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

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

Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows

The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to reduce the cost of moving goods between several destinations while accommodating constraints like set time windows for certain locations and vehicle capacity. Applications of the VRPTW problem in the real world include Supply Chain Management (SCM) and logistic dispatching, both of which are crucial to the economy and are expanding quickly as work habits change. Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle Swarm Optimization (PSO) have been found to work effectively, however, they can experience premature convergence. To lower the risk of PSO's premature convergence, the authors have solved VRPTW in this paper utilising a novel form of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate that RWPSO is competitive with other state-of-the-art algorithms from the literature. Also, comparisons with two cutting-edge algorithms from the literature show how competitive the suggested algorithm is

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Particle Swarm Optimization in Solving Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-Hard problem, which means it is impossible to find a polynomial time solution for it. So researchers try to reach a near optimum solution by using meta-heuristic algorithms. The aim of CVRP is to find optimum route for every vehicle and a sequence of customers, that vehicle serve. This paper proposes a method on how PSO is adjusted for a discrete space problem like CVRP. The process of tweaking solutions is described in detail. At last for evaluation of proposed approach and show the effectiveness of it, the result of running proposed approach over benchmarking data set of capacitated vehicle routing problem is illustrated

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

Sustainable maritime crude oil transportation: a split pickup and split delivery problem with time windows

This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method

Survey on Ten Years of Multi-Depot Vehicle Routing Problems: Mathematical Models, Solution Methods and Real-Life Applications

A crucial practical issue encountered in logistics management is the circulation of final products from depots to end-user customers. When routing and scheduling systems are improved, they will not only improve customer satisfaction but also increase the capacity to serve a large number of customers minimizing time. On the assumption that there is only one depot, the key issue of distribution is generally identified and formulated as VRP standing for Vehicle Routing Problem. In case, a company having more than one depot, the suggested VRP is most unlikely to work out. In view of resolving this limitation and proposing alternatives, VRP with multiple depots and multi-depot MDVRP have been a focus of this paper. Carrying out a comprehensive analytical literature survey of past ten years on cost-effective Multi-Depot Vehicle Routing is the main aim of this research. Therefore, the current status of the MDVRP along with its future developments is reviewed at length in the paper

Particle Swarm Optimization in Solving Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-Hard problem, which means it is impossible to find a polynomial time solution for it. So researchers try to reach a near optimum solution by using meta-heuristic algorithms. The aim of CVRP is to find optimum route for every vehicle and a sequence of customers, that vehicle serve. This paper proposes a method on how PSO is adjusted for a discrete space problem like CVRP. The process of tweaking solutions is described in detail. At last for evaluation of proposed approach and show the effectiveness of it, the result of running proposed approach over benchmarking data set of capacitated vehicle routing problem is illustrated