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

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.

Medical Supply Transportation Scheduling in Pandemic

“…The research paper from Goodarzian et al [13] was used as the main reference during the research of this paper. This is because the model used in the research paper is focused on minimising the total cost of transportation and minimising the total transportation time taken for MD to arrive at the hospital.…”

“…The algorithm below shows the example of generating data using exponential distribution. The data range was extracted from Goodarzian et al [13]. Generate a random number x between MIN and MAX.…”

“…The data below was generated by exponential distribution using C Programming. The data range was extracted from Goodarzian et al [13]. We utilize this data in our research in order to produce the result.…”

“…Furthermore, constraint (12) is the quantity of shipped medical supply cannot exceed the transportation storage. Constraint (13) is the demand medical supply must be equal to quantity of shipped medical supply. Finally, constraints ( 14) and ( 15) is the sign restrictions that indicate the type of decision variables.…”