A Genetic Algorithm for the Parallel Machine Scheduling Problem with Consumable Resources

Belkaid, Fayçal; Sari, Zaki; Souier, Mehdi

doi:10.4018/jamc.2013040102

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…They demonstrate that when the amount of consumable resources available at time zero is sufficient, then the problem becomes equivalent to a parallel machine scheduling problem with release dates and sequence dependent setup times; and as objective the total weighted tardiness. Belkaid et al (2013a) investigate a scheduling problem on identical parallel machines to minimize the makespan. Each job depends on the amount of consumed resources and is characterized by different resource requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Approach for the Reentrant Parallel Machines Scheduling Problem under Consumable Resources Constraints

Belkaid

Yalaoui

Sari

2016

International Journal of Information Systems and Supply Chain Management

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In present manufacturing environment, the reentrant scheduling problem is one of the most important issues in the planning and operation of production systems. It has a large scope such as capacity distribution and inventory control. On the other hand, the markets are very competitive; it is a critical requirement of operational management to have effective management of resources (consumable and renewable) so as to achieve optimal production plan. This study considers a reentrant parallel machines scheduling problem with consumable resources. Each job consumes several components and must be processed more than once in a stage composed of identical parallel machines. The resources availability, jobs assignment and sequencing at each cycle and are considered and optimized simultaneously. On the basis of this representation, a MILP model is developed. Thus, that MILP model can be used for the problem in order to find the exact solution. Since, this problem is clearly NP-hard, and optimal solutions for large instances are highly intractable, a genetic algorithm is developed to obtain near-optimal solution. Then, an improvement phase based on different local search procedures are performed and examined to generate better solutions. The system performances are assessed in terms of measures such as the solution quality and the execution time. The effectiveness of the proposed metaheuristic is examined based on comparative study. The simulation results demonstrate that the presented algorithm is able to find an optimal solution for small-sized problems and can effectively find a near optimal solution for large-sized problems to minimize the makespan of the considered problem.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Approach for the Reentrant Parallel Machines Scheduling Problem under Consumable Resources Constraints

Belkaid

Yalaoui

Sari

2016

International Journal of Information Systems and Supply Chain Management

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“…scheduling problem applied to a flow shop. For their part, Belkaid et al (2013) proposed a genetic algorithm for the parallel machine-scheduling problem.…”

Section: Optimization Of the Pmppmentioning

confidence: 99%

Comparative Analysis of Heuristic Algorithms Used for Solving a Production and Maintenance Planning Problem (PMPP)

Guiras¹,

Hajej²,

Rezg³

et al. 2018

Applied Sciences

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Abstract:In this work, we develop methods to assess the risk of profit-loss resulting from the choice of a computational method for solving a joint production and maintenance-planning problem. In fact, the optimal objective function is calculated via the use of algorithms and optimization methods. The use of these methods can have an impact on an event that can disrupt the optimal production and maintenance plan. To achieve our goals, we start with calculating the manufacturing system's joint production and maintenance plans over a finite horizon using different methods. In the second part of the work, we propose analytical models to quantify the risk of profit-loss resulting from product returns and the integration of an imperfect maintenance policy. Numerical examples are conducted by adopting the different algorithms used. This study provides insights into the most efficient computational methods for the encountered problems. This research proposes new approaches to help and guide managers in the analysis and evaluation of their decisions.

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“…Benbouzid-Sitayeb et al (2006) proposed a Joint Genetic Algorithm (JGA), for joint production and maintenance scheduling problem in permutation flowshop, in which different genetic joint operators (mutation, selection and crossover ) are used and proposed a joint structure to represent an individual in with two fields: the first one for production data and the second one for maintenance data. Belkaid et al (2013) proposed a metaheuristics based on genetic algorithm to solve hard combinatorial optimization problems and constituted an important alternative resolution for parallel machine scheduling problems. To evaluate the efficiency of the genetic algorithm and proposed heuristics, some performance measures composed of a set of computational experiments are applied.…”

Section: Introductionmentioning

confidence: 99%

Modelling, Computation and Optimization in Information Systems and Management Sciences

Thi

Tao

Nguyên

2015

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing.The publications within "Advances in Intelligent Systems and Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Advisory BoardChairman Nikhil R. Pal,

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A Genetic Algorithm for the Parallel Machine Scheduling Problem with Consumable Resources

Cited by 11 publications

References 33 publications

An Efficient Approach for the Reentrant Parallel Machines Scheduling Problem under Consumable Resources Constraints

An Efficient Approach for the Reentrant Parallel Machines Scheduling Problem under Consumable Resources Constraints

Comparative Analysis of Heuristic Algorithms Used for Solving a Production and Maintenance Planning Problem (PMPP)

Modelling, Computation and Optimization in Information Systems and Management Sciences

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