Authors:
Yuan-Jye Tseng
;
Jian-Yu Chen
and
Feng-Yi Huang
Affiliation:
Yuan Ze University, Taiwan
Keyword(s):
Assembly sequence planning, Multi-plant, Collaborative manufacturing, Particle swarm optimization, PSO.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
Abstract:
In a multi-plant collaborative manufacturing system in a global logistics chain, a product can be manufactured and assembled at different plants located at various locations. In this research, a decision support system for multi-plant assembly sequence planning is presented. The multi-plant assembly sequence planning model integrates two tasks, assembly sequence planning and plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the operational constraints and precedence constraints to achieve assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants under the constraints of plant capabilities to achieve multi-plant cost objectives. A particle swarm optimization (PSO) solution approach is presented by encoding a particle using a position matrix defined by the numbers of components and plants. The PSO algorithm simultaneously performs assembly sequence plan
ning and plant assignment with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new multi-plant assembly sequence planning model and the new PSO solution method. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem. In this paper, an example product is tested and illustrated.
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