectively) and 0.15% of the times the system is at SD state. Deviation from the second and third goals is very high, resulting from the relative importance (i.e., higher weighted factor) of the first goal. Components' purchase price and the project delay penalty are so high that ordering higher reliability components is not justifiable despite a sufficient purchase budget.

Discussion

This paper uses goal programming as a multiobjective decision-making approach to optimize the systems' design configuration by considering the system life cycle's parameters. The model objective is achieving a sufficiently low construction cost and increasing the system's availability during its exploitation. These objectives are defined based on the supply chain's criteria and alternatives.

Considering the components' constant failure and repair rates justifies using the exponential distribution function as a memoryless probability distribution. This assumption is required to use the Markov model. Otherwise, system simulation models may create the same results. However, integrating the simulation model in the goal program model will remain a modeling challenge. Using the more appropriate probability distributions (e.g., the Normal distribution function) improves the model's result. The goals' weighting factors express the decision-maker opinion relative to the construction cost's importance compared to system availability. Reducing construction costs is a shortterm objective, leads to decreasing the system availability and customer satisfaction.

Varying the goals' weighting factors changes the priorities between the system construction cost and system availability. The model applicability can be improved, by considering uncertainty in the model parameters, such as component repair and failure rates and system costs. Besides, the goal weighting factors may express as fuzzy numbers or linguistic variables. Future research may also model the effect of using similar components on their repair rates and system availability.

Conclusions

  working at nominal capacity (NC), half capacity (HC), and shutdown (SD). NC occurs when A and at least two out of B, C, and D are working. HC occurs when A and only one out of B, C, and D are working. SD results from failure of A or all B, C, and D. Figure[START_REF] Rui | Reliability of multistate systems with a performance sharing group of limited size[END_REF] illustrates the Markov model of all possible states for this system. In this figure, oval, trapezoid, and rectangular forms represent NC, HC, and SD. Operation of components B, C, and D are identical, and ordering one of them to supplier j, delivery lead time, and the price will be 𝐿 𝐵𝑗 and 𝐶 𝐵𝑗 , respectively. By ordering two components among these components to supplier j, the unit price and delivery lead time of each of them will be 𝐶 𝐵𝑗 ′ and 𝐿 𝐵𝑗 ′ , respectively. Finally, by ordering all these three components to supplier j, price and delivery time values will be 𝐶 𝐵𝑗 " and 𝐿 𝐵𝑗 " , respectively. Assembling B, C, D should begin after A, and simultaneous assembly of B, C, D does not change their assembly times. Times and costs of assembling processes are deterministic and known and independent from the quality of the components. Deterministic weighting factors express the goals' importance.

Fig 2 .

 2 Fig 2. The system's Markov model Diagram

Table 1 .

 1 The model parameters values

	Para. Value Para. Value Para. Value			Supplier (j)
	B 𝑅 𝑖1	1200 0.9	n 𝑅 𝑖2 0.95 4		𝑅 𝑅 𝑖3	0.8 0.99	Relia-bility	1 0.9 0.95 0.99 2 3
	µ 𝑖1 λ 𝑖1 𝑏 1 𝑤 1 𝑇	0.05 0.05 6100 0.01 75	µ 𝑖2 0.07 λ 𝑖2 0.03 𝑏 2 0.05 𝑤 2 200 𝐶 𝐷 300		µ 𝑖3 λ 𝑖3 𝑏 3 𝑤 3	0.1 0.01 0.05 40	C Aj C Bj ′ C Bj " C Bj L Aj	200 220 240 300 340 380 250 280 320 200 240 280 5 7 17
								L Bj	6	19	31
		Assembly process (k)	1	2	3	4 5	L 𝐵j ′ L Bj "	8 12	24 30	37 42
		𝐹 Ak	3	5	7	4 2
		𝐹 Bk	6 13 16 5 -

Table 2 .

 2 The model's optimal solution

	Decision variable	Value	Decision variable	Value
	𝑨 𝟏	1080	𝑅 1	0.99
	𝑨 𝟐	5100	𝑅 2	0.9
	𝑹 𝒆	0.847	𝑅 3	0.9
	𝑷 𝟓𝟎	0.297	𝑅 4	0.95
	𝑷 𝟎	0.153	𝑍	31.363
	𝒅 𝟏 +	80	𝑇 𝑐	92
	𝒅 𝟑 +	0.247	𝑑 2 +	0.103

This paper developed a nonlinear binary goal programming model to select suppliers of an industrial system's components. The model minimizes the system's life cycle cost, including the cost of purchasing price and the construction delay penalty, and maximizes the system's availability concurrently. The numerical results illustrate the effect of the order sizes on the components' prices and delivery lead times. They also show how varying the goals' weighting factors provides a trade-off between short-term (minimizing the system construction cost) and long-term objectives (maximizing the system availability).


Sonja K Fagervold
email: fagervold@obs-banyuls.fr.

Laurent Intertaglia

Nicole Batailler

Joana Bondoso

Philippe Lebaron

Saonia flava gen. nov., sp. nov., a marine bacterium of the family Flavobacteriaceae isolated from coastal seawater

A Gram-negative, aerobic, yellow pigmented, straight rod, strain MOLA117 T , was isolated from a coastal water sample from the north-western Mediterranean Sea, near Banyuls-sur-mer, France. On the basis of phylogenetic analysis of the 16S rRNA gene sequence, strain MOLA117 T was placed within the family Flavobacteriaceae, but showed less than 93% 16S rRNA gene sequence similarity to other recognized species within the family. The most closely related genera included Arenibacter, Cellulophaga, Maribacter and Zobellia. The only isoprenoid quinone was MK6 and the fatty acid iso-C17:0 3-OH was dominant, representing over 33% of the total fatty acids. The DNA G+C content was 36.9 mol%. MOLA117 T required NaCl for growth, did not exhibit gliding motility or produce flexirubin. Based upon the phenotypic and phylogenetic data, we propose that strain MOLA117 T should be considered to represent a novel genus and species, for which the name Saonia flava gen. nov., sp. nov. is proposed. The type strain of Saonia flava is MOLA117 T (=CIP 110873 T = DSM 29762 T ). The accession number of the MOLA117 T 16S rRNA gene sequence is AM990891.

The marine environment continues to be a major source of uncharacterized microorganisms with potentially interesting activities. These activities can be of academic interest but can also result in biotechnological applications. At the Oceanological Observatory in Banyuls, we aim to isolate prokaryotes that are environmentally ubiquitous in marine coastal ecosystems to study their biodiversity and function as well as to explore their use in biotechnology. Each isolated strain is added to the Banyuls Bacterial Culture Collection (WDCM911). During such an effort we isolated a Gram-negative, aerobic, yellow-pigmented bacterium, strain MOLA117 T , from a water sample taken off the coast of Banyuls-sur-Mer, France. The closest cultured relatives, based on their 16S rRNA gene sequence, were species within the genus Zobellia, and the closest described species was Zobellia uliginosa strain ATCC 14397 with 92.7 % identity (BLAST [START_REF] Altschul | Basic local alignment search tool[END_REF]). Some species within the genera Arenibacter, Cellulophaga and Maribacter also showed some similarity (close to 92%) to MOLA117 T . The highest identities to environmental sequences were 94%-93% to three 16S sequences retrieved from the gut of a wood feeding gastropod [START_REF] Zbinden | Bacterial communities associated with the wood-feeding gastropod Pectinodonta sp (Patellogastropoda, Mollusca)[END_REF]. Thus, we suggest that MOLA117 T represents a novel genus within the family Flavobacteriaceae. Our aim was to investigate the exact phylogenetic position and to phenotypically describe strain MOLA117 T . Strain MOLA117 T was isolated from a seawater sample collected in February 2004 from 3 m depth at station SOLA located in the bay of Banyuls-sur-Mer (42° 29' N 3° 08' E). The isolation and bacterial stock procedures were performed as described in [START_REF] Fagervold | Pleionea mediterranea gen. nov., sp nov., a gammaproteobacterium isolated from coastal seawater[END_REF] using modified R2A agar (BD Co., Franklin Lakes, NJ) with 75% filtered Mediterranean seawater. Subsequent growth experiments were realized using Marine Agar (MA, BD Co.) or Marine Broth (MB, BD Co.) in the dark at 25°C. Cellular morphology and size were determined using liquid culture incubated for 4 days, which corresponds to end of log phase/beginning of stationary phase (data not shown), and a Model H-7500 transmission electron microscope (Hitachi, Tokyo, Japan) as previously described [START_REF] Raguénès | Vibrio diabolicus sp. nov., a New Polysaccharide-Secreting Organism Isolated from a Deep-Sea Hydrothermal Vent Polychaete Annelid, Alvinella pompejana[END_REF], see figure S1.

Motility was tested using the hanging drop technique combined with light microscopy (model AX70 microscope, Olympus, Tokyo, Japan) with liquid cultures incubated for 4 days. Most of the tests for phenotypic characterization were performed as previously described [START_REF] Fagervold | Pleionea mediterranea gen. nov., sp nov., a gammaproteobacterium isolated from coastal seawater[END_REF]. Briefly, this included Gram staining using the Ryu KOH reaction [START_REF] Powers | Efficacy of the RYO nonstaining KOH technique for rapidly determining gram reactions of food-borne and waterborne bacteria and yeasts[END_REF], determination of oxidase activity using the oxydase reagent kit (bioMérieux, Marcy l'Etoile, France) and testing for catalase activity by determination of gas production upon exposure to a 3 % hydrogen peroxide solution. Formation of H 2 S was tested with Hydrogen Sulfide Test strips (Sigma Aldrich, St Louis, MO) with cultures grown on MB for 3 days. The requirement and tolerance of MOLA117 T to NaCl was determined at 25 °C for 6 days on MB with NaCl concentrations ranging from 0-15 %, or more precisely 0, 0.5, 1, 2, 3, 4, 5, 6, 7, 10, 13 and 15 %. To verify the optimal and tolerated pH levels for growth, MOLA117 T was incubated in MB at 25 °C for 6 days amended with different buffers to stabilize the pH as follows: pH 4, 5 and 6 (MES), pH 7 and 8 (HEPES) and pH 9 and 10 (AMPSO). Growth temperatures was determined on MA after 4 days of growth at temperatures ranging from 4 to 44 °C, or more accurately 4, 10, 15, 20, 25, 30, 37 and 44°C. Antibiotic resistance was determined using antibioticimpregnated paper discs on MA with a lawn of MOLA117 T that was incubated at 25°C for 4 days. Production of flexirubin was evaluated by flooding a 4 day old plate with 20% (w/v) KOH (positive for flexirubin if color change from yellow to red, purple or brown).

Hydrolysis of starch, aesculin and Tween 80 was tested according to [START_REF] Lányi | 1 Classical and Rapid Identification Methods for Medically Important Bacteria[END_REF] [START_REF] Lányi | 1 Classical and Rapid Identification Methods for Medically Important Bacteria[END_REF].

Growth on different single carbon sources was verified with media supplemented with 1 % of each carbon source [START_REF] Bruns | Muricauda ruestringensis gen. nov., sp. nov., a facultatively anaerobic, appendaged bacterium from German North Sea intertidal sediment[END_REF]. The carbon sources tested included acetate, arabinose, cellobiose, citrate, dextran, galactose, glucose, glutamic acid, glycerol, formate, fumarate, lactate, lactose, maltose, pyruvate, succinate, sucrose, sorbitol, trehalose and xylose. To determine whether strain MOLA117 T was able to degrade cellulose we utilized both filter paper dissolved in agar and filter paper in carbohydrate free broth [START_REF] Smibert | Manuals of methods for general bacteriology[END_REF], as well as CMcellulose [START_REF] Barbeyron | Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. nov[END_REF].

Additional carbohydrate metabolism was tested using the API 20NE and API 50CH test systems (bioMérieux). Enzymatic activities were investigated using the API ZYM system (bioMérieux). For these tests, the medium utilized had the following composition: 25 g/L NaCl, 8g/L MgCl2 and 0.5g/L KCl and the cultures were incubated at 30°C for a week in the dark [START_REF] Barbeyron | Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. nov[END_REF].

Biomass for fatty acid analysis of MOLA117 T was harvested from MA plates grown at 25°C for 4 days. Biomass for polar lipids and respiratory quinone analyses was harvested from cells grown on MB at 25°C for 4 days and cell pellets from 3 L of culture were lyophilized and sent for analysis. Analysis of fatty acid methyl esters, respiratory quinones, polar lipids and GC content was performed by the DSMZ Identification Service (DSMZ, Braunschweig, Germany) according to their protocols (http://www.dsmz.de/services/services-microorganisms/identif ication.html).

The sequence of the 16S rRNA gene of MOLA117 T was determined from PCR products amplified after genomic DNA extraction with the Wizard Genomic DNA purification kit (Promega, Madison, WI) as previously described in Fagervold et al. [START_REF] Fagervold | Pleionea mediterranea gen. nov., sp nov., a gammaproteobacterium isolated from coastal seawater[END_REF].

Bacterial primers 27Fmod (AGRGTTTGATCMTGGCTCAG) [START_REF] Eiler | Composition of freshwater bacterial communities associated with cyanobacterial blooms in four Swedish lakes[END_REF][11] and 1492Rmod (TACGGYTACCTTGTTAYGACTT) [START_REF] Acinas | PCR-Induced Sequence Artifacts and Bias: Insights from Comparison of Two 16S rRNA Clone Libraries Constructed from the Same Sample[END_REF] were used for PCR amplification. PCR products were purified using the Agencourt AMPureXP purification kit (Beckman Coulter France, Villepinte, France) and sequenced with an AB3130xl genetic analyzer (Life Technologies).

Sequence similarities of strain MOLA117 T to sequences available in the NCBI GenBank were analyzed using the Basic Local Alignment Search Tool (BLAST) [START_REF] Altschul | Basic local alignment search tool[END_REF].

Phylogenetic analysis was performed as previously described [START_REF] Fagervold | Pleionea mediterranea gen. nov., sp nov., a gammaproteobacterium isolated from coastal seawater[END_REF], using the ARB software package [START_REF] Ludwig | ARB: a software environment for sequence data[END_REF] and the RAxML Webserver (http://www.phylo.org) for bootstrap analysis (450 replicates) [START_REF] Stamatakis | A Rapid Bootstrap Algorithm for the RAxML Web Servers[END_REF]. This analysis revealed that strain MOLA117 T was phylogenetically related to species within the genera Arenibacter and Cellulophaga.

However, MOLA117 T clearly does not group with any of these genera since the bootstrap values clustering species within the genus Arenibacter together were high, as were the bootstrap values clustering species within the genus Cellulophaga. Nevertheless, the exact phylogenetic position of MOLA117 T is uncertain since the bootstrap values for clustering MOLA117 with either of the genera Arenibacter or Cellulophaga are low (see Figure 1). Also, it is clear from the phylogenetic analysis that MOLA117 T does not belong to any of the other described genera in the family Flavobacteriaceae.

Morphological, physiological and biochemical properties of strain MOLA117 T are listed in Table 1 and in the genus and species descriptions. The major respiratory lipoquinone is MK-6, which is also the major respiratory quinone in Flavobacteriaceae [START_REF] Bernardet | Proposed minimal standards for describing new taxa of the family Flavobacteriaceae and emended description of the family[END_REF]. Pigmentation, salinity requirement for growth and gliding motility are some important characteristics for the description of genera within the family Flavobacteriaceae. All the closest described genera and MOLA117 T are pigmented, but only Zobellia galactivorans Dsij T [START_REF] Barbeyron | Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. nov[END_REF] is positive for flexirubin. This strain also exhibits gliding motility, as do Cellulophaga lytica 23178 T [START_REF] Bowman | Description of Cellulophaga algicola sp nov., isolated from the surfaces of Antarctic algae, and reclassification of Cytophaga uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Cellulophaga uliginosa comb[END_REF] and Maribacter sedimenticola KMM 3903 T [START_REF] Barbeyron | Description of Maribacter forsetii sp nov., a marine Flavobacteriaceae isolated from North Sea water, and emended description of the genus Maribacter[END_REF], but not MOLA117 T and Arenibacter latericius KMM426 T [START_REF] Ivanova | Arenibacter gen. nov., new genus of the family Flavobacteriaceae and description of a new species, Arenibacter latericius sp nov[END_REF] [START_REF] Nedashkovskaya | Arenibacter palladensis sp. nov., a novel marine bacterium isolated from the green alga Ulva fenestrata, and emended description of the genus Arenibacter[END_REF]. Indeed, Arenibacter latericius KMM426 T and MOLA117 T are similar in many characteristics, but they differ in that MOLA117 T can hydrolyze starch while Arenibacter latericius KMM426 T cannot and that MOLA117 T is susceptible to kanamycin. They also exhibit different ranges of growth temperature and pH tolerance; MOLA117 T is the only one that is negative for growth at both 4°C, 37°C and 42°C. Further, even though all compared strains require NaCl for growth (Table 1), MOLA117 T is slightly less halotolerant than related genera (Table 1), as indicated by the more narrow range of NaCl concentrations required for growth. MOLA117 T is the only species, of those compared, which do not reduce nitrate.

The polar lipids that were extracted from MOLA117 T could not be identified in detail by thin layer chromatography and subsequent staining (Figure S2). Acid production was observed with only a few substrates using single carbon sources with the API 50CH system and the API20NE test showed a relatively narrow range of carbon source utilization. However we did see a somewhat wider range when the carbon sources were tested with more specialized media and a 1% carbon source [START_REF] Bruns | Muricauda ruestringensis gen. nov., sp. nov., a facultatively anaerobic, appendaged bacterium from German North Sea intertidal sediment[END_REF] meaning that the carbon utilization patterns from only API 50CH and the API20NE systems probably underestimate the range of substrates MOLA117 T can utilize.

The most predominant fatty acid found in strain MOLA117 T was iso-C 17:0 3-OH at 33.21 % and this fatty acid is found in related genera, like Zobellia, Cellulophaga and Maribacter in relatively high amounts (from 14.9-20.8%), but was not found in the genus Arenibacter. Other major fatty acids found in MOLA117 were: C 15:0 (1.17 %), C 17:1 5

(1.41 %), iso-C 15:0 (11.44 %), iso-C 15:1 (7.12 %), anteiso-C 15:0 (1.26 %), iso-C 17:1 9c (2.62 %), C 16:0 3-OH (2.08 %), iso-C 15:0 3-OH (7.41%), summed feature 3 (4.14 %), and an unknown fatty acid at 14.90 %. Generally, all the genera we compared directly contained C 15:0 , iso-C 15:0 , iso-C 15:1 and anteiso-C 15:0 in varying amounts. The main difference between MOLA117 T and the related genera with respect to fatty acids is that MOLA117 T contains much more iso-C 17:0 3-OH than strains in related genera.

In summary, strain MOLA117 T is different from related genera in numerous metabolic capabilities listed in Table 1 and discussed above. Thus, both the phenotypic and phylogenetic data suggest that strain MOLA117 T should be placed in a new genus Saonia gen. nov., with Saonia flava sp. nov. as the type species and MOLA117 T as the type strain. In addition to properties given in the genus description the species is characterized as follows. Cells are 1.5-3.4 µm long and 0.5-0.6 µm wide, non-spore forming, straight rods. The cells have an EPS like structure and they form aggregates in liquid culture.

Description of

Colonies are yellow, smooth and slightly convex on MA. Growth occurs at temperatures between 15 and 30 °C. Optimal pH for growth is 7, and growth occurs at pH 7-8. Growth occurs in the presence of 1-4% NaCl, but the optimal NaCl concentration is 3 %. H 2 S is not produced. Nitrate is not reduced under aerobic conditions. Using the API 20NE system, MOLA117 T is shown to be positive for urease activity, aesculin hydrolysis and -galactosidase utilization and negative for denitrification, gelatin hydrolysis, indole production, D-glucose fermentation, arginine dihydrolase, -glucosidase, and utilization of D-glucose, L-arabinose, D-mannose, D-mannitol, capric acid, N-acetylglucosamine, D-maltose, potassium gluconate, adipic acid, malic acid, trisodium citrate and phenylacetic acid. Acid production was only observed with a few substrates using the API 50CH system, namely fructose, aesculin, saccharose, trehalose, inulin, raffinose and starch. MOLA117 T could assimilate cellobiose, arabinose, maltose, trehalose, xylose, dextran, lactate, glycerol, sucrose, galactose, glucose and sorbitol as single carbon sources but not lactose, fumarate, succinate, formate, pyruvate, citrate, glutamic acid and acetate. When assayed with the API ZYM system, alkaline phosphatase, esterase lipase (C8), leucine arylamidase, valine arylamidase, acid phosphatase and N-acetyl-glucosaminidase are present, but esterase (C4), lipase (C14), cystine arylamidase, trypsin, -chymotrypsin, napthol-AS-BI-phosphohydrolase, -galactosidase, -galactosidase, glucuronidase, -glucosidase, -Glucosidase, -mannosidase and -fucosidase are absent. MOLA117 T is susceptible to cefotaxime (30 g), ciprofloxacin (5 g), kanamycin (30 g), vancomycin (30 g), rifampicillin (30 g), chloramphenicol (50 g), ampicillin (10 g), and resistant to erythromycin (15 g), penicillin (20 U), polymyxin (50 g), streptomycin (10 g), tetracycline (30 g). The DNA G+C content is 36.9 mol %.

The type strain MOLA117 T (=CIP 110873 T = DSM 29762 T ) was isolated from seawater from the northwestern Mediterranean Sea off the coast of Banyuls-sur-Mer, France.

  Saonia gen. nov. Saonia (Sa.o'ni.a L. fem. N. Sao, the nereid Sao; N.L. fem. n. Saonia named after Sao, the Nereid of "safe" passage, or the rescue of sailors, referring to the marine origin.) Gram-negative, non-motile, aerobic and chemo-organoheterotrophic. Catalase, oxidase and alkaline phosphatase positive. Requires NaCl for growth. Produces yellow pigment but no flexirubin. The most predominant fatty acid is iso-C 17:0 3-OH and the only respiratory lipoquinone is MK-6. Based upon the 16S rRNA gene sequence, the genus forms a separate branch within the family Flavobacteriaceae close to the genera Arenibacter and Cellulophaga. The type species is Saonia flava. Description of Saonia flava sp. nov. Saonia flava (fla'va. L. fem. adj. flava yellow).

Acknowledgements

We are grateful to the Bio2Mar platform (http://bio2mar.obs-banyuls.fr) for providing technical support and access to instrumentation. We thank Marie-Line Escande for assistance with the transmission electron microscopy. We also thank Nyree West for reviewing the manuscript and correcting the English.

Author statement

This work was supported by funds from the "Équipe Mixte de Recherche" a publicprivate partnership linking the University Pierre and Marie Curie, the Centre National de la Recherche Scientifique and Pierre Fabre Laboratories. There is no conflict of interest regarding this article.