Minimizing Total Tardiness in the m-Machine Flow-Shop Problem by Heuristic Algorithms

In this work the m-machine permutation flow-shop problem has been considered. The permutation flow-shop scheduling problem where a set of jobs have to be scheduled on a set of machines in the same order. We propose  heuristic algorithms for the flow-shop problem to minimizing the total tardiness. A new genetic and Tabu search algorithm which initialized by the solution of EDD, NEH and EN algorithm. Computational experiments are performed on benchmark instances and the results show the good performances of these methods. Finally, some future research directions are given

Integrated Flow Shop and Vehicle Routing Problem Based on Tabu Search Algorithm

We consider in this paper a m-machine permutation flow shop scheduling problem and vehicle routing problem (VRP) integrated [1]. The manufacturing workshop is a flow shop and there is only one vehicle available. We are interested in the minimization of the total tardiness. related to the delivery completion times. Therefore, we are faced to three interdependent problems: scheduling the jobs in the flow shop environment, batching the completed jobs, and routing the batches. Then, we present the resolution method based on Tabu search and the results that have been obtained. Computational experiments are performed on random data sets and show the efficiency of the methods. Finally, a conclusion and some future research directions are proposed

Algorithmes métaheuristiques pour minimiser la somme des retards des problèmes d'ordonnancement de type flowshop

Nous considérons dans cette thèse un problème d’ordonnancement de flow-shop de permutation où un ensemble de travaux doit être ordonnancé sur un ensemble de machines. Les travaux doivent être ordonnancés sur les machines dans le même ordre. L’objectif est de minimiser le retard total. Nous proposons des algorithmes heuristiques et des nouvelles matheuristiques pour ce problème. Les matheuristiques sont un nouveau type d’algorithmes approchés qui ont été proposés pour résoudre des problèmes d’optimisation combinatoire. Les méthodes importent de la résolution exacte au sein des approches (méta) heuristiques. Ce type de méthode de résolution a reçu un grand intérêt en raison de leurs très bonnes performances pour résoudre des problèmes difficiles. Nous présentons d’abord les concepts de base d’un problème d’ordonnancement. Nous donnons aussi une brève introduction à la théorie de l’ordonnancement et nous présentons un panel de méthodes de résolution. Enfin, nous considérons un problème où un flow shop de permutation à m-machine et un problème de tournées de véhicules sont intégrés, avec pour objectif la minimisation de la somme des retards. Nous proposons un codage direct d’une solution et une méthode de voisinage. Les résultats montrent que l’algorithme Tabou améliore grandement la solution initiale donnée par EDD et où chaque voyage ne délivre qu’un travail.We consider in this thesis a permutation flow shop scheduling problem where a set of jobs have to be scheduled on a set of machines. The jobs have to be processed on the machines in the same order. The objective is to minimize the total tardiness. We propose heuristic algorithms and many new matheuristic algorithms for this problem. The matheuristic methods are a new type of approximated algorithms that have been proposed for solving combinatorial optimization problems. These methods embed exact resolution into (meta)heuristic approaches. This type of resolution method has received a great interest because of their very good performances for solving some difficult problems. We present the basic concepts and components of a scheduling problem and the aspects related to these components. We also give a brief introduction to the theory of scheduling and present an overview of resolution methods. Finally, we consider a problem where m-machine permutation flow shop scheduling problem and a vehicle routing problem are integrated and the objective is to minimize the total tardiness. We introduce a direct coding for a complete solution and a Tabu search for finding a sequence and trips. The results show that the TS greatly improves the initial solution given by EDD heuristic where each trip serves only one job at a time

Tabu search algorithms to minimize the total tardiness in a flow shop production and outbound distribution scheduling problem

International audience—In this paper, we consider a production and out-bound distribution scheduling problem, coming from a real life problem in a chemotherapy production center. Only one vehicle with infinite capacity is available for delivery. The production workshop is an m-machine flow shop. To each job is associated a processing time per machine, a location site and a delivery due date. The travel times are known. The problem is to define a production schedule, batches of jobs, and delivery routes for each batch, so that the sum of tardiness is minimized. Heuristic algorithms are proposed and evaluated on random data sets

An hybrid metaheuristic, an hybrid lower bound and a Tabu search for the two-machine flowshop total tardiness problem

International audienceThe classical F2∥ΣTj problem is considered. A Tabu search and a matheuristic algorithm are proposed as upper bounds and a lower bound based on a partial relaxation of the MILP model is proposed. All these methods have been extensively tested on randomly generated instances. The results show the performances of the Tabu search algorithm. They also show the good performances of the matheuristic and of the lower bound, preferably for small instances (up to 100 jobs)

Matheuristic algorithms for minimizing total tardiness in the m-machine flow-shop scheduling problem

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Tabu search and matheuristic algorithms for solving an integrated flow shop and vehicle routing problem

International audienceWe consider in this paper an integrated scheduling and vehicle routing problem: jobs have to be produced and then transported to a customer place for a given due date. The manufacturing workshop is a flow shop and there is only one vehicle available. We are interested in the minimization of the total tardiness, related to the delivery completion times. Therefore, we are faced to three interdependent problems: scheduling the jobs in the flow shop environment, batching the completed jobs, and routing the batches. We propose two tabu search algorithms and a matheuristic algorithm. Computational experiments are performed on random data sets and show the efficiency of the methods

Recovering beam search and matheuristic algorithms for the F2||S Tj scheduling problem

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Resolution of the F2||Sum Tj scheduling problem by genetic algorithm and matheuristic

International audienceWe consider in this paper the two-machine flow-shop scheduling problem with total tardiness minimization. We propose a genetic algorithm and a matheuristic algorithm. Some computational experiments are described and the results show that these methods outperform other existing methods based on truncated branch-and-bound algorithms. Some future research directions are given, both in terms of evaluation and of resolution method

Minimisation de la somme des retards pour un problème d'ordonnancement de type flowshop à deux machines et un problème de livraison intégrés

International audienceOn considère un problème intégré d'ordonnancement de type flowshop à deux machines et un problème de livraison de type multitrip VRP. A chaque tâche est associée une durée sur chaque machine, une destination (une destination par tâche) et une date de fin de livraison souhaitée. Un unique véhicule assure les livraisons entre les sites. Le problème consiste à ordonnancer les tâches sur les machines, constituer des lots de tâches et les amener à destination. Nous présentons le codage d'une solution complète et proposons des méthodes de voisinage, validées par des expérimentations numériques. Ce travail bénéficie d'une aide de l'Agence National de la Recherche pour le projet Athena, portant la référence ANR-13-BS02-0006-01.</p