Vehicle Routing Based on Discrete Particle Swarm Optimization and Google Maps API

Transportation imposes considerable cost on goods and has a significant influence on competitive advantage of a company. How to reduce the costs and improve the profit of a company is an important issue. Vehicle routing is a critical factor in reducing transportation costs. Finding optimal vehicle routes offers great potential to efficiently manage fleets, reduce costs and improve service quality. An effective scheme to manage fleets and determine vehicle routes for delivering goods is important for carriers to survive. In the existing literature, a variety of vehicle routing problems (VRP) have been studied. However, most papers do not integrate with GIS. In this paper, we consider a variant of VRP called Vehicle Routing Problem with Arbitrary Pickup and Delivery points (VRPAPD). The goal of this paper is to develop an algorithm for VRPAPD based on Google Maps API. To achieve this goal, we propose an operation model and formulate an optimization problem. In our problem formulation, we consider a set of goods to be picked up and delivered. Each goods has a source address and a destination address. The vehicles to transport the goods have associated capacities, including the maximal weight a vehicle can be carried and the maximal distance a vehicle can travel. The problem is to minimize the routes for picking up and delivering goods. The emerging Google Maps API provides a convenient package to develop an effective vehicle routing system. In this paper, we develop a vehicle routing algorithm by combining a discrete particle swarm optimization (DPSO) method with Google Maps API. We illustrate the effectiveness of our algorithm by an example

Particle Swarm Optimization Algorithm to Solve Vehicle Routing Problem with Fuel Consumption Minimization

The Conventional Vehicle Routing Problem (VRP) has the objective function of minimizing the total vehicles’ traveling distance. Since the fuel cost is a relatively high component of transportation costs, in this study, the objective function of VRP has been extended by considering fuel consumption minimization in the situation wherein the loading weight and traveling time are restricted. Based on these assumptions, we proposed to extend the route division procedure proposed by Kuo and Wang [4] such that when one of the restrictions can not be met the routing division continues to create a new sub-route to find an acceptable solution. To solve the formulated problem, the Particle Swarm Optimization (PSO) algorithm is proposed to optimize the vehicle routing plan. The proposed methodology is validated by solving the problem by taking a particular day data from a bottled drinking water distribution company. It was revealed that the saving of at best 13% can be obtained from the actual routes applied by the company

Low Carbon Logistics Optimization for Multi-depot CVRP with Backhauls - Model and Solution

CVRP (Capacitated Vehicle Routing Problems) is the integrated optimization of VRP and Bin Packing Problem (BPP), which has far-reaching practical significance, because only by taking both loading and routing into consideration can we make sure the delivery route is the most economic and the items are completely and reasonably loaded into the vehicles. In this paper, the CVRP with backhauls from multiple depots is addressed from the low carbon perspective. The problem calls for the minimization of the carbon emissions of a fleet of vehicles needed for the delivery of the items demanded by the clients. The overall problem, denoted as 2L-MDCVRPB, is NP-hard and it is very difficult to get a good performance solution in practice. We propose a quantum-behaved particle swarm optimization (QPSO) and exploration heuristic local search algorithm (EHLSA) in order to solve this model. In addition, three groups of computational experiments based on well-known benchmark instances are carried out to test the efficiency and effectiveness of the proposed model and algorithm, thereby demonstrating that the proposed method takes a short computing time to generate high quality solutions. For some instances, our algorithm can obtain new better solutions

Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pick-Up Service Based on MCPSO

This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehicles and simultaneous delivery and pick-up service. Using minimization of the total transport costs as the objective of the extension VRPTW, a mathematic model is constructed. To solve the problem, an efficient multiswarm cooperative particle swarm optimization (MCPSO) algorithm is applied. And a new encoding method is proposed for the extension VRPTW. Finally, comparing with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the MCPSO algorithm performs best for solving this problem

A Fuzzy Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows using Improved PSO (Case Study)

Most studies on decision making issue have supposed the problem in deterministic environment, and because uncertainty makes the decisions taken suboptimal, so in this paper we propose a credibility based fuzzy model for the Vehicle Routing Problem with Simultaneous Delivery and Pickup and Time Windows (VRPSDPTW). The dispatching cost of vehicles and customers’ time windows are supposed to be trapezoidal fuzzy numbers. We also proposed a hybrid meta-heuristic algorithm called Improved Particle Swarm Optimization (IPSO) for solving the problem. The proposed algorithm is the combination of Particle Swarm Optimization (PSO) and some removal and insertion techniques which helps to improve the searching ability and maintain diversity of solutions. Finally, to demonstrate the applicability of the proposed model in the real world we studied the distribution of dairy products among customers by a distribution company in Fars province. The computational results show that distributors can use this method to reduce operating costs of the company

Investigating the Vehicle Routing Problem with Simultaneous Pickup and Delivery in Multi-Product Distribution: An Optimization Approach

This study addresses a method to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD), which carries multi-products in multiple compartments within a single-vehicle. The unique characteristics of the study is on the route determination of the vehicle from the depot to customers because not only does it consider the vehicle’s capacity but also the compartment capacity of each product as a limitation We calculate the set of instances using two customer grouping methods namely smallest maximum load (SML) and largest maximum load (LML). The solution obtained by the cheapest insertion method can be improved by the Tabu Search algorithm. Finally, the computational result is reported from the test instance

Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics

Systematic Literature Review Of Particle Swarm Optimization Implementation For Time-Dependent Vehicle Routing Problem

Time-dependent VRP (TDVRP) is one of the three VRP variants that have not been widely explored in research in the field of operational research, while Particle Swarm Optimization (PSO) is an optimization algorithm in the field of operational research that uses many variables in its application. There is much research conducted about TDVRP, but few of them discuss PSO's implementation. This article presented as a literature review which aimed to find a research gap about implementation of PSO to resolve TDVRP cases. The research was conducted in five stages. The first stage, a review protocol defined in the form of research questions and methods to perform the review. The second stage is references searching. The third stage is screening the search result. The fourth stage is extracting data from references based on research questions. The fifth stage is reporting the study literature results. The results obtained from the screening process were 37 eligible reference articles, from 172 search results articles. The results of extraction and analysis of 37 reference articles show that research on TDVRP discusses the duration of travel time between 2 locations. The route optimization parameter is determined from the cost of the trip, including the total distance traveled, the total travel time, the number of routes, and the number used vehicles. The datasets that are used in research consist of 2 types, real-world datasets and simulation datasets. Solomon Benchmark is a simulation dataset that is widely used in the case of TDVRP. Research on PSO in the TDVRP case is dominated by the discussion of modifications to determine random values of PSO variables