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Bi-criterion optimisation for configuring an assembly supply chain using Pareto ant colony meta-heuristic
Authors:Luis A Moncayo-Martínez  Gustavo Recio
Affiliation:1. Department of Industrial and Operations Engineering, Instituto Tecnológico Autónomo de México (ITAM), Río Hondo No. 1, Col. Progreso Tizapán, C.P. 01080 Mexico City, Mexico;2. Department of Computer Science, Carlos III University of Madrid, Avda. de la Universidad No 30, Sabatini Building, 28911 Leganés, Madrid, Spain
Abstract:An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.
Keywords:Supply chain configuration  Multi-objective optimisation  Pareto set  Ant colony system
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