Melih Çelik

The Post-Disaster Debris Clearance Problem Under Incomplete Information

Debris management is one of the most time consuming and complicated activities among post-disaster operations. Debris clearance is aimed at pushing the debris to the sides of the roads so that relief distribution and search-and-rescue operations can be maintained in a timely manner. Given the limited resources, uncertainty, and urgency during disaster response, efficient and effective planning of debris clearance to achieve connectivity between relief demand and supply is important. In this paper, we define the stochastic debris clearance problem SDCP, which captures post-disaster situations where the limited information on the debris amounts along the roads is updated as clearance activities proceed. The main decision in SDCP is to determine a sequence of roads to clear in each period such that benefit accrued by satisfying relief demand is maximized. To solve SDCP to optimality, we develop a partially observable Markov decision process model. We then propose a heuristic based on a continuous-time approximation, and we further reduce the computational burden by applying a limited look ahead on the search tree and heuristic pruning. The performance of these approaches is tested on randomly generated instances that reflect various geographical and information settings, and instances based on a real-world earthquake scenario. The results of these experiments underline the importance of applying a stochastic approach and indicate significant improvements over heuristics that mimic the current practice for debris clearance.

Order picking in a parallel-aisle warehouse with turn penalties

In many real-life routing problems, incorporating the negative effects of turns is an important, but often overlooked aspect. This is especially true for order picking in warehouses, where making the turns not only decreases the picking efficiency by reducing the speed of the vehicle, but it also results in other unquantifiable effects such as vehicle tipovers, increased congestion and increased risk of collision with pedestrians or other vehicles. In this paper, we consider the order picking problem in a parallel-aisle warehouse by taking into account the number and effect of the turns. In particular, we show that the problem of minimising the number of turns, minimising travel time under turn penalties, the biobjective problem that involves turn and travel time minimisation as separate objectives, and the triobjective problem with U-turn minimisation as a third objective can all be solved in polynomial time. Our computational results show that the algorithms we develop can generate the corresponding Pareto front very quickly, and significantly outperform heuristic approaches used in practice.

Order picking in parallel-aisle warehouses with multiple blocks: complexity and a graph theory-based heuristic

In this paper, we consider the order picking problem (OPP), which constitutes one of the special cases of the Steiner travelling salesperson problem and addresses the costliest operation in a warehouse. Given a list of items to be picked and their locations in the warehouse layout, the OPP aims to find the shortest route that starts from a depot point, picks all the items in the list, and returns to the depot. This paper fills two important gaps regarding the OPP. First, to the best of our knowledge, we present the first complexity results on the problem. Second, we propose a heuristic approach that makes use of its graph-theoretic properties. Computational experiments on randomly generated instances show that the heuristic not only outperforms its state-of-the-art counterparts in the literature, but it is also robust in terms of changing problem parameters.