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.
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DOI : 10.1051/ro/2021031
@article{RO_2021__55_2_1015_0,
author = {Goodarzian, Fariba and Hoseini-Nasab, Hassan and Toloo, Mehdi and Fakhrzad, Mohammad Bagher},
title = {Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms},
journal = {RAIRO. Operations Research},
pages = {1015--1042},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {2},
doi = {10.1051/ro/2021031},
mrnumber = {4253783},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021031/}
}
TY - JOUR AU - Goodarzian, Fariba AU - Hoseini-Nasab, Hassan AU - Toloo, Mehdi AU - Fakhrzad, Mohammad Bagher TI - Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms JO - RAIRO. Operations Research PY - 2021 SP - 1015 EP - 1042 VL - 55 IS - 2 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021031/ DO - 10.1051/ro/2021031 LA - en ID - RO_2021__55_2_1015_0 ER -
%0 Journal Article %A Goodarzian, Fariba %A Hoseini-Nasab, Hassan %A Toloo, Mehdi %A Fakhrzad, Mohammad Bagher %T Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms %J RAIRO. Operations Research %D 2021 %P 1015-1042 %V 55 %N 2 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021031/ %R 10.1051/ro/2021031 %G en %F RO_2021__55_2_1015_0
Goodarzian, Fariba; Hoseini-Nasab, Hassan; Toloo, Mehdi; Fakhrzad, Mohammad Bagher. Designing a new medicine supply chain network considering production technology policy using two novel heuristic algorithms. RAIRO. Operations Research, Tome 55 (2021) no. 2, pp. 1015-1042. doi: 10.1051/ro/2021031
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