A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking

2014 ◽  
Vol 52 (19) ◽  
pp. 5714-5734 ◽  
Author(s):  
Huaping Chen ◽  
Shengchao Zhou ◽  
Xueping Li ◽  
Rui Xu
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Batch Processing ◽  
Two Stage ◽  
Batch Processing Machines ◽  
Release Times ◽  
Evolution Algorithm

scholarly journals An Enhanced Differential Evolution Algorithm with Fast Evaluating Strategies for TWT-NFSP with SSTs and RTs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rong Hu ◽  
Xing Wu ◽  
Bin Qian ◽  
Jian L. Mao ◽  
Huai P. Jin
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Total Weighted Tardiness ◽  
Release Times ◽  
Evolution Algorithm ◽  
No Wait ◽  
Dependent Setup Times

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times (i.e., the NFSP with SSTs and RTs) is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective (TWT) for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES.

Cost Optimization Problem of Hybrid Flow-Shop Based on Differential Evolution Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 1692-1700
Author(s):  
Zhong Hua Han ◽  
Xiang Bin Meng ◽  
Bin Ma ◽  
Chang Tao Wang
Keyword(s):  
Differential Evolution ◽  
Production Cost ◽  
Job Scheduling ◽  
Flow Shop ◽  
Fitness Function ◽  
Cost Optimization ◽  
Differential Evolution Algorithm ◽  
Hybrid Flow Shop ◽  
Processing Cost ◽  
Evolution Algorithm

A differential evolution algorithm based job scheduling method is presented, whose optimization target is production cost. The cost optimization model of hybrid flow-shop is thereby constructed through considering production cost as a factor in scheduling problem of hybrid flow-shop. In the implementation process of the method, DE is used to take global optimization and find which machine the jobs should be assigned on at each stage, which is also called the process route of the job; then the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. With converting time-based scheduling results to fitness function which is comprehensively considering the processing cost, waiting costs, and the products storage costs, the processing cost is taken as the optimization objective. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

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