haji abbas, M., Hosseininezhad, S. (2016). A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain. Journal of Industrial and Systems Engineering, 9(special issue on location allocation and hub modeling), 71-84.
Marjan haji abbas; Seyed Javad Hosseininezhad. "A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain". Journal of Industrial and Systems Engineering, 9, special issue on location allocation and hub modeling, 2016, 71-84.
haji abbas, M., Hosseininezhad, S. (2016). 'A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain', Journal of Industrial and Systems Engineering, 9(special issue on location allocation and hub modeling), pp. 71-84.
haji abbas, M., Hosseininezhad, S. A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain. Journal of Industrial and Systems Engineering, 2016; 9(special issue on location allocation and hub modeling): 71-84.
A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
Abstract
This paper proposes a discrete capacitated covering location-allocation model for pharmaceutical centers. In the presented model, two objectives are considered; the first one is minimization of costs and the second one try to maximize customer satisfaction by definition of social justice. Social justice in the model means that we consider customers satisfaction by using distance. the introduced model is an extension of the maximum covering model by adding zone constraint that Actually the distance between facility and customer zone is categorized as best, possible and not possible location. The model tries to locate facilities in best and possible location. In addition, number of missed customers is important and the model considered this issue. Since the nature of the demand is uncertain, a robust approach is proposed. The model could applied to other industries that have limitation about their product such as perishability foods or other perishability product. The model solved by GAMS Software. Finally, a numerical example with sensitivity analysis presented to illustrate the proposed model.
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