A Large Neighborhood Search heuristic for Supply Chain Network Design

24 pagesMany exact or approximate solution techniques have been used to solve facility location problems and more generally supply chain network design problems. Yet, the Large Neighborhood Search technique (LNS) has almost never been proposed for solving such problems, although it has proven its efficiency and flexibility in solving other complex combinatorial optimization problems. In this paper we propose an LNS framework for solving a four-layer single period multi-product supply chain network design problem involving multimodal transport. Location decisions for intermediate facilities (e.g. plants and distribution centers) are made using the LNS while transportation modes and product flow decisions are determined by a greedy heuristic. As a post-optimization step, we also use linear programming to determine the optimal product flows once the logistics network is fixed. Extensive experiments based on generated instances of different sizes and characteristics show the effectiveness of the method compared with a state-of-the-art solver

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Scheduling maintenance routing for an offshore wind farm is a challenging and complex task. The problem is to find the best routes for the Crew Transfer Vessels to maintain the turbines in order to minimise the total cost. This paper primarily proposes an efficient solution method to solve the deterministic maintenance routing problem in an offshore wind farm. The proposed solution method is based on the Large Neighbourhood Search metaheuristic. The efficiency of the proposed metaheuristic is validated against state of the art algorithms. The results obtained from the computational experiments validate the effectiveness of the proposed method. In addition, as the maintenance activities are affected by uncertain conditions, a simulation-based optimisation algorithm is developed to tackle these uncertainties. This algorithm benefits from the fast computational time and solution quality of the proposed metaheuristic, combined with Monte Carlo simulation. The uncertain factors considered include the travel time for a vessel to visit turbines, the required time to maintain a turbine, and the transfer time for technicians and equipment to a turbine. Moreover, the proposed simulation-based optimisation algorithm is devised to tackle unpredictable broken-down turbines. The performance of this algorithm is evaluated using a case study based on a reference wind farm scenario developed in the EU FP7 LEANWIND project

Modèles génériques et algorithmes d’optimisation pour la conception des chaînes logistiques durables

This thesis focuses on the development of mathematical models and optimization algorithms for the design of sustainable supply chains. We propose single-period, multi-commodity, multi-mode, four level models (suppliers, production facilities, warehouses and customers) covering economic and environmental pillars of sustainable development. The decision variables are related to the location of the intermediate logistics sites (production units and warehouses), the choice of technology and mode of transport, and the determination of product flow. A first model is based solely on minimizing total costs. This model is extended to bi-objective minimization by considering CO2 emissions. We propose an optimization procedure based on the Large Neighborhood Search (LNS) metaheuristic, which had almost never been applied to problems with mixed variables such as design supply chain. Our extension to the bi-objective case involves the use of the multi-directional local search (MDLS). Extensive numerical experiments assess the relevance of our model and compare the performance of our algorithms to those of a state-of-the-art solver.Cette thèse porte sur le développement de modèles mathématiques et d’algorithmes d’optimisation pour la conception de chaînes logistiques durables. Nous proposons des modèles mono-périodiques, multi-produits et multi-modes de transport à quatre niveaux (fournisseurs, unités de production, entrepôts et clients) couvrant les piliers économique et environnemental du développement durable. Les variables de décision concernent la localisation des sites logistiques intermédiaires (unités de production et entrepôts), les choix de technologie et de mode de transport, et la détermination des flux de produits. Un premier modèle est basé uniquement sur la minimisation des coûts totaux. Ce modèle est étendu au cas bi-objectif en considérant la minimisation des émissions de CO2. Nous proposons une procédure d’optimisation basée sur la recherche à voisinage large (LNS : Large Neighborhood Search). L’application de cette méthode à un problème à variables mixtes tel que la conception de chaîne logistique est inédite. Notre extension au cas bi-objectif fait intervenir l’algorithme récent de recherche locale multi-directionnelle. Les expérimentations numériques permettent d’évaluer la pertinence de nos modèles et de comparer les performances de nos algorithmes à celles d’un solveur du marché

A model and bi-objective solution technique for green sustainable supply chain network design

International audienc

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

International audienceThis paper investigates the inventory control of perishable products with a limited storage lifetime in closed-loop supply chains. Uncertainties related to customers' demands, the return rate of goods, and qualities of the returned products are considered. An efficient interactive response surface methodology is adopted by using a statistical simulation approach. The desirability function is taken into account a minimum desirability level of multiple objectives, the potential correlation between the considered objectives, and minimisation of uncontrollable variables or noise factors effect. The experimental results indicate the efficiency of the proposed simulation-based optimisation approach in handling correlated multiple objectives for inventory control of perishable products in closed-loop supply chains under uncertainty. Considered risks in the supply chain echelons and fair costs allocation has been balanced efficiently via using the proposed interactive approach to solving the multi-objective problem. Finally, the robustness of the obtained solutions is assessed under variation in weights of the response variables

A large neighborhood search heuristic for supply chain network design

International audienceMany exact and approximate solution techniques have been used to solve facility location problems and, more generally, supply chain network design problems. Yet, the Large Neighborhood Search technique (LNS) has almost never been suggested for solving such problems, although it has proven its efficiency and flexibility in solving other complex combinatorial optimization problems. In this paper, we propose an LNS framework for solving a four-layer single period multi-product supply chain network design problem. One important feature of the model is that it includes inter-modality: the itinerary followed by the cargo from origin to destination may take several transportation modes. Moreover, several modes may compete on some arcs. Location decisions for intermediate facilities (e.g. plants and distribution centers) are determined by the LNS while transportation modes and product flow decisions are determined by a greedy heuristic. As a post-optimization step, linear programming is used to optimize product flows once the structure of the logistics network is fixed. Extensive experiments, based on randomly generated instances of different sizes and characteristics, show the effectiveness of the method compared with a state-of-the-art solver

A Large Neighborhood Search based heuristic for Supply Chain Network Design

10 pagesInternational audienceFacility location problems and more generally supply chain design models have been the subject of a large number of models and exact or approximate solution techniques. Yet, the Large Neighborhood Search technique (LNS) has almost never been proposed for solving such problems, although it has proven its efficiency and flexibility to solve complex combinatorial optimization problems. In this paper we propose a new solution framework for solving a four-layer single period multi-product supply chain network design problem involving multimodal transport. Location decisions for intermediate facilities (e.g. plants and distribution centers) are made using LNS while transportation modes and product flow decisions are determined by a greedy heuristic. Finally, an a posteriori flow optimization dual simplex procedure is used to finalize the solution. Extensive experiments based on randomly generated instances of different sizes and characteristics show the effectiveness of the method compared with a state-of-the-art solver. Further research aims at extending the model and solution technique to encompass other advanced supply chain design features

Multi-Directional Local Search for Sustainable Supply Chain Network Design

International audienceIn this paper, we propose a bi-objective MILP formulation to minimize logistics costs as well as CO 2 emissions in a supply chain network design problem with multiple layers of facilities, technology levels and transportation mode decisions. The proposed model aims at investigating the trade-off between cost and CO 2 emissions through supply chain activities (i.e., raw material supply, manufacturing, warehousing, and transportation). To this end, a multi-directional local search (MDLS) metaheuristic is developed. The proposed method provides a limited set of non-dominated solutions ranging from a purely cost effective solution to a purely environmentally effective one. Each iteration of the MDLS consists in performing local searches from all non-dominated solutions. To do so, a Large Neighborhood Search (LNS) algorihtm is used. Extensive experiments based on randomly generated instances of various sizes and features are described. Three classic performance measures are used to compare the set of non-dominated solutions obtained by the MDLS algorithm and by directly solving the MILP model with the epsilon-constraint approach. This paper is concluded by managerial insights about the impact of using greener technology on the supply chain * Corresponding author. Olivier Péton, IMT Atlantique

Multi-directional local search for a sustainable supply chain network design model

International audience1. The problem consideredThe increasing importance of environmental issues has prompted decisionmakersto incorporate environmental factors into supply chain networkdesign (SCND) models. We propose a bi-objective SCND model to minimizetwo conflicting objectives: the total cost and the environmental impactexpressed by CO2 emissions.The logistics network consists of four layers: suppliers, plants, distributioncenters (DCs) and customers. The model considers several possibletransportations modes in the network, each transportation mode havinga lower and upper capacity limitation. Moreover, we consider differentcandidate technology levels at the plants and DCs. Each technology representsa type of service with associated fixed and variable costs and CO2emissions. A higher-level technology may reduce carbon emissions, but islikely to require more investment cost.The model considers CO2 emissions caused by all industrial and logisticsoperations as well as transportation. The main issues to be addressedin the sustainable SCND model includes determining the number, location,and technology level at plants and DCs, suitable transportation mode, andproduct flows between facilities.2. Solution methodWe solve the corresponding bi-objective mixed integer linear programmingmodel with the multi-directional local search (MDLS) framework. The efficiency of this recent framework has been proved on the multiobjectiveknapsack, set packing and orienteering problems, but to the bestof our knowledge, this is the first attempt to solve a facility location problemwith it. The MDLS is based on the principle of separately using independentsingle-objective local searches to iteratively improve the Pareto setapproximation. The motivation for using this framework is the capabilityof using already implemented single objective optimization components.In our case, we use a large neighborhood search algorithm as single objectivemethod. Our algorithm can be decomposed in the three followingsteps:Phase 1: look for an initial Pareto set approximation. The initialphase of the single objective LNS is executed separately for each objective.The output is an initial Pareto set approximation.Phase 2: Intensification around the Pareto set approximation. ThePareto set approximation is improved by exploring the neighborhoodof all the solutions in this set with a Multi-directional local search.Phase 3: optimization of product flows. After stabilizing the locationand transportation mode decisions for all Pareto set approximationsolutions in phase 2, we determine the optimal product flows by applyingthe Simplex algorithm to all solutions in the set.3. Computational resultsWe assess the performance of our approach through a comparison withthe well-known "-constraint method. In particular, we analyze the Paretofronts given by both solutions on a set of 60 generated instances and showthat the efficiency of our approach improves when the instance size grows

Evaluation of the satisfaction level experienced by tourists visiting Gorgan

Climatic and geographical features of Gorgan (e.g. lying in the vicinity of North - and Razavi-khorasan, Semnan, Tehran, and Mazandaran provinces) on the one hand and its cultural and historical attractions (e.g. Existence of Turkman people and their handicrafts) on the other, have rendered this city into a prominent tourist spot. This research, aims to evaluate the Gorgan - tourists’ satisfaction level and the principal factors affecting it. After distribution of 250 questionnaires, data were analyzed through descriptive and analytical statistics methods. Results indicate that four factors including service quality, environmental quality, host behavior quality, and services cost, can explain more than %54 of the variance of the variables. Also, a significant correlation is observed between the mentioned factors and the tourists’ satisfaction level. At the end, some suggestions are proposed for improving tourists’ satisfaction and tourism development in Gorgan