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Providing a Mathematical Routing‐Inventory Model for the Drug Supply Chain Considering the Travel Time Dependence and Perishability on Multiple Graphs

Madad AliCenter of Environment COMSATS University Islamabad-Abbottabad Campus, Islamabad, PakistanMartha NgDepartment of Accounting, Institute Business, and Technology Pelita Indonesia, Pekanbaru, IndonesiaRui DiasSchool of Business and Administration, Polytechnic Institute of Setbal, Portugal and CEFAGE-UE, IIFA,Iskandar MudaDepartment of Doctoral Program, Faculty Economic and Business, Universitas Sumatera Utara, Medan, IndonesiaRiyadh Al-ObaidiBarno AbdullaevaVice-Rector for Scienti c A airs, Tashkent State Pedagogical University, Tashkent, UzbekistanHimanshu SharmaDepartment of Computer Engineering and Applications, GLA University Mathura, Bharthia, IndiaHussein Mohammed AbualrejalHodeidah University, Al HudaydahAli Thaeer HammidComputer Engineering Techniques Department, Faculty of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
ABI

Аннотация

High turnover in the pharmaceutical industry, the location of placebos in urban spaces, and the high rate of corruption of products in this industry are the distinguishing features of the drug supply chain. Thus, to survive and maintain competitive advantages in the current business environment, managers are active in this background to implement the theoretical foundations of supply chain management. One of the influential areas in this category is integrated inventory management, inventory control, and vehicle routing. Therefore, this study mainly aims to analyze and define a routing problem, inventory in the drug supply chain with perishable products, and travel time dependence on multiple graphs with travel time dependence. The Box–Jenkins forecasting method has been utilized to meet the study’s aim and deal with demand uncertainty. This method can identify the best pattern governing the data. Finally, the mathematical model is validated, and managerial perspectives are provided. The study results demonstrated the possibility of achieving cost‐saving and reducing product spoilage. Applying the solutions of this model can provide some inherent social and environmental advantages, including reducing traffic load and emissions.

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