Designing a drug distribution network using a two-tier genetic algorithm and priority-based coding The case of Adorateb

In addition to its inherent impact on the health section, the distribution of pharmaceutical products has also an economic aspect. This paper aims to design a supply chain for pharmaceutical distribution companies so that they have the best coverage and performance at the lowest cost.

Design/methodology/approach:

 An operations research model has been developed based on minimizing distribution costs and construction costs to address the problem. The model’s constraints included the rules governing the national pharmaceutical supply chain. Due to the complexity of the model and the high dimensions of the problem in real conditions, the model has been solved using a two-tier genetic algorithm in combination with priority-based coding and the K-medoids clustering method. To optimize the performance of the algorithm, the parameter tuning of the Taguchi method has been used.

Clustering demand points from 429 to 50 centers had less than 1% implication on the total costs. Distribution costs might even grow in optimal designs, however, the total cost was reduced by 36%. Optimal points for distribution centers were located and an optimal distribution plan for goods was recommended for a company distributing 239 types of pharmaceutical products.

Research limitations/implications:

 In this paper, only costs were considered due to establishing new distribution centers and dispatching goods. In future studies, inventory costs can be considered either. Also, there may be more legal aspects that have not been considered such as the minimum number of distribution centers.

Practical implications: 

To evaluate the efficiency of the model in practice, a case study was implemented on the drug distribution company, i.e., Adorateb. For the studied company, an appropriate distribution network, as well as an optimal distribution pattern of drugs, were identified and compared in terms of cost with the current situation of the company. The recommended optimal location relative to the current situation indicated a significant reduction in costs.

Social implications: 

Designing supply chains strongly influences the performance of the pharmaceutical supply chain. By reducing costs through optimal network design, while covering demand requirements, a society may have sustainable access to pharmaceutical products in a long term.

Originality/value: 

The main innovation of this paper is the recommendation of a mathematical model for designing a pharmaceutical distribution network while considering real supply chain national regulations along with a hybrid solution approach that can handle real-size problems. The applicability of the model and the solution approach were verified by a case study.