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Abstract

In this paper, we propose an exact solution method for the Windy Rural Postman Problem (WRPP). The motivation to study this problem comes from some real-life applications, such as garbage collecting in a predefined sector with hills, where the traversing or the servicing speed can change following the direction. We present a Dantzig-Wolfe decomposition and a branch-and-price algorithm to solve the WRPP. To the best of our knowledge, Dantzig-Wolfe decomposition has never been used to solve that problem. The numerical results show that optimal solutions are found in a very reasonable amount of time on instances with up to 100 nodes and 180 edges.

Abstract

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a Constraint Satisfaction Problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.

Résumé

Cet article concerne la résolution d'un problème d'ordonnancement de type flowshop hybride avec tâches multiprocesseurs. Le critère à optimiser est le makespan. Nous proposons une méthode de recherche arborescente à base de divergences pour le résoudre. La méthode est évaluée sur des jeux-tests en mesurant son pouvoir de résolution exacte ou l'écart à une borne. Une étude comparative est également menée avec d'autres méthodes de la littérature. Les résultats obtenus prouvent l'efficacité de notre approche notamment sur les grandes instances.

Abstract

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems.

Résumé

Cet article concerne la résolution d'un problème d'ordonnancement de type flowshop hybride avec tâches multiprocesseurs. Le critère à optimiser est le makespan. Nous proposons une méthode de recherche arborescente à base de divergences pour le résoudre. La méthode est évaluée sur des jeux-tests en mesurant son pouvoir de résolution exacte ou l'écart à une borne. Une étude comparative est également menée avec d'autres méthodes de la littérature. Les résultats obtenus prouvent l'efficacité de notre approche notamment sur les grandes instances.

Abstract

This paper investigates how to adapt a discrepancy-based search method to solve two-stage hybrid flowshop scheduling problems in which each stage consists of several identical machines operating in parallel. The objective is to determine a schedule that minimizes the makespan. We present an adaptation of the Climbing Depth-bounded Discrepancy Search (CDDS) method based on Johnson's rule and on dedicated lower bounds for the two-stage hybrid flow shop problem. We report the results of extensive computational experiments, which show that the proposed adaptation of the CDDS method solves instances in restrained CPU time and with high quality of makespan.

Abstract

In this paper we make a comparative study of several mixed integer linear programming (MILP) formulations for resource-constrained project scheduling problems (RCPSPs). First, we present three discrete and continuous time MILP formulations issued from the literature. Second, instead of relying on the traditional discretization of the time horizon, we propose two original MILP formulations for the RCPSP based on the concept of event : the Start/End formulation and the On/Off formulation. These formulations present the advantage of involving fewer variables than the formulations indexed by time. Because the variables of this type of formulations are not function of the time horizon, we have a better capacity to deal with instances of very large scheduling horizon. We also illustrate our contribution with a series of tests on various types of instances with the three MILP formulations issued from the literature together with our two new formulations, and we draw some conclusions on their use.

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