SOLVING THE OPEN SHOP SCHEDULING PROBLEM VIA A HYBRID GENETIC-VARIABLE NEIGHBORHOOD SEARCH ALGORITHM

2009 ◽  
Vol 40 (4) ◽  
pp. 259-285 ◽  
Author(s):  
G. I. Zobolas ◽  
C. D. Tarantilis ◽  
G. Ioannou
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Open Shop ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Open Shop Scheduling ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

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.

scholarly journals Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems

2021 ◽  
pp. 275-288
Author(s):  
Hanan Ali Chachan ◽  
Faez Hassan Ali
Keyword(s):  
Hybrid Algorithm ◽  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.

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