Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

Abstract

Optimised sequencing in the Mixed Model Assembly Line (MMAL) is a major factor to effectively balance the rate at which raw materials are used for production. In this paper we present an Ant Colony Optimisation with Elitist Ant (ACOEA) algorithm on the basis of the basic Ant Colony Optimisation (ACO) algorithm. An ACOEA algorithm with the taboo search and elitist strategy is proposed to form an optimal sequence of multi-product models which can minimise deviation between the ideal material usage rate and the practical material usage rate. In this paper we compare applications of the ACOEA, ACO, and two other commonly applied algorithms (Genetic Algorithm and Goal Chasing Algorithm) to benchmark, stochastic problems and practical problems, and demonstrate that the use of the ACOEA algorithm minimised the deviation between the ideal material consumption rate and the practical material consumption rate under various critical parameters about multi-product models. We also demonstrate that the convergence rate for the ACOEA algorithm is significantly more than that for all the others considered.

A multi-period inventory transportation model for tactical planning of food grain supply chain

“…The ant colony optimisation (ACO) has been successfully used to solve combinatorial problems such as the Traveling Salesman Problem, Quadratic Assembly and Vehicle Routing (Dorigo and Stützle 2009), FCTP (Xie and Jia 2012), job-shop scheduling (Chang et al 2008) and the sequencing problems (Zhu and Zhang 2011). Therefore, the Improved Max-Min Ant System (IMMAS) algorithm is used to solve the model and its detail description is given in the next section.…”