Supply Chain Networks Design (SCND) is a systematic approach for finding the best position and size for facilities to ensure optimal product flow using mathematical modeling. Three ant colony-based algorithms, ACO1, ACO2, and ACO3, are used to design supply chain networks in this paper. ACO2 and ACO3 are developed based on two new pheromone trails and one heuristic trail. To measure the effectiveness of the proposed algorithms, a numerical study is performed on generated problem cases, and the results are compared to those obtained using LINGO. The proposed algorithms provide a significantly better solution with a difference of about 3.04% for ACO1, 1.78% for ACO2, and 1.65% for ACO3 on average from the exact solution and in a very short time compared to Lingo. The computational analysis shows that ACO2 and ACO3 give better results than ACO1.
Robust scheduling is aiming at constructing proactive schedules capable of dealing with multiple disruptions during project execution. Insertion a time buffer, before an activity start time, is a method to improve the robustness (stability) of a baseline schedule. In this paper, we introduce new heuristics for inserting time buffers in a given baseline schedule while the project due date is predefined and stochastic activity duration is considered. Computational results obtained from a set of benchmark projects show that the proposed heuristics capable of generating proactive schedules with acceptable quality and solution robustness.
Reverse logistics (RL) network can be adequately planned and implemented to gain additional benefits such as maximizing customer satisfaction and a positive image of the business organization, even though most distribution networks are not equipped with reverse channels to deal with the return of merchandise. Therefore, the main objective of this paper is to develop a new mixed-integer nonlinear programming (MINLP) mathematical model with a single-objective, single-product, multi-stage closed-loop supply chain network design (CLSC ND), considering the fixed transportation charge in the distribution network that has been neglected in the recently published papers in the field of CLSC ND. Since such network design challenges belong to the class of NP-hard problems, an algorithm based on ant colony optimization (ACO) is proposed to design a multi-stage RL network with fixed transportation cost and variable cost for the routes. Four network characteristics of different sizes were designed, and 30 instances were randomly generated for each network characteristic to evaluate the effectiveness of the proposed algorithm. The computational analysis of the results shows the high-quality effectiveness of the proposed ACO algorithm compared with the exact results.
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