scholarly journals Impact of Personnel Flexibility on Job Shop Scheduling

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ren Lin ◽  
Guohua Zhou ◽  
Aijun Liu ◽  
Hui Lu ◽  
Tonglei Li
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Case Analysis ◽  
Quantitative Relationship ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Scheduling Model ◽  
The Relationship

Considering the lack of the research on the relationship between HR flexibility and scheduling effect, a resource-competency matrix-based method was proposed in order to reveal the quantitative relationship between them. Meanwhile, a job shop scheduling model with HR flexibility was established and the improved genetic algorithm was used to solve the model. A case analysis demonstrated significant impact of HR flexibility on the scheduling effect, which provided valuable guidance for building flexible manufacturing systems.

scholarly journals Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints

2020 ◽  
Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane
Keyword(s):  
Manufacturing Systems ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Efficient Production ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

AbstractIn job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.

An Improved Genetic Algorithm For Just-In-Time Job-Shop Scheduling Problem

2012 ◽  
Vol 472-475 ◽  
pp. 2462-2467 ◽  
Author(s):  
Hong An Yang ◽  
Jin Yuan Li ◽  
Liang Liang Qi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Idle Time ◽  
Just In Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Insertion Mechanism ◽  
Early Operations

This paper studies a just-in-time job-shop scheduling problem (JITJSSP) in which each operation has an earliness cost or a tardiness cost if it is completed before or after its due date and the objective function is to minimize the total earliness and tardiness costs of all operations. In order to solve this problem, an improved genetic algorithm (IGA) is introduced in this paper. IGA utilizes an operation-based scheme to represent schedules as chromosomes. Then, each chromosome is processed through a three-stage mechanism. Firstly, the semi-active decoding process is employed to expand the search space of solutions and guarantee comprehensive solutions. Secondly, the greedy insertion mechanism for tardy operations is executed to move the tardy operations left to the appropriate idle time to reduce the tardiness costs. Finally, the greedy insertion mechanism for early operations is proposed to shift the early operations right to the suitable idle time to decrease the earliness costs. After the maximum number of generations is reached, IGA continues with selection, crossover and mutation. The experimental results finally show that most of solutions on the benchmarks are improved by our algorithm.

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