The StockingCost Constraint

Houndji, Vinasétan Ratheil; Schaus, Pierre; Wolsey, Laurence A.; Deville, Yves

doi:10.1007/978-3-319-10428-7_29

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Table 1 compiles the experiment's results on the instances from the CSPlib repository. For each instance (represented as Instance NI-NT), we note the optimal solution, the time spent by the CP algorithm [29] to reach it, the best solution found by our approach over ten runs, and the corresponding time, along with the gap between the global optimum and the best solution, the coefficient of variation of the solutions and the meantime of the search.…”

Section: Resultsmentioning

confidence: 99%

“…This allowed them to effectively reduce the number of variables and constraints in the MIP models. Houndji et al [20] introduced a new global constraint they named StockingCost to solve the PSP with Constraint Programming. They tested it on new instances and published it on CSPlib (Gent and Walsh [8]).…”

Section: Discrete Lot Sizing and Scheduling Problems (Dlsp)mentioning

confidence: 99%

“…Several studies addressed the Pigment Sequencing Problem -PSP (see, for example, [20,23,32]). It can be described as a problem that requires producing various items on one machine with predefined setup costs.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Different types of lot-sizing problems have been studied in the literature. In recent years, researchers such as Houndji et al [20] and Ceschia et al [24] have focused on an NP-Hard variant known as the Pigment Sequencing Problem. This problem is included in the CSPlib repository [8] and involves producing multiple items on a single machine with limited capacity (one item per period).…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithms for Solving the Pigment Sequencing Problem

Houndji,

Gna

2024

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Submission to Episciences Lot sizing is important in production planning. It consists of determining a production plan that meets the orders and other constraints while minimizing the production cost. Here, we consider a Discrete Lot Sizing and Scheduling Problem (DLSP), specifically the Pigment Sequencing Problem (PSP). We have developed a solution that uses Genetic Algorithms to tackle the PSP. Our approach introduces adaptive techniques for each step of the genetic algorithm, including initialization, selection, crossover, and mutation. We conducted a series of experiments to assess the performance of our approach across some multiple trials using publicly available instances of the PSP. Our experimental results demonstrate that Genetic Algorithms are practical and effective approaches for solving DLSP. Le dimensionnement de lots tient une place importante en planification de production en industrie. Il consiste à trouver un plan de production qui à la fois satisfait les demandes et autres contraintes tout en minimisant les coûts de production. Dans cet article, nous étudions une instance de problèmes de dimension discret (DLSP), le Pigment Sequencing Problem (PSP). Nous avons développé une approche basée sur les algorithmes génétiques afin de le résoudre. Notre approche propose des méthodes adaptatives pour chacune des étapes des algorithmes génétiques que sont l'initialisation, la sélection, le croisement et la mutation. Les expériences menées nous ont permis d'évaluer la performance de cette approche sur des instances en accès publique de PSP. Les résultats obtenus montrent que les algorithmes génétiques constituent une approche intéressante et effective dans la résolution des DLSP.

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Section: Resultsmentioning

confidence: 99%

Section: Discrete Lot Sizing and Scheduling Problems (Dlsp)mentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithms for Solving the Pigment Sequencing Problem

Houndji,

Gna

2024

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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“…The CP literature is very limited in the field of lot-sizing problems. To the best of our knowledge, [17] is the only paper to study a lotsizing-related global constraint. They consider a production planning problem in which a set of items has to be produced before their production deadline on a limited capacity machine, with the objective of minimizing stocking costs.…”

Section: Introductionmentioning

confidence: 99%

A global constraint for the capacitated single-item lot-sizing problem

German,

Cambazard,

Gayon

et al. 2019

Preprint

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The goal of this paper is to set a constraint programming framework to solve lot-sizing problems. More specifically, we consider a single-item lot-sizing problem with time-varying lower and upper bounds for production and inventory. The cost structure includes time-varying holding costs, unitary production costs and setup costs. We establish a new lower bound for this problem by using a subtle time decomposition. We formulate this NP-hard problem as a global constraint and show that bound consistency can be achieved in pseudo-polynomial time and when not including the costs, in polynomial time. We develop filtering rules based on existing dynamic programming algorithms, exploiting the above mentioned time decomposition for difficult instances. In a numerical study, we compare several formulations of the problem: mixed integer linear programming, constraint programming and dynamic programming. We show that our global constraint is able to find solutions, unlike the decomposed constraint programming model and that constraint programming can be competitive, in particular when adding combinatorial side constraints.

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