scholarly journals A Differential-Based Harmony Search Algorithm With Variable Neighborhood Search for Job Shop Scheduling Problem and Its Runtime Analysis

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76313-76330 ◽  
Author(s):  
Fuqing Zhao ◽  
Shuo Qin ◽  
Guoqiang Yang ◽  
Weimin Ma ◽  
Chuck Zhang ◽  
...  
Keyword(s):  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

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.

A Hybrid Grey Wolf Optimization for Job Shop Scheduling Problem

2018 ◽  
Vol 17 (03) ◽  
pp. 1850016 ◽  
Author(s):  
Tianhua Jiang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Grey Wolf ◽  
Job Shop Scheduling Problem ◽  
Genetic Operators ◽  
Grey Wolf Optimization ◽  
Shop Scheduling

This paper aims to develop a hybrid grey wolf optimization algorithm (HGWO) for solving the job shop scheduling problem (JSP) with the objective of minimizing the makespan. Firstly, to make the GWO suitable for the discrete nature of JSP, an encoding mechanism is proposed to implement the continuous encoding of the discrete scheduling problem, and a ranked-order value (ROV) rule is used to conduct the conversion between individual position and operation permutation. Secondly, a heuristic algorithm and the random rule are combined to implement the population initialization in order to ensure the quality and diversity of initial solutions. Thirdly, a variable neighborhood search algorithm is embedded to improve the local search ability of our algorithm. In addition, to further improve the solution quality, genetic operators (crossover and mutation) are introduced to balance the exploitation and exploration ability. Finally, experimental results demonstrate the effectiveness of the proposed algorithm based on 23 benchmark instances.

scholarly journals A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

2021 ◽  
Vol 7 ◽  
pp. e574
Author(s):  
Nayeli Jazmin Escamilla Serna ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Joselito Medina-Marin ◽  
Norberto Hernandez-Romero ◽  
Irving Barragan-Vite ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

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