In this study, the response phase of the management of natural disasters is investigated. One of the important issues in this phase is determining the distribution areas and timely distribution of relief to affected areas in which transportation routing is of a critical matter. In the event of disasters, especially flood and earthquake, terrestrial transportation is not that much easy due to the damage to many infrastructures. For this reason, we propose that delivering relief from the distribution areas to disaster stricken places should be done simultaneously by terrestrial as well as aerial transportation modes to increase route reliability and reduce travel time. In this study, for relief allocation after earthquake, we offer a mixed-integer nonlinear open location-routing model in uncertainty condition. This model includes several contradictory objectives and variety of factors such as travel time, total costs, and reliability. In order to solve this model, a hybrid solution by combining robust optimization and fuzzy multiobjective programming has been used. The performance and effectiveness of the offered model and solution approach has been investigated through a case study on the earthquake in East Azerbaijan, Iran. Our computational results show the solution we have offered for real problems has been effective.

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time.