Evolutionary algorithms for scheduling m-machine flow shop with lot streaming
Robotics and Computer-Integrated Manufacturing
Lot streaming for product assembly in job shop environment
Robotics and Computer-Integrated Manufacturing
An evolutionary algorithm for assembly job shop with part sharing
Computers and Industrial Engineering
A resource-constrained assembly job shop scheduling problem with Lot Streaming technique
Computers and Industrial Engineering
A DE-based approach to no-wait flow-shop scheduling
Computers and Industrial Engineering
Efficient metaheuristics for pick and place robotic systems optimization
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive 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. To solve this problem, we propose a Differential Evolution Algorithm (DEA) and Particle Swarm Optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.