Lot streaming for product assembly in job shop environment

Authors:
F. T. S. Chan;T. C. Wong;L. Y. Chan
Affiliations:
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, HKSAR, China;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, HKSAR, China;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, HKSAR, China
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2008

Citing 9
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Lot streaming in open shops

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An evolutionary algorithm for assembly job shop with part sharing

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A resource-constrained assembly job shop scheduling problem with Lot Streaming technique

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Performance evaluation of proposed Differential Evolution and Particle Swarm Optimization algorithms for scheduling m-machine flow shops with lot streaming

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A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop

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Abstract

Assembly job shop scheduling problem (AJSP) is an extension of classical job shop scheduling problem (JSP). AJSP starts with JSP and appends an assembly stage to the completed jobs. Lot streaming (LS) technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. This paper combines, for the first time, LS and AJSP, extending LS applicability to both machining and assembly. To solve this complex problem, an efficient algorithm is proposed using genetic algorithms and simple dispatching rules. Experimental results suggest that equal size LS outperforms varied size LS with respect to the objective function.