Evolution programs for job-shop scheduling

Mesghouni, Khaled; Hammadi, Slim; Borne, Pierre

doi:10.1109/icsmc.1997.625839

Cited by 38 publications

(33 citation statements)

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“…We could mention to some of solution seed structures used for solving FJSP. Mesghouni et al (1997) proposed the Parallel Jobs Representation in a form of a matrix where each row represents a job and each entry is a pair value. An indirect representation containing a pair of chromosomes, A and B, was proposed by Chen et al (1997) where A is a string of machine assignments and B is a string of sequencing.…”

Section: Introductionmentioning

confidence: 99%

A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

Sadaghiani¹,

Boroujerdi²,

Mirhabibi³

et al. 2014

10.5267/j.dsl

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Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

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Section: Introductionmentioning

confidence: 99%

A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

Sadaghiani¹,

Boroujerdi²,

Mirhabibi³

et al. 2014

10.5267/j.dsl

View full text Add to dashboard Cite

show abstract

“…Mesghouni et al [222] were the first to use GA for the solution of FJSSP by proposing parallel job and parallel machine representation. In literature, the approaches for FJSSP solution can be classified as follows:…”

Section: General Approaches For Fjssp Solution Using Gamentioning

confidence: 99%

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Amjad

Butt

Kousar

et al. 2018

Mathematical Problems in Engineering

113

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Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

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“…The processing time is the multiplication result of cycle time and customer demand (order quantity). Since each operation is done by particular machines with different processing times, the problem is called a Non-deterministic Polynomial-time hard (NP-hard) problem [8].…”

Section: Figure 1 Genetic Algorithm Flowchartmentioning

confidence: 99%

Application of Genetic Algorithm on Spare Part Automotive Body Scheduling

Rendy

Maukar

Silitonga

2016

International Journal of Industrial Research and Applied Engineering

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Abstract. Job shop scheduling is one of the complex problems in the manufacturing industry, such as an automotive body manufacturer. This manufacturing company, located in Cikarang, Indonesia, deals with huge customer demand that leads to difficulties in production scheduling. This will cause a delay in some jobs and product deliveries. The current system is using the semi-active scheduling approach and requires 637 minutes for performing 6 jobs with 5 machines. The genetic algorithm (GA) model is proposed as an alternative solution to solve this problem. The GA parameter is set as follow: The population size expected is 30 with maximum generation can be produced in amount of 50. The crossover rate, mutation, and preservation are set to 0.3, 0.1, and 0.1, respectively. After 50 generations are obtained, the optimum solution is shown in generation 6 with a makespan of 597 minutes. Thus, the genetic algorithm model is effectively reducing the makespan of the job-shop scheduling problem by 10% compared to the current method applied at the company.

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Evolution programs for job-shop scheduling

Cited by 38 publications

References 1 publication

A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Application of Genetic Algorithm on Spare Part Automotive Body Scheduling

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