Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants.

Combined Location-Inventory Optimization of Deteriorating Products Supply Chain Based on CQMIP under Stochastic Environment

The design and optimization of combined location-inventory model for deteriorating products are a main focus in supply chain management. There were many combined location-inventory design models in this field, but these models are under the assumptions of adequate capacity facilities, invariable lead time, unique product, and uncorrelated retailer’s demands. These assumptions have a big gap in the practical situation. In this paper, we design a combined location-inventory model for deteriorating products under capacitated facilities, stochastic lead time, multiple products, and correlated retailers’ stochastic demands assumptions. These constraints are near to actual supply chain circumstance. The problem is modeled as conic quadratic mix-integer programming (CQMIP) to minimize the total expected cost. We explain how to formulate these problems as conic quadratic mixed-integer problems, and in order to obtain better computational results we use extended cover cuts. Simultaneously we compare our method with the previous Lagrange methods; the result is that the new CQMIP method can get better solution.

Discretization Based Heuristics for the Capacitated Multi-facility Weber Problem with Convex Polyhedral Barriers

The Capacitated Multi-facility Weber Problem addresses optimally locating I capacitated facilities in the plane and satisfying demand of J customers so as to minimize the total transportation cost. It assumes that facilities can be located anywhere on the plane and customers are directly connected to them. This study considers the case where there exist convex polyhedral barriers blocking passage and locating facilities inside. Then, the distances between facilities and customers have to be measured by taking into account the polyhedral barriers. The resulting problem is non-convex and difficult to solve. We propose several discretization based heuristic procedures which are especially designed for the problem. The performance of the suggested methods are tested on an extensive set of randomly generated test instances which are derived from standard test instances. Our results imply that the suggested heuristics yield very accurate and efficient solutions for this problem.