A Particle Swarm Optimization for the vehicle routing problem

Choosak Pornsing, University of Rhode Island


This dissertation is a study on the use of swarm methods for optimization, and is divided into three main parts. In the first part, two novel swarm meta-heuristic algorithms — named Survival Sub-swarms Adaptive Particle Swarm Optimization (SSS-APSO) and Survival Sub-swarms Adaptive Particle Swarm Optimization with velocity-line bouncing (SSS-APSO-vb) — are developed. These new algorithms present self-adaptive inertia weight and time-varying adaptive swarm topology techniques. The objective of these new approaches is to avoid premature convergence by executing the exploration and exploitation stages simultaneously. Although proposed PSOs are fundamentally based on commonly modeled behaviors of swarming creatures, the novelty is that the whole swarm may divide into many sub-swarms in order to find a good source of food or to flee from predators. This behavior allows the particles to disperse through the search space (diversification) and the sub-swarm with the worst performance dies out while that the best performance grows by producing offspring. The tendency of an individual particle to avoid collision with other particles by means of simple neighborhood rules is retained in this algorithm. Numerical experiments show that the new approaches outperform other competitive algorithms by providing the best solutions on a suite of standard test problem with a much higher consistency than the algorithms compared. ^ In the second part, the SSS-APSO-vb is used to solve the capacitated vehicle routing problem (CVRP). To do so, two new solution representations — the continuous and the discrete versions — are presented. The computational experiments are conducted based on the well-known benchmark data sets and compared to two notable PSO-based algorithms from literature. The results show that the proposed methods outperform the competitive PSO-based algorithms. The continuous PSO works well with the small-size benchmark problems (the number of customers is less than 75), while the discrete PSO yields the best solutions with the large-size benchmark problem (the number of customers is more than 75). The effectiveness of the proposed methods is enhanced by the strength mechanism of the SSS-APSO-vb, the search ability of the controllable noisy-fitness evaluation, and the powerful but cheapest cost of the common local improvement methods. ^ In the third part, a particular reverse logistics problem — the partitioned vehicle of a multi commodity recyclables collection problem — is solved by a variant of PSO, named Hybrid PSO-LR. The problem is formulated as the generalized assignment problem (GAP) in which is solved in three phases: (i) construction of a cost allocation matrix, (ii) solving an assignment problem, and (iii) sequencing customers within routes. The performance of the proposed method is tested on randomly generated problems and compared to PSO approaches (sequential and parallel) and a sweep method. Numerical experiments show that Hybrid PSO-LR is effective and efficient for the partitioned vehicle routing of a multi commodity recyclables collection problem. This part also shows that the PSO enhances the LR by providing exceptional lower bounds.^

Subject Area

Engineering, Industrial|Operations Research

Recommended Citation

Choosak Pornsing, "A Particle Swarm Optimization for the vehicle routing problem" (2014). Dissertations and Master's Theses (Campus Access). Paper AAI3619475.