Chapter 6 - Parallel algorithms for solving rich vehicle routing problems

Abstract

In this chapter, we describe the most important parallelization techniques and cooperative search strategies for solving different variants of the Vehicle Routing Problems and Pickup and Delivery Problems. The algorithms used to solve various optimization problems must be usually very efficient and must give near-optimal solutions in very reasonable time. The exact methods are mostly used when the number of the customers in particular problems is relatively low, and thus the optimal solution can be found in acceptable time. The heuristics and metaheuristics approaches do not guarantee optimum but solutions with sufficient quality to meet problem objectives. In case problem size becomes larger, both parallelization approaches and cooperative strategies can significantly help reduce time required to get high-quality solutions. All parallelization methods are based on the idea of solving problem instances by several processes working simultaneously on some processors. The cooperative search strategies additionally contain some mechanisms to share and exchange data among processes while the search is in progress, which can significantly reduce computational time. In this chapter, we describe parallelism taxonomies, cooperative search techniques, parallel tabu search, parallel simulated annealing, parallel genetic and evolutionary algorithms, parallel ant colony and very efficient parallel memetic algorithms.