Hybrid genetic algorithm for SDST flow shop scheduling with due dates: a case study

2010 ◽  
Vol 2 (3/4) ◽  
pp. 141 ◽  
Author(s):  
Ashwani Dhingra ◽  
Pankaj Chandna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Due Dates ◽  
Shop Scheduling

scholarly journals MULTI-OPERATOR HYBRID GENETIC ALGORITHM-SIMULATED ANNEALING FOR REENTRANT PERMUTATION FLOW-SHOP SCHEDULING

2021 ◽  
Vol 11 (3) ◽  
pp. 109-126
Author(s):  
Achmad Pratama Rifai ◽  
Putri Adriani Kusumastuti ◽  
Setyo Tri Windras Mara ◽  
Rachmadi Norcahyo ◽  
Siti Zawiah Md Dawal
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Hybrid Genetic Algorithm Based on Chaotic Migration Strategy for Solving Flow Shop Scheduling Problem with Fuzzy Delivery Time

2015 ◽  
Vol 19 (3) ◽  
pp. 359-364
Author(s):  
Wen-Zhan Dai ◽  
◽  
Kai Xia
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Migration Strategy ◽  
Shop Scheduling ◽  
Delivery Times ◽  
Search Capability

In this paper, a hybrid genetic algorithm based on a chaotic migration strategy (HGABCM) for solving the flow shop scheduling problem with fuzzy delivery times is proposed. First, the initial population is divided into several sub-populations, and each sub-population is isolated and evolved. Next, these offspring are further optimized by a strategy that combines NEH heuristic algorithm proposed by M. Navaz, J.-E. Enscore, I. Ham in 1983 with a newly designed algorithm that has excellent local search capability, thereby enhancing the strategy’s local search capability. Then, the concept of a chaotic migration sequence is introduced to guide the ergodic process of the migration of individuals effectively so that information is exchanged sufficiently among sub-populations and the process of falling into a local optimal solution is thereby avoided. Finally, several digital simulations are provided to demonstrate the effectiveness of the algorithm proposed in this paper.

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