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This study introduces new probabilistic constraints and objective functions to manage the uncertain nature of the renewable energy sources in single-item capacitated lot sizing problem for flow-shop configurations by integrating the capacity contract selection problem with multiple energy sources. The aim of the probabilistic models built by considering the different probabilistic constraints and objective functions is to provide a decision-making tool and to promote the use of renewable energy sources in manufacturing industry despite of their stochastic nature. Mixed Integer Non-Linear Programming models are proposed by integrating the uncertainty of the renewable energy sources based on different features. The developed models are tested on a small-size instance and the results of the models are compared in terms of economical, ecological and reliability aspects.

The problem of determining economic production quantities and demands, with respect to prices for a hybrid manufacturing / remanufacturing system is addressed in this paper. Most of studies in the literature consider same market for new and remanufactured (as-good-as new assumption) and separate production line. We consider in this paper that products are processed on a common line and are sold in distinct market with price sensitive demands. The problem is modelled and solved with a non-linear model. A numerical analysis is developed to validate the model and analyze its feasibility and limits.

International audience; The growth of e-commerce, stimulated by wider access to Internet services for both customers and companies buying and selling products through this channel, has generated a huge potential for consolidation and coordination of flows through multi-level distribution systems. In addition, to address the particular challenges of the last mile, the transition from the use of internal combustion engine vehicles (ICEVs) to alternative fuel vehicles (AFVs), the implementation of access restrictions in densely populated areas, and the integration of Customer-to-Parcel (C2P) stations (e.g., lockers and pick-up points) have become frequently adopted measures. In contrast to the classical two-echelon vehicle tour problem (2E-VRP), in this study, customers can be served either at C2P stations or by home delivery within a specified time window. We consider the satellites of the second echelon as potential C2P stations, and the grey areas are the home delivery customers located at the borders of the urban centres.; La croissance du e-commerce, stimulée par un accès plus large aux services Internet, tant pour les clients que pour les entreprises qui achètent et vendent leurs produits par ce canal, a généré un énorme potentiel de consolidation et de coordination des flux à travers des systèmes de distribution multi-échelons. De plus, pour répondre aux défis particuliers du dernier kilomètre, la transition de l'utilisation de véhicules à moteur à combustion interne (ICEV) vers des véhicules à carburant alternatif (AFV), la mise en place de restrictions d'accès dans les zones densément peuplées et l'intégration de stations Customer-to-Parcel (C2P) (par exemple, des casiers et des points de retrait) sont devenues des mesures fréquemment adoptées. Contrairement au problème classique de tournées de véhicules à deux échelons (2E-VRP), dans cette étude, les clients peuvent être servis soit dans des stations C2P, soit par une livraison à domicile dans une fenêtre de temps spécifiée. Nous considérons les satellites du second échelon comme des potentielles stations C2P, et les zones grises sont constituées des clients en livraison à domicile situés aux frontières des centres urbains.

International audience; Nowadays, energy saving is one of the main concerns of companies. Therefore, different studies have developed and integrated methods to measure the energy consumption of machine tools. In this paper, an integrated economic production quantity (EPQ) model that considers energy consumption is analyzed. The concept of specific energy consumption (SEC) is used to evaluate the energy consumption during the production time of the machine.Three types of SEC, depending on the production rate, were considered. By minimizing the total cost, the status of the machine in the non-production phase, the optimal production rate, the optimal cycle time, and the influence of these different methods of measuring energy consumption are defined. The results of the study show that the difference in using the SEC -types model does not affect the state selection of the machine during the non-production phase but can change the optimal solutions of the overall system. From the manager's point of view, the choice of different models to represent the energy consumption of the machine leads to different optimal decisions. Numerical analysis are performed, and results are discussed.

Retailers with short lifetime products in stock always face a problem of whether new products should be ordered when on-hand products partially decay and how to deal with the old products if a new batch is ordered. In this article, we consider the sales of a perishable product with a fixed short lifetime in two shelves, where new items of the product in a regular shelf are sold in a preset normal price, and old items in a markdown (discount) shelf are sold in a discounted price. We study the problem of the joint ordering of new items and pricing of old items and propose a joint ordering and markdown policy when the demand of the product depends on its price, and freshness as well as unsatisfied demand is lost. First, we formulate a one-period model, in which the present shelf ages of items in the two shelves are considered and use the Karush–Kuhn–Tucker condition to analytically obtain the optimal solution of the joint ordering and markdown problem. Second, numerical experiments are conducted to evaluate the performance of the two-shelf policy when the optimal solution of the one-period model is applied to the multiperiod problem in the form of a myopic policy. The results show that the proposed two-shelf joint ordering and markdown policy for perishable products performs better than the traditional one-shelf policy. Note to Practitioners —In this article, we develop a joint ordering and markdown policy for perishable products deployed on two shelves: a regular shelf for fresh products and a markdown shelf for less fresh products. Although the closed-form policy is obtained for the one-period situation with deterministic demand, it is not difficult to extend it to the multiperiod case with random demand. This article not only reveals the underlying reason of employing both regular shelf and markdown shelf in the practice of retailing management for perishable products but also proposes an easy-to-implement ordering and markdown policy, which will bring higher profit than the traditional one-shelf policy and will improve the performance of managing a perishable products retailing system.

International audience

International audience

International audience; The rapid uptake of the Wireless Body Area Networks (WBAN) and their services poses unprecedented security requirements. WBAN are evolving to support these requirements. Fulfilling these tasks is challenging as their mobile context is increasingly complex, heterogeneous, and evolving. One potential solution to meet the WBAN security requirements is trust management that helps to reach a more secure and adaptable WBAN environment. Accordingly, this article aims to serve as a brief survey of trust management approaches within intra-WBAN and inter-WBAN. For that, we first summarize trust management concepts, discuss WBAN challenges and classify the attacks on WBAN trust management models. Subsequently, we detail and compare the existing trust based approaches in a WBAN context. We pinpoint their limitations and provide a new classification of these different approaches. We also propose a set of best practices that may help the reader to build a robust and an efficient trust management framework. We complete this survey by highlighting the open future directions and perspectives for research.

Our aim in this study is to propose a novel approach for optimizing a variant of the home health care routing problem, in which the health-care workers perform a requested number of visits to patients by using electric vehicles. Our approach on the one hand uses multi-objective optimization methods to find optimal planning routes of EVs and on the other hand deals with uncertainty in some major problem constraints. The goal is to help decision makers choosing appropriate routes; while minimising the cost of electric vehicular displacements in terms of: the total travel cost for all home-health workers and the waiting time for all patients. Moreover, we consider in our problem the fuzzy nature of some major constraints.

International audience; Emergency department is a key component of the health system where the management of crowding situations is crucial to the well-being of patients. This study proposes a new machine learning methodology and a queuing network model to measure and optimize crowding through a congestion indicator, which indicates a real-time level saturation.

International audience; The problem addressed in this paper was motivated by a real optimization problem of supply chain of the hospital center of Troyes (HCT). The HCT is currently seeking to review and improve its logistics processes. The implementation of techniques and methods of operational research must provide solutions to improve the efficiency of logistics activities. In this work, the studied problem focuses on the catering component of the hospital logistics. A novel mathematical model for the production scheduling of multi-products and multi-stages food processes in hospital catering is proposed. This mathematical model has been implemented in commercial solver CPLEX and it has been tested on real instances of HCT and from the literature. The implementation results of the mathematical model proposed have proved its efficiency for the scheduling of the food production process.

International audience; In less than truckload transportation, shippers collaborate to reduce their logistics costs by consolidating their transportation requests in the procurement of transportation services from a carrier for serving the requests. In this paper, we study shipper collaboration in forward and reverse logistics, in which multiple shippers with forward or/and reverse logistics operations consolidate their transportation requests. In the forward and reverse logistics, manufacturers deliver new products to their customers and used products are collected from customers and transported to remanufacturers for repair or reproduction. This gives rise to a new vehicle routing problem with pickup and delivery requests and three different types of depots (product depots, vehicle depots and recycle depots). A hybrid approach combining greedy randomized adaptive search procedure (GRASP) and iterated local search (ILS) is proposed to find a near optimal solution of the problem. Numerical experiments on a large set of randomly generated instances with different problem sizes demonstrate that shipper collaboration in forward and reverse logistics can realize significant cost savings compared with the isolated operation of each shipper without cooperation, and the proposed approach is effective in the sense that it can find a high quality solution in a reasonable computation time.

International audience; Disassembly lot sizing problem is one of the important operational problems in disassembly systems. It can be defined as the problem of determining the disassembly quantity and timing of the used-products to fulfill the demand of their parts over a finite planning horizon. This paper considers the case of multiple product types with parts commonality and the objective is to minimize the sum of setup, disassembly operation, and inventory holding costs. High inventory holding cost can be generated: because of disparity between independent and unbalanced demands, and the disassembly of one unit of used-product generates all the parts with different ratios. Aggregate formulation (AGG) can be used to model this problem by considering disposal decisions. Linear-Programming (LP) relaxation of this model doesn’t give very good lower bound, especially for the large-sized instances. We aim to improve lower bound of the problem. Facility Location-based formulation (FAL) is developed which can obtain optimal or near optimal solution by using LP relaxation approach. A two-phase heuristic is proposed which constructs an initial solution by using LP relaxation approach, and then improves by a dynamic programming based heuristic. Computational experiments are conducted on randomly generated test problems which show that the models and methods can give optimal or near-optimal solutions in very short computational times.

International audience; Des évènements catastrophiques en milieu urbain peuvent causer des impacts considérables. Selon l'étendue et la puissance de l'évènement, cela peut générer un grand volume de débris. Ce travail traite le transport de débris modélisé comme un problème de planification des équipes de travail, suivi de tournées multiples réalisées par des camions-bennes. Des résultats intéressants ont été obtenus pour une étude de cas du tremblement de terre au Haïti en 2010.

Background In France, the number of emergency department (ED) admissions doubled between 1996 and 2016. To cope with the resulting crowding situation, redirecting patients to new healthcare services was considered a viable solution which would spread demand more evenly across available healthcare delivery points and render care more efficient. The objective of this study was to analyze the impact of opening new on-demand care services based on variations in patient flow at a large hospital emergency department. Methods We performed a before-and-after study investigating the use of unscheduled care services in the Aube region in eastern France, that focused on ED attendance at Troyes Hospital. A hierarchical clustering based on co-occurrence of diagnoses was applied which divided the population into different multimorbidity profiles. Temporal trends of the resultant clusters were also studied empirically and using regression models. A multivariate logistic regression model was constructed to adjust the periodic effect for appropriate confounders and therefore confirm its presence. Results In total, 120,722 visits to the ED were recorded over a 24-month period (2018–2019) and 16 clusters were identified, accounting for 94.76% of all visits. There was a decrease of 56.77 visits per week in seven specific clusters and an increase of use of unscheduled health care services by 328.12 visits per week. Conclusions Using an innovative and reliable methodology to evaluate changes in patient flow through the ED, these findings may help inform public health policy experts on the implementation of unscheduled care services to ease pressure on hospital EDs.

International audience; We study a new competitive multi-facility location and quality design problem in a continuous space. The facility location and quality design are considered together because of their interdependence. Especially, new entrant facilities compete for customer demands with existing ones and the latter's reactions are taken into account. The goal is to maximize the profit of all new entrant facilities by optimally determining their locations and qualities. For this problem, a probabilistic Huff-like gravity model is adopted to analyze the market share to be captured by new and existing facilities, and then a mathematical programming model is provided based on the market share analysis. Since it is shown to be strongly NP-hard, a new iterative solution framework is first proposed to solve it, where at each iteration, new configurations of facility locations are firstly generated, and then the quality decisions of all facilities are modelled as a competitive decision process by a non-cooperative game. The best qualities for new and existing facilities are determined by their Nash equilibrium. Finally, optimal or near-optimal solutions are calculated. Then based on the proposed solution framework, a particle swarm optimization-based approach is developed. Computational results for randomly generated instances indicate that the devised algorithm is able to find suitable locations and qualities of newly entering facilities in a competitive environment and outperforms favorably a genetic algorithm-based approach.

The levelized cost of energy (LCOE) is a widespread metric that doesn't disfavor any power generation or saving technology. In this paper, a stochastic model of the LCOE is provided. The proposed model includes all main parameters to be applied on renewable, non-renewable and energy saving systems. These parameters were modeled through non-homogeneous Poisson process as well as Weibull functions. Besides, a continuous optimization model is proposed to calculate the optimal LCOE under the constraints of main Capex/Opex expenditures. Under such conditions, the optimal value of the levelized cost of energy is obtained, which captures parameters' influence on this metric. Afterwards, these parameters' influences on the LCOE were assessed through a sensitivity analysis.

International audience; In this paper, we study a complex outpatient planning problem in the chemotherapy department. The planning concerns sequences of patients’ treatment sessions subject to exact in-between resting periods (i.e., exact time-lags). The planning is constrained by the hospital infrastructure and the availability of medical staff (i.e., multiple time-varying resources’ availability). In order to maximize the patients’ service quality, the objective of the function considered is to minimize the total wait times, which is equivalent to the criteria for minimizing the total completion time. Our main contribution is a thorough analysis of this problem, using the Hybrid Flow Shop problem as a theoretical framework to study the problem. A novel Mixed Integer Linear Programming (MILP) is introduced. Concerning the resolution methods, priority-based heuristics and an adapted genetic algorithm (GA) are presented. Numerical experiments are conducted on historical data to compare the performances of the approximate resolution methods against the MILP solved by CPLEX. Numerical results confirm the performances of the proposed methods.

International audience; Renewable energies are increasingly used around the world to replace fossil energy resources such as gas, coal, and oil sources in order to reduce greenhouse gases. Eco-industrial parks promote the use and sharing of renewable energy sources between factories in a collective self-consumption framework. This article presents a new strategy of photovoltaic self-consumption in an eco-industrial park, that combines collective and individual self-consumption. This strategy has been compared with the classical configuration of self-consumption, in which factories do not share a common photovoltaic installation. Two mathematical models have been proposed and solved for these two configurations, the results show that the new strategy is more efficient than the classical configuration of individual self-consumption.

International audience; This article addresses a new version of the biomedical sample transportation problem, as a vehicle routing problem with precedence constraints arising in the context of healthcare logistics, and proposes an iterated local search algorithm to solve it. This new version is more realistic and complex since it considers the collection centres’ opening hours and the moment at which they are visited as decision variables, granting additional flexibility to elaborate more efficient routes. Indeed, this problem is harder to model and to solve than its previous version because the constraint on the short samples’ lifetime leads to interdependency between successive pickups at each collection center. A metaheuristic is thus proposed to solve real-life instances. Numerical experiments confirm (1) the value of simultaneously planning routes, opening hours, and visit hours (which is new in the literature) and (2) the efficiency of the proposed algorithm to solve this problem.

International audience

The Puzzle-Based Storage (PBS) system is an innovative high-density storage system for physical goods, which has the advantage of gravity flow racks in terms of space efficiency and a relatively high accessibility to each unit-load of a variety of personalized goods stored. For the PBS system, among many intricate scheduling problems studied, the minimization of total number of item-moves when retrieving a single item with multiple escorts is an essential building block for its operation. To tackle this problem, we propose a hybrid approach that combines state appraisal, neighborhood search, and beam search. Our numerical experiments on a large number of benchmark instances show that, compared with the results of these instances provided by the best existing heuristic with computational complexity of O ( n 4 ) , our algorithm with different computational complexity settings can improve the overall average solution accuracy from 1.096% to 0.055% by its setting of O ( n 3 ) , or to 0.570% by its setting of O ( n ) , where n is the size of the PBS system. For PBS systems that are more in line with the actual storage density, our algorithm shows stronger robustness by improving the accuracy from 1.086% to 0.026% by its setting of O ( n 3 ) , or to 0.249% by its setting of O ( n ) . The significant improvement in efficiency and accuracy of our algorithm for this basic problem makes its industrial applications in PBS systems promising.

BackgroundMathematical infectious disease models available in literature, mostly take in their design that the parameters of basic reproduction number R0 and interval serial SI as constant values during tracking the outbreak cases. In this report a new intelligent model called HH-COVID-19 is proposed, with simple design and adaptive parameters.MethodsThe parameters R0 and SI are adapted by adding three new weighting factors α, β and γ and two free parameters σ1 and σ2 in function of time t, thus the HH-COVID-19 become time-variant model. The parameters R0, SI, α, β, γ, σ1 and σ2 are estimated optimally based on a recent algorithm of artificial intelligence (AI), inspired from nature called Harris Hawks Optimizer (HHO), using the data of the confirmed infected cases in Algeria country in the first t = 55 days.ResultsParameters estimated optimally: R0 = 1.341, SI = 5.991, α = 2.987, β = 1.566, γ = 4.998, σ1 = −0.133 and σ2 = 0.0324. R0 starts on 1.341 and ends to 2.677, and SI starts on 5.991 and ends to 6.692. The estimated results are identically to the actual infected incidence in Algeria, HH-COVID-19 proved its superiority in comparison study. HH-COVID-19 predicts that in 1 May, the infected cases exceed 50 000, during May, to reach quickly the herd immunity stage at beginning of July.ConclusionHH-COVID-19 can be used for tracking any COVID-19 outbreak cases around the world, just should updating its new parameters to fitting the area to be studied, especially when the population is directly vulnerable to COVID-19 infection.

Part VI - Meta-Heuristics and Optimization Techniques for Energy-Oriented Manufacturing Systems; International audience; This work presents an Order Acceptance Scheduling (OAS) problem with the introduction of energy consumption limits at each period of the planning horizon. The objective is to maximize total profit of accepted orders while considering energy usage over time, in accordance with imposed deadlines. A time-indexed formulation is presented to tackle the investigated problem.

International audience; This research focuses on an Order Acceptance Scheduling (OAS) problem on a single machine under time-of-use (TOU) tariffs and taxed carbon emissions periods with the objective to maximize total profit minus tardiness penalties and environmental costs. Due to the NP-hardness of the considered problem especially in presence of sequence-dependent setup-times, two fix-and-relax (FR) heuristics based on different time-indexed (TI) formulations are proposed. A metaheuristic based on the Dynamic Island Model (DIM) framework is also employed to tackle this optimization problem. These approached methods show promising results both in terms of solution quality and solving time compared to state-of-the-art exact solving approaches.

Part VIII - Modern Analytics and New AI-Based Smart Techniques for Replenishment and Production Planning Under Uncertainty; International audience; A stochastic inventory system with multiple products controlled by a periodic review joint replenishment policy P(s, Si)is considered. This system places a joint replenishment order to bring the inventory position of each item i to its order-up-to level Si when the aggregate reorder point of all items drops below s at each review moment. By imposing service levels on the system, we propose an algorithm for optimizing the policy to minimize the total cost of the system. The performance of this algorithm is evaluated by numerical experiments on randomly generated instances.

Part II - Hybrid Approaches for Production Planning and Scheduling; International audience; This study tackles an unrelated parallel machines rescheduling problem. Sequence and machine dependent setup times and limited resources are taken into consideration. The study focuses on the objective of proposing an efficient and stable rescheduling solution. The resolution approach is explained and illustrated. Different indicators to optimize the rescheduling planning are tested and results are analyzed. The problem is inspired from a concrete case of textile industry.

The three-dimensional Open Dimension Packing problem (3D-ODPP) is a real-world driven optimization problem that aims at the minimization of package volume in right-size packaging systems. The problem can be found in many industrial scenarios, such as e-commerce secondary packaging. The objective of the 3D-ODPP is to find out the length, width, and height of the cardboard box that can be used to pack a given set of or products so that the volume of the box is minimal. Many literature researches have focused on exact methods to deal with the 3D-ODPP. Despite the fact that the exact methods are capable of finding the global solution, their applications are very limited in terms of problem size and computational time because the 3D-ODPP is NP-hard in the strong sense. In addition, constructive and meta-heuristic methods for solving the 3D-ODPP have not been discussed frequently in the literature and remain a gap in the state-of-the-art.

In this paper we study the vehicle routing problem with zone-based pricing where the transporter defines a service price for all customers in a geographical zone. Each customer accepts or rejects the transporter’s offer according to a personal threshold value. Once the customer accepts the offer, the delivery service becomes compulsory. Two decisions need to be made: the price per zone and the routing to serve all customers that accept that price. The objective is to maximize the profit, which is the difference of total service revenue and the routing costs. We present a mathematical model for the problem and propose a branch-and-price algorithm. A numerical analysis shows promising results on the proposed set of instances.

At Universite de Technologie de Compiegne (UTC), complete course timetables are made available to students for the forthcoming semester, then students select their courses. A student cannot attend two events at the same time without violating a hard conflict constraint. Ideally, a conflict-free individual timetable will be created for each student, but it may be the case that there is no feasible solution for all students simultaneously. With thousands of students and hundreds of courses, UTC’s Student Scheduling Problem (SSP) cannot be processed manually. A heuristic designed decades ago can no longer be used, because too many students are left non-assigned and because the heuristic was not designed to deal with the current requirements. In this study we propose a preprocessing that reduces the size of instances, together with lower bounds that allow the quality of computed solutions to be assessed. Where most of the graphs relating to students’ hard conflict constraints are interval graphs, we show that a clique formulation of the hard conflict constraints may substantially reduce the number of equations in relation to other formulations. We propose integer linear programming (ILP) formulations to address the current requirements. We test an ILP with a global objective function and other ILPs within a lexicographic scheme. We investigate valid inequalities for reducing computation times. We report our computational experiments and results obtained on real instances from UTC. The solution method has proved its effectiveness and is now the tool used for student scheduling at UTC.

Order Acceptance Scheduling (OAS) is a two-fold decision problem that consists in the sequencing of a subset of selected orders with the objective of maximizing the total profit. In recent years, emphasis focused on the reduction of energy consumption and carbon emissions. Optimizing production schedules is a promising lever for this purpose. In this context, we provide a mathematical formulation for the OAS problem under energy aspects. To assess the performance of our model, a comparative analysis is conducted. Our results clearly point out that the arc-time-indexed model is better than the disjunctive one.

International audience; Transportation service procurement is often realized by an auction. With the rolling horizon planning concept adopted in logistics, carriers usually plan their transportation operations of several periods (days) in advance. This implies that carriers must consider multiple periods when participating in combinatorial auctions organized by shippers. Since transportation requests in future cannot be foreseen, carriers must consider request uncertainty in such auctions. In this paper, we consider a carrier's bid generation problem appeared in a multi-round combinatorial auction for truckload transportation service procurement with the consideration of multiple periods and request uncertainty. The problem is to maximize the total expected net profit of the carrier in a planning horizon of multiple periods by optimally determining the transportation requests to bid, the period to serve each request, and the routes to serve all requests including the carrier's reserved requests. This problem is hard to solve because of its stochastic nature. By adopting the scenario approach of stochastic optimization, a mixed integer linear programming model is formulated for the problem. A Benders decomposition approach is then proposed to solve the model, with Pareto-optimal cuts to accelerate its solution process. The performance of the approach is evaluated by numerical experiments on randomly generated instances. The computational results demonstrate that the Bender decomposition approach is much more efficient than CPLEX solver in solving large instances of the problem. In addition, the value of considering uncertain requests and multi-period in the bid generation is evaluated.

In this paper, an inventory replenishment planning problem in a two-echelon distribution system of Alibaba with a central distribution centre (CDC) and multiple front distribution centers (FDCs) is studied. In the system, the CDC replenishes its inventories of multiple products from external suppliers, while each FDC replenishes its inventories of the products from the CDC. These products are jointly replenished at each stock. This problem arises when Alibaba prepares its annual “double 11” promotion. Facing stochastic demands of the products in the promotion period, the objective of the problem is to optimally determine the inventory replenishment quantities of the products for each stock before the promotion so that the expected total profit of the system is maximized, subject to the storage capacity constraint of each stock. During the promotion, customer orders may be fulfilled through multiple channels, that is, a customer order may be fulfilled by its local FDC, other FDCs, or the CDC. This stochastic optimization problem is formulated as a convex nonlinear programming model and solved optimally by a piecewise linear approximation approach. The effectiveness of the model and the approach is demonstrated by numerical experiments on instances generated based on data of Alibaba. The results show that our model can lead to a significantly higher expected total profit compared with a deterministic planning model.

International audience

International audience

International audience; In this study, we consider the stochastic dynamic disassembly assembly routing problem with returns (2DARP-R), where the yields are uncertain due to the unknown situation of returned products. We introduce the formulation of the robust 2DARP-R defined by discrete scenarios. We address five hypotheses, where different levels of decision flexibility are given. Results show that allowing flexibility in the procurement process improves significantly the total cost with an average deviation equal to 11.24% for instances with 5 scenarios, 8.70% for instances with 10 scenarios and 7.06% for instances with 20 scenarios.

International audience

International audience; In this study, we consider a particular version of the hybrid flexible flow shop (HFFS) scheduling problem, inspired from a real-life application in a printing industry. The considered problem is a variation of the classical Flow shop problem, in which specific constraints are jointly considered, such as nonidentical parallel machines, sequence-dependent setups on machines, machine eligibility, and precedence constraints among jobs, in order to minimize the total tardiness time. After a problem description, a mathematical model, in form of mixed integer linear programming (MILP) model, that incorporates these aspects is developed and evaluated using ILOG Cplex software.

International audience; The buffer allocation problem (BAP) is one of the major optimization problems considered by production systems designers. The BAP is widely studied in the literature, since buffers have a great impact on improving the efficiency of production systems especially for mass production. Nevertheless, with all upcoming changes in world’s economy, industrial requirements and ecological awareness, the necessity of considering energy efficiency in manufacturing systems design becomes crucial. This paper introduces the first variant of energy-efficient buffer allocation problem (EE-BAP). It addresses a novel integrated analytical method to find the best buffer allocation that optimizes both throughput and energy consumption for serial production lines with unreliable machines. A large experimental study, based on different instances well established in the literature, is carried out to analyze the relevance of the proposed method. The obtained results show that considerable energy savings with low or almost no productivity loss are allowed. The energy-efficient BAP is a novel problem that considers the complex trade-off between throughput and energy efficiency performance and introduces new perspectives of research in the field of sustainable manufacturing systems design.

International audience; Around the world, the hospital world comes up against scaling and organization challenges in order to satisfy patient needs. These needs may often be urgent and require high quality and safety. This chapter discusses hospital logistics and the links it has with operational research. It finds complex systems, which are increasingly being studied by operational research, with the aim of bringing in optimized solutions while respecting a pre-established set of rules. The chapter takes into account the journeys made by good drivers and also consider the welfare of the drivers on these journeys. It also presents some optimization methods and techniques that are proposed and tested on real-life instances, as well as those taken from literature. The Champagne Sud hospitals are currently carrying out a review to improve the management of processes and procedures within their logistics network.

Disruptions in urban roads can significantly alter the quality of the transportation network by generating more congestion, gas emission, noise, stress, etc. In some situations, it can even break t...

International audience; In the last decades, the urban mobility has become a critical issue with several social, economic and ecological challenges. This is a consequence of the fast and unplanned cities growth and of the high population density in urban areas. In this context, we focus on the Disruption Scheduling problem on Urban Networks (DSUN) which consists in scheduling a set of planned disruptions in an urban road network while ensuring a path between all points of this network (strong connectivity in graph theory). Disruptions can break the urban network connection, requiring then to modify the routes direction (arcs reversals). Such situations may disturb the users’ habits. The goal of DSUN is (1) to minimize the number of arcs reversals and (2) the sum of the starting times to all disruptions simultaneously. DSUN is formalized in this study by means of a mathematical formulation. Moreover, since it is a bi-objective problem, we propose an exact algorithm based on the ϵ-constraint method. Computational experiments are performed on theoretical instances, as well as on realistic instances built from the road network map of Troyes city in France. The numerical results show that the exact algorithm can prove optimality for instances with up to 100 vertices and 20 disruptions.

International audience; China’s economy has experienced rapid development in the last several decades. However, environmental problems emerge along with the fast economic growth. Industrial pollutions and wastes have attracted much concern from the government and the public. In this paper, we propose a modified distance friction minimization model with undesirable outputs to evaluate the environmental efficiency of China’s regional industry from 2011 to 2015. We then provide benchmarking targets for inefficient regions. Our approach can not only analyze the internal factors of the poorly performing regions but also set the benchmarking targets in which the inefficient regions need the least effort to be efficient. The analysis results indicate that the developed regions perform better than the developing ones. In particular, the regions that have always been efficient from 2011 to 2015 are all developed ones to our knowledge. Moreover, the gap in environmental performance tends to extend between developed and developing areas every year. We suggest that the Chinese government should formulate and perfect relative regulations and laws on environmental protection and take measures to reduce the unbalanced trend of its regional industry.

International audience; Although economic order quantity (EOQ) problem is a widespread problem with many variants, few of them are dedicated to disassembly systems which brings new issues in inventory management due to their specificities. EOQ problem for disassembly systems consists in determining reorder time interval and/or order quantity of end-of-life products which must be disassembled to obtain their component. Three new models are proposed for the problem variant with stockout. The stockouts are considered as full backorders, full lost sales and partial backorders. For each problem the cost function is derived according to the assumptions and dedicated methods are proposed. These models show that it can be economically interested to allow stockout depending on the input parameters.

In this paper, we study a multi-period inventory model for a perishable product with both stochastic supply and demand in a rolling horizon framework. The product has a fixed shelf life such as fresh products, blood cells, chemicals, drugs and other pharmaceutical products. The objective is to minimize the expected total cost composed of ordering, purchasing, holding, shortage and waste costs. We focus on finding a high-quality solution close to the optimal solution of the model that provides decision support for decision-makers. We propose a stochastic programming model and transform it into MILP model based on conditional scenarios (CS) approach to reduce the computational burden. By comparing with the sample average approximation (SAA) method in a numerical study, we show that our method works efficiently.

International audience; Emergency departments (ED) face significant challenges to deliver high quality and timely patient care while facing rising costs. Prediction models in healthcare are becoming a trend allowing healthcare institutions to better manage the flow of patients through the services. In this work, a Generalized Stochastic Petri net (GSPN) simulation engine is used as a simulator (and performance evaluation) and a waiting time prediction model for the ED of Troyes hospital in France. Compared to existing literature, this work puts more emphasis on using features on GSPN (time, hierarchy, concurrency, parallelism) to capture the complex nature of the system. A new concept of real-time estimation of waiting time is presented. We validated the simulation model with a gap less than 10% between real and simulated performances. To the best of our knowledges, this work is the first to use a GSPN model as a management and waiting time’s forecasting tool for emergency departments.

International audience; This paper considers the problem of assigning nonpreemptive jobs on identical parallel machines to optimize workload balancing criteria. Since workload balancing is an important practical issue for services and production systems to ensure an efficient use of resources, different measures of performance have been considered in the scheduling literature to characterize this problem: maximum completion time, difference between maximum and minimum completion times and the Normalized Sum of Square for Workload Deviations. In this study, we propose a theoretical and computational analysis of these criteria. First, we prove that these criteria are equivalent in the case of identical jobs and in some particular cases. Then, we study the general version of the problem using jobs requiring different processing times and establish the theoretical relationship between the aforementioned criteria. Based on these theoretical developments, we propose new mathematical formulations to provide optimal solutions to some unsolved instances in order to enhance the latest benchmark presented in the literature.

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

In this paper, we focus on a problem that requires the location of recharging stations and the routingof electric vehicles in a goods distribution system. The goods are disseminated from a depot anddistributed to the customers via a heterogeneous fleet of electric vehicles with limited capacity.Differently from the classical vehicle routing problem, the vehicles have battery restrictions thatneed to be recharged at some stations if a trip is longer than their range. The problem reducesto finding the optimal locations of the recharging stations and their number to minimize the totalcost, which includes the routing cost, the recharging cost, and the fixed costs of opening stationsand operating vehicles. We propose a novel mathematical formulation and an efficient Bendersdecomposition algorithm embedded into a two-phase general framework to solve this environmentallogistics problem. Phase I solves a restricted problem to provide an upper bound for the originalproblem which is later solved in Phase II. Between the two phases, an intermediate processingprocedure is introduced to reduce the computations of the Phase II problem. This is achieved bya combination of the Phase I upper bound and several lower bounds obtained via exploiting theunderlying network structure. Our approach solves the problem in a general setting with nonidentical stations and vehicles by allowing multiple visits to the stations and partial recharging.The computational study provides both managerial and methodological insights.Keywords: Recharging Station Location, Electric Vehicle Routing, Environmental Logistics,Integer Programming, Benders Decomposition

International audience