A learning tabu search for a truck allocation problem with linear and nonlinear cost components

Abstract

The two-level problem studied in this article consists of optimizing the refueling costs of a fleet of locomotives over a railway network. The goal consists of determining: (1) the number of refueling trucks contracted for each yard (truck assignment problem denoted TAP) and (2) the refueling plan of each locomotive (fuel distribution problem denoted FDP). As the FDP can be solved efficiently with existing methods, the focus is put on the TAP only. In a first version of the problem (denoted (P1)), various linear costs (e.g., fuel, fixed cost associated with each refueling, weekly operating costs of trucks) have to be minimized while satisfying a set of constraints (e.g., limited capacities of the locomotives and the trucks). In contrast with the existing literature on this problem, two types of nonlinear cost components will also be considered, based on the following ideas: (1) if several trucks from the same fuel supplier are contracted for the same yard, the supplier is likely to propose discounted prices for that yard (Problem (P2)); (2) if a train stops too often on its route, a penalty is incurred, which represents the dissatisfaction of the clients (Problem (P3)). Even if exact methods based on a mixed integer linear program formulation are available for (P1), they are not appropriate anymore to tackle (P2) and (P3). Various methods are proposed for the TAP: a descent local search, a tabu search, and a learning tabu search (LTS). The latter is a new type of local search algorithm. It involves a learning process relying on a trail system, and it can be applied to any combinatorial optimization problem. Results are reported and discussed for a large set of instances (for (P1), (P2), and (P3)), and show the good performance of LTS

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

“…Bierlaire, Thémans, and Zufferey (2010) have combined variable neighbourhood search (Thevenin and Zufferey 2019) (with the aim to explore the solution space) with a modified trust-region algorithm (for intensification purposes), for nonlinear global optimisation. For solving the BAP in unreliable machines, Shi and Men (2003) propose a tabu search (Schindl and Zufferey 2015) to speed up the nested partitions method. Amiri and Mohtashami (2011) propose a GA integrated to line-search method for determining the optimal/near-optimal size of each buffer storage.…”

A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times

“…On the one hand, it is interesting to extend the problem with considering other practical constraints like lunch-break time of caregivers, and flexible time-window of patients. On the other side, from the perspective of improving solving methods, we can consider the incorporation of machine learning techniques with meta-heuristic to obtain more efficiency solutions (Schindl & Zufferey, 2015;Chen et al, 2016b). Furthermore, sharing economic (Choi & He, 2019) also provides a promising way to improve the efficiency of the operational management of HHC service.…”

Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations

“…The taboo search algorithm can search beyond the local optimal solution by setting taboo tables and amnesty criteria. Schindl [13] used a taboo search algorithm to solve the models built for truck allocation problems, but its optimization results technical stations can affect each other, and collaborative operations between technical stations can facilitate minimization of the average dwell time of cars at the two yards. As such, extending the problem from a single technical station to two adjacent technical stations is necessary and valuable.…”