Capacitated single-allocation hub location model for a flood relief distribution network

In disaster management, the logistics for disaster relief must deal with uncontrolled variables, including transportation difficulties, limited resources, and demand variations. In this work, an optimization model based on the capacitated single-allocation hub location problem is proposed to determine an optimal location of flood relief facilities with the advantage of economies of scale to transport commodities during a disaster. The objective is to minimize the total transportation cost, which depends on the flood severity. The travel time is bounded to ensure that survival packages will be delivered to victims in a reasonable time. Owing to complexity of the problem, a hybrid algorithm is developed based on a variable neighborhood search and tabu search (VNS-TS). The computational results show that the VNS found the optimal solutions within a 2% gap, while the proposed VNS-TS found the optimal solution with a 0% gap. A case study of severe flooding in Thailand is presented with consideration of related parameters such as water level, hub capacity, and discount factors. Sensitivity analyses on the number of flows, discount factors, capacity, and bound length are provided. The results indicated that demand variation has an impact on the transportation cost, number of hubs, and route patterns.     

Joint location and dispatching decisions for Emergency Medical Services

The main purpose of Emergency Medical Service systems is to save lives by providing quick response to emergencies. The performance of these systems is affected by the location of the ambulances and their allocation to the customers. Previous literature has suggested that simultaneously making location and dispatching decisions could potentially improve some performance measures, such as response times. We developed a mathematical formulation that combines an integer programming model representing location and dispatching decisions, with a hypercube model representing the queuing elements and congestion phenomena. Dispatching decisions are modeled as a fixed priority list for each customer. Due to the model’s complexity, we developed an optimization framework based on Genetic Algorithms. Our results show that minimization of response time and maximization of coverage can be achieved by the commonly used closest dispatching rule. In addition, solutions with minimum response time also yield good values of expected coverage. The optimization framework was able to consistently obtain the best solutions (compared to enumeration procedures), making it suitable to attempt the optimization of alternative optimization criteria. We illustrate the potential benefit of the joint approach by using a fairness performance indicator. We conclude that the joint approach can give insights of the implicit trade-offs between several conflicting optimization criteria.