A bi-objective robust inspection planning model in a multi-stage serial production system

International audienceIn this paper, a bi-objective mixed-integer linear programming (BOMILP) model for planning of an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system is presented. The model involves two inter-related decisions: 1) which quality characteristics need what kind of inspections (i.e., which-what decision) and 2) when the inspection of these characteristics should be performed (i.e., when decision). These decisions require a trade-off between the cost of manufacturing (i.e., production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in the manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e., production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France

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Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

A bi-objective robust inspection planning model in a multi-stage serial production system

In this paper, a bi-objective mixed-integer linear programming (BOMILP) model for planning of an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system is presented. The model involves two inter-related decisions: 1) which quality characteristics need what kind of inspections (i.e., which-what decision) and 2) when the inspection of these characteristics should be performed (i.e., when decision). These decisions require a trade-off between the cost of manufacturing (i.e., production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in the manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e., production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France

Environnement de programmation générique pour la recherche locale : Metalab

Analyse informatique de la recherche locale -- Méthodes de recherche locale -- Principes de la recherche locale -- Les formes heuristiques en recherche locale -- Quelques algorithmes représentatifs -- Portée et limites du paradigme -- Langages de recherche locale -- Les différents styles de programmation -- Réalisabilité d'un atelier de programmation -- Les bibliothèques -- Les cadres d'application -- Les langages déclaratifs -- Modélisation de la recherche locale -- Conception générique de la recherche locale -- Formalisme et méthodologie -- Principes de modélisation -- Découplage des parties logicielles -- Modulation des canaux de programme -- Composition des variables -- Interprétation dynamique du modèle -- Objectifs -- Usage des diagrammes de composition -- Phase d'initialisation -- Phase d'énumération -- Phase de transition -- Études expérimentales -- Présentation du logiciel METALAB -- Concept de problème -- Concept de solution -- Concept de caractéristique -- Concept de mouvement -- Concept d'attribut -- Concept de serveur -- Concept d'explorateur -- Finalisation de l'algorithme -- Revue de problèmes -- Le PVC avec coûts d'arêtes dépendant du temps -- Le problème de la p-médiane -- Le problème de coloration de graphe -- Le problème d'affectation quadratique -- Étude de cas, méthodes taboues pour le PVC -- Contexte -- Incrémentation du voisinage 2-opt -- Segmentation du voisinage 2-opt -- Techniques d'anticipation

Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics

Metaheuristic approach for solving one class of optimization problems in transport

Problem dodele vezova obuhvata nekoliko važnih odluka koje je potrebno doneti da bi se dosegla maksimalna efikasnost luke. U luci, menadžeri terminala treba da dodele slobodne vezove brodovima koji su najavili dolazak...Berth Allocation Problem incorporates some of the most important decisions that have to be made in order to achieve maximum eciency in a port. Terminal manager of a port has to assign incoming vessels to the available berths, where they will be loaded/unloaded in such a way that some objective function is optimized. It is well known that even the simpler variants of Berth Allocation Problem are NP-hard, and thus, metaheuristic approaches are more convenient than exact methods, because they provide high quality solutions in reasonable computational time. This study considers two variants of the Berth Allocation Problem: Minimum Cost Hybrid Berth AllocationProblem (MCHBAP) and Dynamic Minimum Cost Hybrid Berth AllocationProblem (DMCHBAP), both with xed handling times of vessels. Objective function to be minimized consists of the following components: costs of positioning, speeding up or waiting of vessels, and tardiness of completion for all vessels. Having in mind that the speed of nding high-quality solutions is of crucial importance for designing an ecient and reliable decision support system in container terminal, metaheuristic methods represent the natural choice when dealing with MCHBAP and DMCHBAP. This study examines the following metaheuristic approaches for both types of a given problem: two variants of the Bee Colony Optimization (BCO), two variants of the Evolutionary Algorithm (EA), and four variants of Variable Neighborhood Search (VNS). All metaheuristics are evaluated and compared against each other and against exact methods integrated in commercial CPLEX solver on real-life instances from the literature and randomly generated instances of higher dimensions. The analysis of the obtained results shows that on real-life instances all metaheuristics were able to nd optimal solutions in short execution times. Randomly generated instances were out of reach for exact solver due to time or memory limits, while metaheuristics easily provided high-quality solutions in short CPU time in each run. The conducted computational analysis indicates that metaheuristics represent a promising approach for MCHBAP and similar problems in maritime transportation..

Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics