Comparing Two Heuristic Local Search Algorithms for a
Complex Routing Problem
Pablo CABRERA-GUERRERO1*, Andrés MOLTEDO-PERFETTI2, Enrique CABRERA3, Fernando PAREDES4
1 Pontificia Universidad Católica de Valparaíso,
Av. Brasil 2241, Valparaíso, 2362807, Chile
email@example.com (* Corresponding author)
2 Escuela de Psicología, Universidad Santo Tomás,
Los Limonares 190, Viña del Mar, 2561694, Chile
3 CINFAV, Universidad de Valparaíso,
Blanco 951, Valparaíso, 2362905, Chile
4 Escuela de Ingeniería Industrial, Universidad Diego Portales,
Manuel Rodríguez Sur 415, Santiago, 8370109, Chile
Abstract: Vehicle routing problems (VRP) have been widely studied in literature. Heuristics as well as exact algorithms have been applied to solve this kind of problems. In this study we approximately solve the VRP with simultaneous pickup and delivery and time windows by means of two well-known heuristics namely Tabu Search and Simulated Annealing. We compare the obtained results and then propose a restoration technique that allows both Tabu Search and Simulated Annealing to better explore the solution space. Results show that the proposed restoration technique allows both heuristic algorithms to obtain better results.
Keywords: Tabu Search, Simulated Annealing, Reverse Logistic, Restoration Techniques.
CITE THIS PAPER AS:
Pablo CABRERA-GUERRERO, Andrés MOLTEDO-PERFETTI, Enrique CABRERA, Fernando PAREDES, Comparing Two Heuristic Local Search Algorithms for a Complex Routing Problem, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(4), pp. 411-420, 2016.
Heuristic algorithms have been applied on a variety of problems in operations research and logistics for more than 50 years now. They have been shown to be very effective in dealing with complex problems that cannot be solved to optimality by traditional techniques such as mathematical programming. This is because mathematical programming methods often fail as the optimisation problem gets larger (i.e. more decision variables are involved).
Two well-known heuristic local search algorithms that have been applied on this kind of complex optimisation problems are Tabu Search  and Simulated Annealing [20, 21, 27]. In this paper we study the performance of these two heuristic algorithms when solving a complex problem arising in logistics called vehicle routing problem (VRP) with simultaneous pickup and delivery and time windows (VRPSPDTW).
Tabu Search algorithm has been used to approximately solve a range of combinatorial optimisation problems [5, 10, 23]. It has been shown to be a very simple, yet effective, method to approximately solve large and complex combinatorial optimisation problems such as the one we address in this paper. Just as Tabu Search, Simulated Annealing has also been considered to approximately solve a variety of optimisation problems (see for instance [3, 17, 24, 36]).
In this paper we implement both Tabu Search and Simulated Annealing algorithms and apply them on the VRPSPDTW. We then compare the results obtained by each technique.
One difficult we face when solving routing problems by means of local search algorithms is that neighbours of the current solution are often not feasible. Thus, we need to restore such neighbours so they become feasible. In this paper we propose a generic restoration strategy in the aim of making both local search algorithms to perform more efficiently. It is important to note that the restoration strategy we propose in this paper is directly applied on the local search algorithms. Thus, it can be seen as a generic strategy that can be included in any local search algorithm other than Tabu Search and Simulated Annealing.
This paper is organised as follows. In next section both Tabu Search and Simulated Annealing algorithms are described and their main features are highlighted. In Section 3 the VRPSPDTW problem we address in this paper is introduced. The mathematical formulation for this problem is presented at the end of this section. In Section 4 we introduce the restoration technique that is used within both heuristic algorithms. In Section 5 computational experiments performed in this paper are presented. In this section we discuss how the restoration technique helps both local search algorithms to better explore the solution space. Finally, in Section 6 some conclusions are drawn and future work is outlined.
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