Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms

¹ Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence 11, 3rd Street NW, P.O. Box 2259, Auburn, WA 98071, USA
² School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
³ Department of Industrial Engineering, Yazd University, Yazd, Iran
⁴ Republic Department of System Engineering, Faculty of Economics, Technical University of Ostrava, Ostrava, Czech Republic
⁵ Department of Operations Management & Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman

^* Corresponding author: fariba.goodarzian@mirlabs.org

Received: 4 February 2020
Accepted: 28 February 2021

Abstract

The role of medicines in health systems is increasing day by day. The medicine supply chain is a part of the health system that if not properly addressed, the concept of health in that community is unlikely to experience significant growth. To fill gaps and available challenging in the medicine supply chain network (MSCN), in the present paper, efforts have been made to propose a location-production-distribution-transportation-inventory holding problem for a multi-echelon multi-product multi-period bi-objective MSCN network under production technology policy. To design the network, a mixed-integer linear programming (MILP) model capable of minimizing the total costs of the network and the total time the transportation is developed. As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems. Then, some experiments were designed and solved by an optimization solver called GAMS (CPLEX) and the presented algorithms to validate the model and effectiveness of the presented algorithms. Comparison of the provided results by the presented algorithms and the exact solution is indicative of the high-quality efficiency and performance of the proposed algorithm to find a near-optimal solution within reasonable computational time. Hence, the results are compared with commercial solvers (GAMS) with the suggested algorithms in the small-sized problems and then the results of the proposed meta-heuristic algorithms with the heuristic methods are compared with each other in the large-sized problems. To tune and control the parameters of the proposed algorithms, the Taguchi method is utilized. To validate the proposed algorithms and the MSCN model, assessment metrics are used and a few sensitivity analyses are stated, respectively. The results demonstrate the high quality of the proposed IACO algorithm.