A new hybrid island model genetic algorithm for job shop scheduling problem

Kurdi, Mohamed Zakaria

doi:10.1016/j.cie.2015.07.015

Cited by 62 publications

(41 citation statements)

References 50 publications

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“…The suggested model exploits a selfadaptation phase strategy for maintaining an optimal balance between diversification and intensification of the search process. The suggested self-adaptation phase strategy selects the optimal individuals based on the local search using tabu search (Kurdi, 2015).…”

Section: Job Shop Schedulingmentioning

confidence: 99%

Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

Kalshetty¹,

Adamuthe²,

Kumar³

2020

JSR

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Job scheduling is one of the key activities performed in industries for manufacturing planning. In job scheduling, each job that contains various operations is allocated to one of the available machines for processing. Each job has a duration and each machine can handle only one operation at a time. An efficient allocation of jobs is mandatory for decreasing the makespan and idle time of the machines. In Job Shop Scheduling (JSS), the operations of the jobs are ordered. Genetic algorithm (GA) is a popular heuristic algorithm investigated to solve different scheduling problems. This paper presents feasibility preserving solution representation, initialization and operators for solving job shop scheduling problem. Proposed GA obtained best known results with good success rate for Lawrence (1984) datasets. Experiments show fast convergence of GA towards best solution. Hybridization of GA with local search or repair operator is required to obtain best solution with better success rate.Index Terms: Genetic algorithm, generalized order crossover, job shop scheduling problem, scheduling problem.

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Section: Job Shop Schedulingmentioning

confidence: 99%

Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

Kalshetty¹,

Adamuthe²,

Kumar³

2020

JSR

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“…Wang and Tang [31] presented an improved adaptive GA for solving the minimum makespan problem of production planning, which was inspired by hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability were designed. Kurdi [32] proposed a new hybrid island model GA to solve the production planning problem with the objective of makespan minimization, and a new naturally inspired selfadaptation phase strategy was proposed which was capable of striking a better balance between diversification and intensification of the search process; in the proposed selfadaptation phase strategy, the best individuals were recruited to perform a local search using Tabu Search. Huang and Süer [33] proposed a dispatching rule based genetic algorithm with fuzzy satisfaction levels to solve the multiobjective manufacturing production planning problem, and the objective was to develop a decision making platform which appropriately handles conflicts among different performance measures in a manufacturing system.…”

Section: Literature Reviewmentioning

confidence: 99%

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Guo

Liu

et al. 2017

Mathematical Problems in Engineering

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The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

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“…Lei and Guo [25] used an effective neighborhood search for scheduling in dual resource constrained interval job shop with environmental objective. A new hybrid island model GA for JSP was proposed by Kurdi [26]. A new naturally inspired migration selection mechanism and a new naturally inspired evolution model were proposed in order to avoid falling into the premature convergence and improve the search diversification [27].…”

Section: Introductionmentioning

confidence: 99%

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Wang

Cai

2016

Adv. Manuf.

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Job-shop scheduling problem (JSP) is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm (AMGA) has been proposed to solve this problem. Firstly, using multi-populations and adaptive crossover probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some classical benchmark JSPs taken from the literature and compared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

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A new hybrid island model genetic algorithm for job shop scheduling problem

Cited by 62 publications

References 50 publications

Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

An adaptive multi-population genetic algorithm for job-shop scheduling problem

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