A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix

Abstract

<p class="Abstract" style="margin: 0cm 0cm 0pt; layout-grid-mode: char;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;"><span style="mso-bidi-font-size: 9.0pt;">Manufacturing supply chain(SC) faces changing business environment and various customer demands. Pareto Ant Colony Optimisation (P-ACO) in order to obtain the non-dominated set of different SC designs was utilized as the guidance for designing manufacturing SC. P-ACO explores the solution space on the basis of applying the Ant Colony Optimisation algorithm and implementing more than one pheromone matrix, one for every objective. The SC</span><span style="mso-bidi-font-size: 9.0pt; mso-fareast-language: ZH-CN;"> design</span><span style="mso-bidi-font-size: 9.0pt;"> problem has been addressed by using Pareto Ant Colony Optimisation in which two objectives are minimised simultaneously. There were tested two ways in which the quantity of pheromones in the PM is incremented. In the SPM, the pheromone increment is a function of the two objectives, cost and time, while in MPM the pheromone matrix is divided into two pheromones, one for the cost and another one for the time. It could be concluded that the number of solutions do not depend on if the pheromone is split or is a function of the two variables because both method explore the same solution space. Although both methods explore the same solution space, the POS generated by every one is different. The POS that is generated when the pheromone matrix is split got solutions with lower time and cost than SMP because in the probabilistic decision rule a value of λ = 0.2 is used. It means that the ants preferred solution with a low cost instead of solutions with low time. The strategy of letting the best-so-far ant deposit pheromone over the PM accelerates the algorithm to get the optimal POS although the number of ants in the colony is small.</span></span></span></p><p class="Abstract" style="margin: 0cm 0cm 0pt; layout-grid-mode: char;"><span style="mso-bidi-font-size: 9.0pt;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">An experimental example is used to test the algorithm and show the benefits of utilising two pheromone matrices and multiple ant colonies in SC optimisation problem.</span></span></span></p&gt

Cuckoo search algorithm for multi-objective supply chain problem

“…There are many studies concerning on MOO applied Ant Colony Optimization(ACO) algorithm for SC problem with different configurations and frameworks. Zhao et al [20] applied ACO algorithm for optimization SC design with a different business environment, and different customer demands to reduce total cost and total lead-times. Monkayo Martinez and Zhang [21] developed SC for a family of the product containing a complex hierarchy of sub-assemblies and components to satisfy two objectives (cost and time).…”

Solution of a Bi-Objective Purchasing Scheduling Problem with Constrained Funds using Pareto Optimization

“…represents the relative importance of the different objectives according to [16]. Selection of arcs (i,j) related to the heuristic rule of equation (11) implies that updating pheromone requires a multi-objective definition in design of the P-ACO 2 variant.…”