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Supervised learning techniques are widely employed in credit card fraud detection, as they make use of the assumption that fraudulent patterns can be learned from an analysis of past transactions. The task becomes challenging, however, when it has to take account of changes in customer behavior and fraudsters’ ability to invent novel fraud patterns. In this context, unsupervised learning techniques can help the fraud detection systems to find anomalies. In this paper we present a hybrid technique that combines supervised and unsupervised techniques to improve the fraud detection accuracy. Unsupervised outlier scores, computed at different levels of granularity, are compared and tested on a real, annotated, credit card fraud detection dataset. Experimental results show that the combination is efficient and does indeed improve the accuracy of the detection., SCOPUS: ar.j, info:eu-repo/semantics/published

The line graph of a graph G has one node per each edge of G, two of them being adjacent only when the corresponding edges have a node of G in common. In this work, we consider the problem of finding the minimum number of edges to delete so that the resulting graph is a line graph, which presents an interesting application in haplotyping of diploid organisms. We propose an Integer Linear Programming formulation for this problem. We compare our approach with the only other existing formulation for the problem and explore the possibility of combining both of them. Finally, we present a computational study to compare the different approaches proposed., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can be composed of and on the minimum step length. Our algorithm relies on dynamic programming and is built on the basis that said curve-fitting task can be tackled as a shortest-path type of problem. Numerical results on synthetic and realistic data sets reveal that our algorithm is able to provide the globally optimal monotone stepwise curve fit for samples with thousands of data points in less than a few hours. Furthermore, the algorithm gives a certificate on the optimality gap of any incumbent solution it generates. From a practical standpoint, this piece of research is motivated by the roll-out of smart grids and the increasing role played by the small flexible consumption of electricity in the large-scale integration of renewable energy sources into current power systems. Within this context, our algorithm constitutes an useful tool to generate bidding curves for a pool of small flexible consumers to partake in wholesale electricity markets., SCOPUS: ar.j, info:eu-repo/semantics/published

International audience; Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.

International audience; Several algorithms for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem (RCPSP) were proposed in the literature. However, fast computable lower bounds usually do not provide the best estimations and the methods that obtain better bounds are mainly based on the cooperation between linear and constraint programming and therefore are time-consuming. In this paper, a new pseudo-polynomial algorithm is proposed to find a makespan lower bound for RCPSP with time-dependent resource capacities. Its idea is based on a consecutive evaluation of pairs of resources and their cumulated workload. Using the proposed algorithm, several bounds for the PSPLIB benchmark were improved. The results for industrial applications are also presented where the algorithm could provide good bounds even for very large problem instances.

Reverse supply chains (RSC) deal with End-Of-Life products (EOL). They provide the benefits of reducing solid waste, creating new jobs, and generating income from the sales of re-manufactured products and savings in raw materials. At the same time, their design is more challenging in comparison with forward supply chains because of hardly predictable reverse flows of EOL products. Conventional approaches to address the uncertainty are usually risk-oriented and do not consider eventual opportunities. In order to overcome this drawback, we propose to use the R* criterion in the decision making process in a context of complete ignorance with a discrete set of equally possible scenarios. This criterion integrated in a new lexicographic algorithm is used in order to take the Decision-Maker’s (DM) behavior into account in different ways regarding if the uncertainty is considered as a risk or as an opportunity. Its performances are evaluated on the case study of facility location problem in reverse logistics.

Automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.

International audience; In this work we formulate a Stackelberg Security Game that coordinates resources in a border patrol problem. In this security domain, resources from different precincts have to be paired to conduct patrols in the border due to logistic constraints. Given this structure, models that enumerate the pure defender strategies scale poorly. We describe the set of mixed strategies using a polynomial number of variables but exponentially many constraints that come from the matching polytope. We then include this description in a mixed integer formulation to compute the Strong Stackelberg Equilibrium efficiently with a branch and cut scheme. Since the optimal patrol solution is a probability distribution over the set of exponential size, we also introduce an efficient sampling method that can be used to deploy the security resources every shift. Our computational results evaluate the efficiency of the branch and cut scheme developed and the accuracy of the sampling method.

In the Rank Pricing Problem (RPP), a firm intends to maximize its profit through the pricing of a set of products to sell. Customers are interested in purchasing at most one product among a subset of products. To do so, they are endowed with a ranked list of preferences and a budget. Their choice rule consists in purchasing the highest-ranked product in their list and whose price is below their budget. In this paper, we consider an extension of RPP, the Rank Pricing Problem with Ties (RPPT), in which we allow for indifference between products in the list of preferences of the customers. Considering the bilevel structure of the problem, this generalization differs from the RPP in that it can lead to multiple optimal solutions for the second level problems associated to the customers. In such cases, we look for pessimistic optimal solutions of the bilevel problem: the customer selects the cheapest product. We present a new three-indexed integer formulation for RPPT and introduce two resolution approaches. In the first one, we project out the customer decision variables, obtaining a reduced formulation that we then strengthen with valid inequalities from the former formulation. Alternatively, we follow a Benders decomposition approach leveraging the separability of the problem into a master problem and several subproblems. The separation problems to include the valid inequalities to the master problem dynamically are shown to reduce to min-cost flow problems. We finally carry out extensive computational experiments to assess the performance of the resolution approaches., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper proposes different algorithms to tackle the Generalized Train Unit Shunting Problem (G-TUSP). This is the pre-operational problem of managing rolling stock in a station, between arrivals and departures. It includes four sub-problems: the Train Matching Problem, the Track Assignment Problem, the Shunting Routing Problem, and the Shunting Maintenance Problem. In our algorithms, we consider different combinations for the integrated or sequential solutions of these sub-problems, typically considered independently in the literature. We assess the performance of the algorithms proposed in real-life and fictive instances representing traffic in Metz-Ville station, which includes four shunting yards. It is a main junction between two dense traffic lines in the east of France. In a thorough experimental analysis, we study the contribution of each sub-problem to the difficulty of the G-TUSP, and we identify the best algorithms. The outcomes of our algorithms are superior to solutions manually designed by experienced railway practitioners.

SIAM: Society for Industrial and Applied Mathematics; International audience; We propose a new family of multilevel methods for unconstrained minimization. The resulting strategies are multilevel extensions of high-order optimization methods based on q-order Taylor models (with q ≥ 1) that have been recently proposed in the literature. The use of high-order models, while decreasing the worst-case complexity bound, makes these methods computationally more expensive. Hence, to counteract this effect, we propose a multilevel strategy that exploits a hierarchy of problems of decreasing dimension, still approximating the original one, to reduce the global cost of the step computation. A theoretical analysis of the family of methods is proposed. Specifically, local and global convergence results are proved and a complexity bound to reach first order stationary points is also derived. A multilevel version of the well known adaptive method based on cubic regularization (ARC, corresponding to q = 2 in our setting) has been implemented. Numerical experiments clearly highlight the relevance of the new multilevel approach leading to considerable computational savings in terms of floating point operations compared to the classical one-level strategy.

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The Safe Set Problem is a type of partitioning problem with particular constraints on the weight of adjacent partition components. Several theoretical results exist in the literature along with a mixed integer linear formulation. We propose a new, more compact, Mixed Integer Linear Program for the (Weighted) Safe Set Problem and Connected Safe Set Problem with a polynomial number of variables and constraints, based on a flow from an additional fictitious node. The model is enriched by symmetry-breaking considerations and a variable reduction subproblem. We test the model on a benchmark of small instances which underline the difficulty of solving the Safe Set Problem through mathematical programming approaches. We also compare it with the only existing formulation in the literature and find that our approach is usually more efficient on a set of benchmark instances.

We propose a nonparametric methodology for intertemporal production analysis that accounts for durable as well as storable inputs. Durable inputs contribute to the production outputs in multiple consecutive periods. Storable inputs are non-durable and can be stored in inventories for use in future periods. We explicitly model the possibility that firms use several vintages of the durable inputs, i.e. they invest in new durables and scrap older durables over time. Furthermore, we allow for production delays of durable inputs and storage costs for storable inputs. We characterize production behavior that is dynamically cost efficient, which allows us to evaluate the efficiency of observed production decisions. For cost inefficient behavior, we propose a measure to quantify the degree of inefficiency. An attractive feature of this measure is that it can be decomposed in period-specific cost inefficiencies. We demonstrate the usefulness of our methodology through an application to the Italian manufacturing sector of fabricated metal products over the period 1995–2007 drawn from the AMADEUS dataset., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper proposes a novel framework to support the generation of strategies for multi-criteria long-term improvement. It can be applied to general preference models but it is illustrated in this article on a Multi-Attribute Value Theory model. The novel contributions to the literature are twofold. Firstly, the framework addresses the issue of resistance to change that may arise during the implementation of a strategy. It constrains a step of improvement to be focused on a single criterion, and minimizes the intensity of operational changes. Secondly, it addresses the realism of the improvement scenarios by treating three types of structural dependencies differently: the positive synergies, the negative synergies and the bottlenecks. The scenarios are generated by finding a set of efficient solutions to a shortest path problem in a graph whose edges represent possible steps of improvement. Each edge is characterized by an increase of rank or level and two penalty functions relating to the difficulty of its execution, one representing a risk associated to bottleneck mechanisms and the other to operational change relative to a previous edge. A case-study using the Shanghai Academic Ranking of World Universities is presented in order to illustrate how this framework could be useful to generate a sequence of strategic actions for the Universite libre de Bruxelles.

The aircraft maintenance processes play an important role in a safe operation of an aircraft. Maintenance services organizations take responsibility for the maintenance process and approve the airworthiness of an aircraft after undertaking the maintenance activities. International law determines the quality of aircraft maintenance processes by setting requirements concerning, among other, a quality management system, a safety management system and operators’ competences. As a consequence of the rising number of aircraft in operation, the volume of maintenance activities grows. However, the customers increasingly pose requirements concerning the minimization of the maintenance service lead time. In order to remain competitive, the maintenance service organizations have to reduce the lead time of their services. However, this objective in not easy to attain, since the complexity of aircraft maintenance operations require specific skills and pose a number of organisational and technical constraints to be respected during the maintenance process. In the paper, a mathematical programming model is developed in order to help decision makers in managing the operators’ skills during the operators assignment to the activities to be performed. In particular, the Hall’s marriage theorem is used to formalise complex restrictions of operators assignment to maintenance activities. The objective of the optimization problem is to minimize total makespan time. The model is applied to a case study.

In this paper, a bilevel programming model is proposed to study a problem of market regulation through government intervention. One of the main characteristics of the problem herein analyzed is that the government monopolizes the raw material in one industry, and competes in another industry with private firms for the production of commodities. Under this scheme, the government controls a state-owned firm to balance the market; that is, to minimize the difference between the produced and demanded commodities. On the other hand, a regulatory organization that coordinates private firms aims to maximize the total profit by deciding the amount of raw material bought from the a state-owned firm. Two equivalent single-level reformulations are proposed to solve the problem. The first reformulation is based on the strong duality condition of the lower level and results in a continuous non-linear model. The second reformulation resorts to the complementarity slackness optimality constraints yielding a mixed-integer linear model. Additionally, three heuristic algorithms are designed to obtain good-quality solutions with low computational effort. In this problem, the feasible region of the dual problem associated to the follower is independent from the leader's decision. Therefore, the proposed heuristics exploit this particular characteristic of the bilevel model. Moreover, the third heuristic hybridizes the other two algorithms to enhance its performance. Extensive computational experimentation is carried out to measure the efficiency of the proposed solution methodologies. A case study based on the Mexican petrochemical industry is presented. Additional instances generated from the case study are considered to validate the robustness of the proposed heuristic algorithms. Numerical results indicate that the hybrid algorithm outperforms the other two heuristics. However, all of them demonstrate to be good alternatives for solving the problem. Additionally, optimal solutions of all the instances are obtained by using good quality solutions (given by the hybrid algorithm) as initial solutions when solving the second reformulation via a general purpose solver., SCOPUS: ar.j, info:eu-repo/semantics/published

Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks; (ii) define the rules of how to compose algorithms from the building blocks; and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process., SCOPUS: ar.j, info:eu-repo/semantics/published

International audience; We consider a problem minimizing the total duration of accomplishing missions performed by heterogeneous vehicles. The problem respects constraints related to vehicles’ capabilities and energy capacities. The goal is to determine the best routes of each vehicle deployed by choosing which waypoints to pass and which observations to perform. Each vehicle has a particular distance matrix and a limited energy. In order to provide high quality solutions within reasonable computational time, two decomposition-based approximate methods were implemented: (i) the Multiphase heuristic, and (ii) the Two-Phase iterative heuristic. The performance of the methods is evaluated against the Branch-and-Cut algorithm using generated instances.

Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are NP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed AAD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases., SCOPUS: ar.j, info:eu-repo/semantics/published

In this paper, we analyze different mathematical formulations for general Stackelberg games (GSGs)and Stackelberg security games (SSGs). We consider GSGs in which a single leader commits to a utility maximizing strategy knowing that p possible followers optimize their own utility taking the leader's strategy into account. SSGs are a type of GSG that arise in security applications where the strategies of the leader consist of protecting a subset of targets and the strategies of the p followers consist of attacking a single target. We compare existing mixed integer linear programming (MILP)formulations for GSGs, ranking them according to the tightness of their linear programming (LP)relaxations. We show that SSG formulations are projections of GSG formulations and exploit this link to derive a new SSG MILP formulation that (i)has the tightest LP relaxation known among SSG MILP formulations and (ii)has an LP relaxation that coincides with the convex hull of feasible solutions in the case of a single follower. We present computational experiments empirically comparing the difficulty of solving the formulations in the general and security settings. The new SSG MILP formulation remains computationally efficient as problem size increases., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper adresses the problem of disassembly process planning taking into account the quality or states of the product to be disassembled. We propose an approach which is able to return the best disassembly level for a product considering the disassembly cost and the state of the product and/or the states of its subassemblies or components. The state of the product is represented using the concept of « Potentiel d’Utilisation Residuel (PUR) » which is assumed to be a Gaussian random variable with known truncated distribution. A stochastic program is proposed to model the problem with the objective of maximizing the disassembly process profit. The latter is calculated as the difference between the positive revenue generated by recovered parts and the costs of the disassembly tasks. The revenue of a recovered part is a function of PUR. The developed approach is tested on two example case studies from the literature to analyze the impact of uncertain product quality on its disassembly process planning.

International audience; To remain competitive in currently unpredictable markets, the enterprises must adapt their manufacturing systems to frequent market changes and high product variety. Reconfigurable manufacturing systems (RMSs) promise to offer a rapid and cost-effective response to production fluctuations under the condition that their configuration is attentively studied and optimised. This paper presents a decision support tool for designing reconfigurable machining systems to be used for family part production. The objective is to elaborate a cost-effective solution for production of several part families. This design issue is modelled as a combinatorial optimization problem. An illustrative example and computational experiments are discussed to reveal the application of the proposed methodology. Insight gained would be useful to the decision makers managing the configuration of manufacturing systems for diversified products.

Assigning aircraft to gates is an important decision problem that airport professionals face every day. The solution of this problem has raised a significant research effort and many variants of this problem have been studied. In this paper, we review past work with a focus on identifying types of formulations, classifying objectives, and categorising solution methods. The review indicates that there is no standard formulation, that passenger oriented objectives are most common, and that more recent work are multi-objective. In terms of solution methods, heuristic and metaheuristic approaches are dominant which provides an opportunity to develop exact and approximate approaches both for the single and multi-objective problems., SCOPUS: re.j, info:eu-repo/semantics/published

International audience; In order to implement the human-centric manufacturing and sustainability concepts in industry, an important effort should be done in order to model working conditions for human operators and improve them. Several studies have been conducted for mass production assembly lines where short cycle times make the work content highly repetitive. However, the case of low-volume production with long cycle times and different impacts on human operators has been rarely considered in the literature. In this paper, we develop a model to take into account the associated ergonomic risks in assembly lines with long cycle times. An optimization method is also developed in order to schedule tasks and assign the required tasks to a set of human operators taking into account the existing ergonomic risks.

International audience; The assembly process is extremely complex for aircraft and its management requires to address numerous optimization problems related to the assignment of tasks to workstations, staffing problem for each workstation and finally the assignment of tasks to operators at each workstation. This paper treats the latter problem dealing with the assignment of tasks to operators under ergonomic constraints. The problem of optimal tasks scheduling in aircraft assembly line is modelled as Resource-Constrained Project Scheduling Problem (RCPSP). The objective of this research is to assign tasks to operators and to find an optimal schedule of task processing under economic and ergonomic constraints. Two different models to solve this problem are presented and evaluated on an industrial case study.

International audience; This paper addresses the problem of profit-oriented disassembly line design and balancing considering partial disassembly, presence of hazardous parts and uncertainty of task processing times. Few papers have studied the stochastic disassembly line balancing problem and existing approaches have focused on heuristic and metaheuristic methods. Most existing work has concentrated on complete disassembly where task times are assumed to be normal random variables and where AND/OR graphs are not considered. The objective of this paper is the design of a serial line that obtains the maximum revenue and then balances the workload under uncertainty. The processing time of a disassembly task is assumed to be a random variable with any known probability distribution. An AND/OR graph is used to model the precedence relationships among tasks. Stochastic programming models and exact-based solution approaches combining the L-shaped algorithm and Monte Carlo sampling techniques are proposed. The relevance and applicability of the proposed models and solution methods are shown by solving efficiently a set of disassembly problem instances from the literature.

Future trends in management and operation of assembly systems: from customized assembly systems to cyber-physical systems.

Classical control of cyber-physical systems used to rely on basic linear controllers. These controllers provided a safe and robust behavior but lack the ability to perform more complex controls such as aggressive maneuvering or performing fuel-efficient controls. Another approach called optimal control is capable of computing such difficult trajectories but lacks the ability to adapt to dynamic changes in the environment. In both cases, the control was designed offline, relying on more or less complex algorithms to find the appropriate parameters. More recent kinds of approaches such as Linear Model-Predictive Control (MPC) rely on the online use of convex optimization to compute the best control at each sample time. In these settings optimization algorithms are specialized for the specific control problem and embed on the device.This paper proposes to revisit the code generation of an interior point method (IPM) algorithm, an efficient family of convex optimization, focusing on the proof of its implementation at code level. Our approach relies on the code specialization phase to produce additional annotations formalizing the intended specification of the algorithm. Deductive methods are then used to prove automatically the validity of these assertions. Since the algorithm is complex, additional lemmas are also produced, allowing the complete proof to be checked by SMT solvers only.This work is the first to address the effective formal proof of an IPM algorithm. The approach could also be generalized more systematically to code generation frameworks, producing proof certificate along the code, for numerical intensive software.

International audience; This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results.

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system.

Research has shown the effectiveness of inferring user interests from social neighbors, also called "social profiling". However, the evolution in the social profile is not widely taken into consideration. To overcome this drawback, we propose a time-aware social profiling method that considers the temporal factors of the information and the relationships between the user and his/her social neighbors. This method aims at weighting user interests in the social profile, by applying a time decay function. The temporal score of a given interest is computed by combining the temporal score of information used to extract the interests with the temporal score of individuals who share the information in the network. The experiments conducted on a co-authorship network, DBLP showed that the time-aware social profiling process applying our proposed time-aware method outperforms the existing time-agnostic social profiling process. The combinatorial analysis and the parametric study led us to observe that in the context of co-authorship network, the individual temporal score has more influence than the information temporal score. As this kind of network does not exhibit a rapid evolution of information and relationships, to obtain a relevant social profile, the information should be damped slowly.

International audience; The reverse supply chain and disassembly processes are getting more and more important for tackling the burden of waste electrical and electronic equipment. The disassembly's complexity and frequently manual operation makes this process relatively expensive compared to its potential profit. The collection of End-of-Life product is also a big issue dealing with vehicle routing. Thus, the decisions taken for collection and disassembly of End–of–Life products need to be optimized. In this work, an optimisation model is developed for incorporating these problems. Our experimental study shows joint optimization of collection and disassembly with coordination between them improves the global performance of the reverse supply chain including lower total cost corresponding to the component demand satisfaction.

Ergonomic risks (ER) should be taken into account in the operational planning.We provide a literature survey on operational planning that considers physical ER.We discuss contributions to assembly line balancing and job rotation scheduling.We summarize major findings and work out future research directions. Factors such as repetitiveness of work, required application of forces, handling of heavy loads, and awkward, static postures expose assembly line workers to risks of musculoskeletal disorders. As a rule, companies perform a post hoc analysis of ergonomic risks and examine ways to modify workplaces with high ergonomic risks. However, it is possible to lower ergonomic risks by taking ergonomics aspects into account right from the planning stage. In this survey, we provide an overview of the existing optimization approaches to assembly line balancing and job rotation scheduling that consider physical ergonomic risks. We summarize major findings to provide helpful insights for practitioners and identify research directions.

Closing the loop in the supply chains is one of the mandatory conditions for more sustainable development. The Collection-Disassembly Problem appears in the reverse part of the closed-loop supply chains. Its aim is to coordinate the activities of collection of end-of-life products from collection centres and their subsequent disassembly. The disassembly step is required for efficient remanufacturing and recycling of returned products. The Collection-Disassembly problem integrates such optimization problems as dynamic lot-sizing and vehicle routing in general cases. In this paper, we develop a Two-Phase Iterative Heuristic to efficiently address large size instances. The numerical tests show that the heuristic provides good solutions under acceptable computational time.

In this paper, a long-term production planning problem is considered with the objective criteria Cmax and Tmax and under resource capacity and precedence constraints. The case study presented is characterized by four years planning horizon, 3552 operations, 51 workers and 57 units of equipment. The solution method elaborated in this study is a heuristic algorithm. Its performances are evaluated in numerical experiments. New procedures for makespan and resource load estimation are developed in order to identify bottleneck resources. The makespan estimation algorithm is tested on the well-known PSPLIB benchmark library where the best known lower bounds are improved for 5 instances. This procedure can also be used for the estimation of the gap from optimal value of the makespan time provided by the heuristic algorithm.

International audience; New generation of rotary transfer machines processing different models of parts is considered. In order to enhance the costeffectiveness of mixed-model rotary transfer machines, the problems of process planning for the parts to be machined and the configuration of a rotary transfer machine are integrated in the same optimisation problem. This problem is modelled as a combinatorial optimisation problem. The decisions to be taken simultaneously concern the orientation of parts for machining, the machining parameters for processing the parts as well as the configuration of machining units to be used at working positions of the machine. Constraints related to the design of such units – spindle heads, turrets – and working positions, as well as precedence constraints related to machining operations, are taken into account. The problem consists in minimising an estimated cost of the rotary transfer machine, while reaching a given output and satisfying all the constraints. The proposed methods to solve theproblem are based on its MIP formulation. The optimisation techniques are validated on an industrial case study. Numericalexperiments evaluate the efficiency of the approach against the variety of parts to be produced.

Assembly lines are used for a large variety of products in different industrial sectors. In this paper the focus is placed on complex assembly systems and workstations used for the final assembly of large and bulk products, such as trucks, aircrafts, buses, tool machines. An high number of tasks to be performed at a single assembly station, several workers involved in parallel in the assembly process and long Takt times make such systems different from the models intensively studied in the literature (e.g. the traditional Simple Assembly Line Balancing Problem). This study firstly presents a new balancing model to address the problem of the total cost minimization when different operator skills are involved at the same time and then it applies the model to a real industrial case.

Winning the communication battle is an important part of winning the war. Numerous external communications targets must be defined, including media, public opinion (domestic and foreign), political and military partners, and enemy combatants. This paper examines the French Ministry of Defense’s (MoD) communication strategy during the first weeks of Operation Serval, part of the UN-initiated African-led International Support to Mali in 2012–2013. Based on readings (press, official communications), a content analysis, and semi-structured interviews with MoD public affairs personnel (military and civilian), this paper looks at the traditional information-sharing communication challenges of armed forces during operations, including the balance between the need for secrecy, especially during frontline combat, and the (French) people’s right to be informed. The main thesis of this paper is that the armed forces are a pragmatic institution, which has a wide range of tools to manage the media, in spite of the media’s inevitable criticism.

This paper deals with an optimization problem that arises when a new paced simple assembly line has to be designed subject to a limited number of available workstations, cycle time constraint, and precedence relations between necessary assembly tasks. The studied problem, referred to as SALPB-S, consists in assigning the set of tasks to workstations so as to find the most robust line configuration (or solution) under task time variability. The robustness of solution is measured via its stability radius, i.e., as the maximal amplitude of deviations for task time nominal values that do not violate the solution feasibility. In this work, the concept of stability radius is considered for two well-known norms: ? 1 and ? ∞ . For each norm, the problem is proven to be strongly NP -hard and a mixed-integer linear program (MILP) is proposed for addressing it. To accelerate the seeking of optimal solutions, an upper bound on the stability radius is devised and integrated into the corresponding MILP. Computational results are reported on a collection of instances derived from classic benchmark data used in the literature for the Simple Assembly Line Balancing Problem.

A problem of design of reconfigurable rotary transfer machines is considered. Parts are divided into batches. Parts of a batch are located at the loading position of rotary table in a given sequence and they are processed simultaneously. Operations are partitioned into groups which are performed by spindle heads or by turrets. Constraints related to the design of spindle heads, turrets, and working positions, as well as precedence constraints related to operations, are given. The problem consists in minimizing the estimated cost of the transfer machine, while reaching a given output and satisfying all the constraints. The proposed methods to solve the problem are based on sequential assignment of operations to machining modules. Experimental results with different heuristics are reported.

This paper presents a new, carefully designed algorithm for five bi-objective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) both, the weighted and non-weighted total tardiness of all jobs. The proposed algorithm combines two search methods, two-phase local search and Pareto local search, which are representative of two different, but complementary, paradigms for multi-objective optimization in terms of Pareto-optimality. The design of the hybrid algorithm is based on a careful experimental analysis of crucial algorithmic components of these two search methods. We compared our algorithm to the two best algorithms identified, among a set of 23 candidate algorithms, in a recent review of the bi-objective permutation flow-shop scheduling problem. We have reimplemented carefully these two algorithms in order to assess the quality of our algorithm. The experimental comparison in this paper shows that the proposed algorithm obtains results that often dominate the output of the two best algorithms from the literature. Therefore, our analysis shows without ambiguity that the proposed algorithm is a new state-of-the-art algorithm for the bi-objective permutation flow-shop problems studied in this paper. © 2010 Elsevier Ltd. All rights reserved., SCOPUS: ar.j, info:eu-repo/semantics/published

Intra-domain routing protocols are based on shortest path first (SPF) routing, where shortest paths arecalculated between each pair of nodes (routers) using pre-assigned link weights, also referred to as linkmetric. These link weights can be modified by network administrators in accordance with the routingpolicies of the network operator. The operator’s objective is usually to minimize traffic congestion orminimize total routing cost subject to the traffic demands and the protocol constraints. However,determining a link weights combination that best suits the network operator’s requirements is a difficulttask.This paper provides a survey of meta-heuristic approaches to traffic engineering, focusing on local searchapproaches and extensions to the basic problem taking into account changing demands and robustnessissues with respect to network failures., FLWIN, info:eu-repo/semantics/published

International audience; A paced assembly line consisting of several workstations is considered. This line is intended to assemble products of different types. The sequence of products is given. The sequence of technological tasks is common for all types of products. The assignment of tasks to the stations and task sequence on each station are known and cannot be modified, and they do not depend on the product type. Tasks assigned to the same station are performed sequentially. The processing time of a task depends on the number of workers performing this task. Workers are identical and versatile. If a worker is assigned to a task, he/she works on this task from its start till completion. Workers can switch between the stations at the end of each task and the time needed by any worker to move from one station to another one can be neglected. At the line design stage, it is necessary to know how many workers are necessary for the line. To know the response to this question we will consider each possible takt and assign workers to tasks so that the total number of workers is minimized, provided that a given takt time is satisfied. The maximum of minimal numbers of workers for all takts will be considered as the necessary number of workers for the line. Thus, the problem is to assign workers to tasks for a takt. We prove that this problem is NP-hard in the strong sense, we develop an integer linear programming formulation to solve it, and propose conventional and randomized heuristics.

International audience; We consider a multi-echelon joint inventory-location (MJIL) problem that makes location, order assignment, and inventory decisions simultaneously. The model deals with the distribution of a single commodity from a single manufacturer to a set of retailers through a set of sites where distribution centers can be located. The retailers face deterministic demand and hold working inventory. The distribution centers order a single commodity from the manufacturer at regular intervals and distribute the product to the retailers. The distribution centers also hold working inventory representing product that has been ordered from the manufacturer but has not been yet requested by any of the retailers. Lateral supply among the distribution centers is not allowed. The problem is formulated as a nonlinear mixed-integer program, which is shown to be NP-hard. This problem has recently attracted attention, and a number of different solution approaches have been proposed to solve it. In this paper, we present a Lagrangian relaxation-based heuristic that is capable of efficiently solving large-size instances of the problem. A computational study demonstrates that our heuristic solution procedure is efficient, and yields optimal or near-optimal solutions.

Design solutions have been proposed to implement generic data structures, however such techniques dedicated to algorithms are not well known. This article discusses various recurrent problems encountered when designing reusable, extensible algorithms for operations research. It explains how to use object-oriented concepts and the notion of genericity to design algorithms that are independent of the data structures and the algorithms they use, but that can still interact deeply with them. An object-oriented design is sometimes considered to be less efficient than a classical one, and operations research is one of these scientific fields where efficiency really matters. Hence, the main goal of this article is to explain how to design algorithms that are both generic and efficient. It also discusses specific recurring design issues for operations research software and proposes solutions that improve the genericity of the algorithms. Copyright © 2005 John Wiley & Sons, Ltd.

The combined network design and (distributed)traffic routing problem can be formulated as a large-scale multi-period mixed integer optimization problem. This problem com-bines network design decisions and routing decisions, with time-dependent demands. A compact formulation for this problem,based on the aggregation of flows by destination, was proposedin [1]. They observed that the resolution on realistic instancesbecomes intractable and unscalable with state-of-the-art solversdue to the weak linear programming bound provided by theformulation. In this paper, we consider an extended formulationwhere flows are decomposed by origin-destination pairs, whilekeeping the requirement of destination-based routing. The qualityof the extended formulation and the computational time neededto solve it are evaluated on a representative set of networktopologies and traffic demands, and compared to results obtainedwith the base formulation of [1]. The extended formulation canthen be considered for more efficient resolution methods involvingdecomposition and cutting planes approaches., info:eu-repo/semantics/published

International audience; The timetabling step in railway planning is based on the estimation of the running times. Usually, they are estimated as the shortest journey duration increased of a short time supplement. Estimating the running time amounts to define the speed profile which indicates the speed that the train driver must hold at each position. The approach proposed in this paper produces a set of solutions optimizing both the running time and energy consumption. The approach is based on an original method of speed profiling performed by a multi-objective evolutionary algorithm. The speed profiles found by the evolutionary algorithm are all compromises between journey duration on the one hand and energy consumption on the other hand. A set of results obtained on two real-life instances are analyzed and discussed to highlight the relevance of such an approach in an operational context.

Multiobjective optimization problems are problems with several, typically conflicting, criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multiobjective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multiobjective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices, but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multiobjective algorithms. © 2012 IEEE., info:eu-repo/semantics/published

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuristic information, how to select the best solutions for updating the pheromone information, and how to define and use weights to aggregate the different objectives. This formulation reveals more similarities than previously thought in the design choices made in existing MOACO algorithms. The main contribution of this paper is an experimental analysis of how particular design choices affect the quality and the shape of the Pareto front approximations generated by each MOACO algorithm. This study provides general guidelines to understand how MOACO algorithms work, and how to improve their design. © 2012 Springer Science + Business Media, LLC., SCOPUS: ar.j, info:eu-repo/semantics/published