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

OPTIMIZING VEHICLE ROUTING WITH A HYBRID SWARM-INTELLIGENT FROG JUMPING OPTIMIZATION ALGORITHM

The issues in Vehicle Routing with Time Windows (VR-TW) are addressed in this study using a novel hybrid swarm-intelligent frog jumping optimisation (HSIFJO) algorithm. The method employs a diversity management strategy for developing memeplexes, which assists in preserving diversity and prevents the premature termination of the search. To increase population diversity and improve solution quality, an enhanced clone selection (CS) process is employed. To maximise the algorithm's potential, an enhanced and extended extremal optimisation (EO) strategy is used, coupled with different move operators. A proposed adaptive soft time windows (ASTW) surcharge approach acknowledges the possibility of impractical solutions during the evolution process. When compared to existing state-of-the-art heuristics, the suggested approach performs exceptionally well in performance evaluation

Optimization strategies for the integrated management of perishable supply chains: A literature review

Purpose: The main purpose of this article is to systematically review the papers published in the period 2005-2020 about the integration of production, inventory and distribution activities in perishable supply chains. Design/methodology/approach: The proposed research methodology is based on several steps. First, database and keywords are selected, with the aim to search and collect the main papers, dealing with the integration of production, inventory, distribution activities in perishable supply chains. Then, a bibliometric analysis is carried out, to detect: the main publishing sources, the chronological distribution, the most used keywords, the featured authors, about the selected papers. A five-dimension classification framework is proposed to carry out a content analysis, where the papers of the literature review are classified and discussed, according to: supply chain structure, objective, perishability type, solution approach, approach validation. Findings: Interest in the application of optimization models for integrated decision-making along perishable supply chains is strongly growing. Integrating multiple stages of the supply chain into a single framework is complex, especially when referring to perishable products. The vast majority of the problems addressed are then NP-Hard. Only a limited quantity of the selected papers aims to solve real-life case studies. There is a need for further research, which is capable of modeling and quantitatively improving existing supply chains. The potentials of Industry 4.0 are currently little explored. Originality/value: Based on the analysis of the papers published, this article outlines the current state of the art on the optimization strategies for the integrated management of perishable supply chains, which are very complex to be managed. Research trends and gaps are discussed, future challenges are presentedPeer Reviewe

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Optimizing Fresh Agricultural Product Distribution Paths Under Demand Uncertainty

Consumers' demand for fresh agricultural products (FAPs) and their quality requirements are increasing in the current agricultural-product consumption market. FAPs' unique perishability and short shelf-life features mean a high level of delivery efficiency is required to ensure their freshness and quality. However, consumers' demand for FAPs is contingent and geographically dispersed. Therefore, the conflicting relationship between the costs associated with the logistics distribution and the level of delivery quality is important to consider. In this paper, the authors consider a fresh agricultural-product distribution path planning problem with time windows (FAPDPPPTW). To address the FAPDPPPTW under demand uncertainty, a mixed-integer linear programming model based on robust optimization is proposed. Moreover, a particle swarm optimization algorithm combined with a variable neighborhood search is designed to solve the proposed mathematical model. The numerical experiment results show the robustness and fast convergence of the algorithm.</p

A review of recent advances in the operations research literature on the green routing problem and its variants

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community

FOOD DELIVERY BASED ON PSO ALGORITHM AND GOOGLE MAPS

This article presents a solution to deal with the optimization of delivery routes problem for a mobile application focused on the restaurant sector, by using a bioinspired algorithm (PSO) to minimize delivery costs, maximize a greater number of deliveries and recommend an optional route for food delivery. Different computational experiments are carried out by using Google Maps (API) for showing the best delivery route. The results obtained are very promising for offering a good delivery service

Investigating the effect of carbon tax and carbon quota policy to achieve low carbon logistics operations

Developing a low-carbon economy and reducing carbon dioxide emission have become a consensus for both academics and practitioners. However, the existing literature did not pay enough attention in interrogating the impacts of Carbon Tax (CT) and Carbon Quota (CQ) policy on distribution costs and carbon dioxide emission in the field of vehicle routing problem. Moreover, the investigated subsidies factor is also incomplete. This research stands on the position of the company to study the impact of CT and CQ policy on aforementioned two aspects. A mathematical model is developed to achieve the best low carbon vehicle routing under the optimal policy. The optimization goal of this research is to minimize the total cost that includes vehicle-using, transportation, CT, CQ, and raw material subsidy costs. An improved optimization algorithm, namely Genetic Algorithm-Tabu Search (GA-TS), is proposed to solve a given business case. In the simulation experiments, GA-TS and a traditional GA are compared and the results show the advantage of GA-TS on reducing the total cost and carbon dioxide emission. Furthermore, the experiments also explore the total cost and carbon dioxide emission under three scenarios (Benchmark, CT and CQ), incorporating four policies: CT, Carbon Tax Subsidy (CTS), CQ, and Carbon Quota Subsidy (CQS). It is concluded that CQS is the ideal policy to minimize distribution cost and carbon dioxide emission. In addition, the impact of vehicles’ capacities on the total cost and carbon dioxide emission is also analyzed in this research. This research also aimed at assisting practitioners in better formulating delivery routes, as well as policy makers in developing carbon policies. Finally, the limitations and the future research directions of this research are also discussed

Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

Vehicle Routing Problem in Cold Chain Logistics: a Joint Distribution Model with Carbon Trading Mechanisms

Fierce competition and the mandate for green development have driven cold chain logistics companies to minimize total distribution costs and carbon emissions to gain a competitive advantage and achieve sustainable development. However, the cold chain logistics literature considers carbon trading mechanisms in sharing economy, namely the joint distribution, is limited. Our research builds a Joint Distribution-Green Vehicle Routing Problem (JD-GVRP) model, in which cold chain logistics companies collaborate among each other to deliver cold chain commodities by considering carbon tax policy. Based on the real business data from four cold chain companies and 28 customers, a simulated annealing (SA) algorithm is applied to optimize the model. The results indicate that joint distribution is an effective way to reduce total costs and carbon emissions when compared with the single distribution. The total cost is positively correlated with the carbon price, while the carbon emissions vary differently when the carbon price increases. In addition, carbon quotas have no effect on the delivery path. This research expands cold chain logistics literature by linking it with joint distribution and carbon trading mechanisms. Moreover, this research suggests that cold chain logistics companies could enhance delivery efficiency, reduce the business cost, and improve competitiveness by reinforcing the collaboration at the industry level. Furthermore, the government should advocate the mode of joint distribution and formulate an effective carbon trading policy to better utilize social and industrial resources to achieve the balanced economic and environmental benefits