Computers and Operations Research
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Lot streaming and scheduling heuristics for m-machine no-wait flowshops
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling and lot streaming in flowshops with no-wait in process
Journal of Scheduling
Flow shop scheduling with lot streaming
Operations Research Letters
An evolutionary algorithm for assembly job shop with part sharing
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
A differential evolution algorithm for lot-streaming flow shop scheduling problem
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers
Journal of Intelligent Manufacturing
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This paper addresses the problem of making sequencing and scheduling decisions for n jobs-m-machines flow shops under lot sizing environment. Lot streaming (Lot sizing) is the process of creating sub lots to move the completed portion of a production sub lots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. Evolutionary algorithms that belong to search heuristics find more applications in recent research. Genetic algorithm (GA) and hybrid genetic algorithm (HEA) also known as hybrid evolutionary algorithm fall under evolutionary heuristics. On this concern this paper proposes two evolutionary algorithms namely, GA and HEA to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set-up time. The following two algorithms are used to evaluate the performance of the proposed GA and HEA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) simulated annealing algorithm (SA) for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.