A Comparative Study of the PSO and GA for the m-MDPDPTW

Abstract

Abstract:The m-MDPDPTW is the multi-vehicles, multi-depots pick-up and delivery problem with time windows. It is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers for the purpose of satisfying precedence, capacity and time constraints. This problem is a very important class of operational research, which is part of the category of NP-hard problems. Its resolution therefore requires the use of evolutionary algorithms such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO). We present, in this sense, a comparative study between two approaches based respectively on the GA and the PSO for the optimization of m-MDPDPTW. We propose, in this paper, a literature review of the Vehicle Routing Problem (VRP) and the Pick-up and Delivery Problem with Time Windows (PDPTW), present our approaches, whose objective is to give a satisfying solution to the m-MDPDPTW minimizing the total distance travelled. The performance of both approaches is evaluated using various sets instances from [10] PDPTW benchmark data problems. From our study, in the case of m-MDPDPTW problem, the proposed GA reached to better results compared with the PSO algorithm and can be considered the most appropriate model to solve our m-MDPDPTW problem.

A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems

“…The authors use self-generated test instances and compare two GA variants with different methods, such as hill-climbing, in their evaluation. A GA for Multi-Depot PDPTW with homogeneous vehicles is presented in [22]. The GA uses a path representation, i.e., the order of the customers is stored in the genotype.…”

“…Further, the service start time has to start within the specified time window [a i , b i ] of customer i (20). The time precedence relationship between pickup and delivery nodes is modeled using inequalities (21) through the service start at node i and i + n. Restrictions (22) ensure that the load L j is correctly updated after the customer i is visited. In addition, (23) guarantees that the capacity of the vehicle associated with the served customer i is not exceeded.…”

Vehicle Routing Problem with Soft Time Windows of Cargo Transport O2O Platforms

“…Tiwari et al [8] proved a modified environmental adaptation method can evade the incidence of being struck into local minima. Alaia et al [9] investigated a multi-vehicle, multidepot pickup and delivery problem with time windows and solved a benchmark example using a GA and PSO algorithm, and the results showed that the GA algorithm is better than the PSO algorithm. Jiang [10] used a dynamic clustering algorithm to solve the model that considered the order similarity and the picking time.…”

“…As the pickup point and delivery point presented a one-to-one corresponding relation in this study, first, the pickup points were randomly permutated and combined to form a chromosome during the coding process, the vehicles were inserted into multiple positions of the chromosome, and the delivery points were randomly inserted at each pickup point served by each vehicle. Taking five cargo owners and two vehicles as an example, first, a chromosome (2, 3, 5, 4, 1) was randomly generated, the vehicles were inserted into the chromosome to obtain (11,2,3,5,12,4,1), and the delivery points were inserted to acquire (11,2,3,7,8,5,10,12,4,9,1,6). Finally, the formed chromosome showed that the first vehicle provided the delivery service for cargo owners 2, 3, and 5, and the second vehicle provided the delivery service for cargo owners 1 and 4.…”

Meta‐heuristic optimization algorithm for predicting software defects

“…PSO begins from an initial swarm and utilizes cooperation to find the best solution where GA utilizes genetic operations. (Alaia et al, 2018) compared GA and PSO implementation in an optimization problem. They concluded that GA is the favourable approach although that it endures a few issues.…”