Jan Christiaens

Stochastic local search with learning automaton for the swap-body vehicle routing problem

Abstract This work presents the stochastic local search method for the Swap-Body Vehicle Routing Problem (SB-VRP) that won the First VeRoLog Solver Challenge. The SB-VRP, proposed on the occasion of the challenge, is a generalization of the classical Vehicle Routing Problem (VRP) in which customers are served by vehicles whose sizes may be enlarged via the addition of a swap body (trailer). The inclusion of a swap body doubles vehicle capacity while also increasing its operational cost. However, not all customers may be served by vehicles consisting of two bodies. Therefore swap locations are present where one of the bodies may be temporarily parked, enabling double body vehicles to serve customers requiring a single body. Both total travel time and distance incur costs that should be minimized, while the number of customers visited by a single vehicle is limited both by its capacity and by a maximum travel time. State of the art VRP approaches do not accommodate SB-VRP generalizations well. Thus, dedicated approaches taking advantage of the swap body characteristic are desired. The present paper proposes a stochastic local search algorithm with both general and dedicated heuristic components, a subproblem optimization scheme and a learning automaton. The algorithm improves the best known solution for the majority of the instances proposed during the challenge. Results are also presented for a new set of instances with the aim of stimulating further research concerning the SB-VRP.

The sport teams grouping problem

The sport teams grouping problem (STGP) concerns the assignment of sport teams to round-robin tournaments. The objective is to minimize the total travel distance of the participating teams while simultaneously respecting fairness constraints. The STGP is an NP-Hard combinatorial optimization problem highly relevant in practice. This paper investigates the performance of some complimentary optimization approaches to the STGP. Three integer programming formulations are presented and thoroughly analyzed: two compact formulations and another with an exponential number of variables, for which a branch-and-price algorithm is proposed. Additionally, a meta-heuristic method is applied to quickly generate feasible high-quality solutions for a set of real-world instances. By combining the different approaches’ results, solutions within 1.7% of the optimum values were produced for all feasible instances. Additionally, to support further research, the considered STGP instances and corresponding solutions files were shared online.

A decomposition approach to dual shuttle automated storage and retrieval systems

Heuristic control policies for dual shuttle automated storage and retrieval systems are studied.A decomposition approach employing both exact and heuristic components is presented and validated.A fast metaheuristic suitable for real-world sized AS/RS problems is proposed. Automated Storage and Retrieval Systems (AS/RS) have become vital in todays distribution and production environments, however it remains necessary to equip them with more efficient operational control policies. Motivated by real situations encountered by companies employing AS/RS, the present paper studies a miniload AS/RS system, with a dual shuttle crane in which a set of storage and retrieval requests must be scheduled such that the prioritized waiting time is minimized. Dual shuttle cranes have received minimal academic attention and thus continue to pose new problems that must be solved. The miniload AS/RS problem is addressed by decomposing it into a location assignment and sequencing problem. Different heuristic strategies are introduced for making the assignments, while a general mathematical model and efficient branch and bound procedure are proposed for optimizing the sequence. Additionally, a fast metaheuristic capable of solving larger instances is also developed. A set of real-world based benchmarks with varying characteristics is generated to evaluate the proposed methods. Very small instances prove the only for which optimal sequences are found in reasonable calculation time. Experimental results demonstrate the effectiveness of the heuristic decomposition method.