Efficient Vehicle Routing Research Articles

Heuristic-Based Hybrid Algorithm for Value Stream Design with Milk-Run Approach

In this study, a heuristic algorithm has been developed for the Milk-Run related to the vehicle routing problem. It aimed to supply in the right place and at the right time in a short time with the internal logistics system approach. Since the proposed problem formulation is NP-hard, we suggested a heuristic-based hybrid genetic algorithm method to solve the problem. Real life problem is solved with a milk run approach inspired by vehicle routing problems. Firstly the model was developed with mixed integer linear programming then the problem was solved with the proposed hybrid genetic algorithm. The aim reduce the total transportation cost in the network and the number of vehicles required by using an efficient vehicle routing strategy. It explains the change in the existing distribution and collection systems of a logistics service company. The response of variables such as time, weight, volume, and pallet was measured under various scenarios with cost and time savings by applying Milk-Run optimization. The deterministic model and the proposed heuristic algorithm compared the previews and outputs of the paths. Accordingly, 30% and 50% discounts were made on restrictions for six different scenarios.

Energy efficient waste collection vehicle routing system using time series prediction

Energy efficient waste collection vehicle routing system using time series prediction

How to achieve fair and efficient cooperative vehicle routing?

A cooperative is a business entity with the primary objective of providing benefits, services, and goods to its members, who both own and exercise democratic control over it. In the context of a cooperative, a fleet typically consists of vehicles owned by self-concerned individually rational owners who prioritize their own efficiency and the fairness of the system. This fairness refers to how their individual gain aligns with the gain of others. In this paper, we focus on the routing of such cooperative fleets. Considering only the fleet’s efficiency in terms of minimising its overall cost, the studied problem corresponds to the multiple Traveling Salesman Problem (mTSP). However, our interest lies in finding both efficient and fair solutions, so we propose two new variants of this problem that integrate and maximise the fleet’s egalitarian and elitist social welfare. Additionally, to enhance the balance between fleet efficiency and fairness, we propose the systematic elitist and systematic egalitarian social welfare optimisation algorithm. Through simulation results, we observe a wide diversity of routes depending on the approach considered. Therefore, a cooperative may choose a model that best balances its fleet’s efficiency and fairness based on its specific requirements.

Dynamic route optimization for IoT based intelligent waste collection vehicle routing system

Efficient waste management has been a major problem for municipalities in urban communities. The one important task is to find the optimum route for the waste collection vehicle with the optimum cost of transportation, reduced emissions, and improved city image. The proposed waste management system consists of three major parts, Smart Bins, Smart vehicles, and a cloud network for communication. The smart bin is embedded with sensors to measure the level of garbage in the bin. On overshooting the overflow threshold, the bins become active in the network and send signals to the server through the cloud network. The active bins are clustered based on their geographical locations. A dynamic optimum path is generated for each vehicle within each cluster. The research proposes a clustering-based efficient waste collection vehicle routing system. For Dynamic path optimization, a weighted multiple heuristic-based Optimum A*(Op-A*) algorithm is proposed which utilizes heuristics like distance, traffic, road quality, fuel status, the waste amount in the smart bin, and vehicle load capacity. This research focuses on implementing and testing the proposed Op-A* algorithm with a synthetic dataset. The results are compared with existing state-of-art algorithms. The improved result generated by the proposed algorithm demonstrates the significantly outperformed state-of-the-art benchmarks from the literature in terms of total transportation cost and path selection time for the waste collection vehicle routing system.

AI-based efficient wireless technologies and infrastructure-based networks with VANET for smart transportation high performance

VANETs (Vehicular Ad-hoc NETworks) were deemed the most suitable communication network for supporting the dissemination of alert messages due to their low dissemination delays as well as extensive vehicle coverage in vicinity of an emergency. With the introduction of cooperative ITS services, it is envisaged that emerging vehicular networks will progressively rely on Vehicle to Infrastructure (V2I) communication lines, which are expected to be nominally accessible with certain temporary as well as time-limited connectivity losses. This study proposes a novel method for VANET-based efficient vehicle clustering and routing based on network infrastructure for high-performance smart transportation. The vehicle clustering is using infrastructure-based fuzzy K-means convolutional neural networks. Then the energy-efficient cluster-based multi-hop distributed routing has been carried out. The experimental analysis in terms of latency, network lifetime, throughput, QoS, energy efficiency, and packet delivery ratio. In addition, empirical equations that can be used to predict speed recommendations for drivers are derived from the result.

A Novel Two-Phase Approach for Optimization of the Last-Mile Delivery Problem with Service Options

As the growth of e-commerce continues to accelerate, there is a need for new and innovative strategies in last-mile delivery to meet the changing demands of customers. The main objective of this study is to address this need by optimizing the last-mile delivery problem with service options (LMDPSOs) through a novel two-phase approach that considers various delivery options such as home delivery, self-pickup, and delivery at different prices. This original approach enables simultaneous optimization of the selection of the most appropriate pickup and delivery points and determination of the most efficient vehicle routing. The LMDPSOs reduces overall costs, minimizes environmental impact, and considers customer satisfaction levels by determining the most appropriate trips according to the available service options. This research employs a two-phase methodology for decision making. The first phase determines the value of the proposed locations through a novel multi-criteria decision-making (MCDM) approach that incorporates sustainability criteria. In the second phase, a tailored mathematical model is proposed for vehicle routing with service options. The model is coded in the CPLEXsoftware version 12.6 in various dimensions. We evaluate the potential and advantages of diverse delivery choices, illustrating that aggregating orders at pickup and delivery points can reduce delivery costs and minimize environmental impact. Additionally, this paper directs managers in selecting the most appropriate delivery method for last-mile delivery, considering environmental, social, and economic factors.

Efficient Vehicle Routing Solutions: A Metaheuristic Approach

Efficient vehicle directing is fundamental for smoothing out strategies processes, cutting costs, and raising consumer satisfaction. The current study explores the usage of metaheuristic procedures to handle multifaceted vehicle directing issues that emerge in various fields, including public transportation frameworks, transportation logistics, and delivery services. The study is explicitly worried about the application and investigation of a modified version of Ant Colony Optimization (ACO), a metaheuristic algorithm that draws motivation from ant foraging behavior. To increment solution quality, robustness, and convergence speed, the modified ACO algorithm incorporates enhancements like multi-colony systems, local search heuristics, and dynamic pheromone update instruments. This examination shows the adequacy and adaptability of the recommended approach in delivering ideal or almost ideal routing arrangements through mathematical modeling, theoretical analysis, and empirical assessments utilizing real-world datasets. The outcomes add to the corpus of information on optimization algorithms and give valuable exhortation to working on the manageability and proficiency of vehicle routing operations.

Sustainable vehicle routing problem on real-time roads: the restrictive inheritance-based heuristic algorithm

• This article aims to present an efficient vehicle routing algorithm for a real-time routing solution. • The recursive routing process reflects the vehicle's energy consumption mechanism and the effect of vehicle speeds on CO2 emissions. • The restrictive inheritance strategy and loop termination condition prevent high memory usage, redundancy, and infinite negative loop, leading to high computational efficiency and a Pareto-optimal solution. • The suggested method contributes to reducing energy consumption and CO2 emission and shows robustness in reflecting ever-changing road conditions. Road transportation is a substantial contributor to urban greenhouse gas (GHG) emissions. This article focuses on the energy-efficient vehicle routing problem, given the relationship between a vehicle's energy consumption and CO2 emission. Extensive studies have established the energy dissipation model considering road gradients, traffic flow, and road congestion, and the route searching process entails algorithm application. However, despite the efforts to overcome the trade-off between computational efficiency and solution quality, the probabilistic and randomized-based routing algorithms would be trapped into the local optima or compromise computational efficiency. Hence, this article aims to propose an efficient vehicle routing algorithm in the sustainable transportation domain. The method derives a Pareto-optimal solution in an acceptable execution time; furthermore, the embedded cost function includes energy dissipation and recuperation mechanism in hilly regions. For a real-world application, this article refers to road gradients and a real-time traffic API. The findings showed the amelioration of CO2 emissions and computational loads, thus corroborating the pragmatic aspect. The eco-oriented route decision connotes a practical foundation of sustainability and contributes to future studies in this promising field.

Optimization of Humanitarian Aid Distribution in Case of an Earthquake and Tsunami in the City of Iquique, Chile

In this paper, we introduce, model, and solve a clustered resource allocation and routing problem for humanitarian aid distribution in the event of an earthquake and subsequent tsunami. First, for the preparedness stage, we build a set of clusters to identify, classify, sort, focus, and prioritize the aid distribution. The clusters are built with k-means method and a modified version of the capacitated p-median model. Each cluster has a set of beneficiaries and candidate delivery aid points. Second, vehicle routes are strategically determined to visit the clusters for the response stage. A mixed integer linear programming model is presented to determine efficient vehicle routes, minimizing the aid distribution times. A vulnerability index is added to our model to prioritize aid distribution. A case study is solved for the city of Iquique, Chile.

Study on food sampling routing system based on traveling salesman problem

The issue of preserving fresh food during the transportation is one of the long-standing issues for food distributors. On the other hand, the issue of vehicle routing is one of the most important issues in distribution management. Affecting the quality of food is the distribution process, which reduces the distribution time through vehicle routing plays an important role in reducing food spoilage. Therefore, in this study, the problem of vehicle routing in the distribution of perishable food materials is investigated. The time is different so that the distance is minimized and the capacities and time windows are not violated. This research aims to find facilities, identify the size of each cargo, and establish the most efficient vehicle routes to reduce overall costs at three levels in the event of a system failure. Undoubtedly, the traveling salesman issue is one of the most famous issues in the field of artificial intelligence.

Routing on-road heavy vehicles for alleviating greenhouse gas emissions

The increasing demand for freight transport leads to rising fuel consumption and additional greenhouse gas (GHG) emissions. This study aims to evaluate the impact of route optimization of heavy vehicles on fuel consumption and GHG emissions. The regular routes of two piloted trucks were extracted and compared with the optimized distances from the simulated model by Network Analyst in Arc GIS. By using Network Analyst and combining it with the Fuel Consumption Rate (FCR) and GHG emission, this study takes a step forward to quantify the amount of fuel usage and GHG emitted from heavy vehicles in the optimized routes versus their regular paths. For this purpose, two different trucks of Blacktown City (Australia) were selected: a waste collector and a tree maintenance truck. The result of the study shows that using the optimized route instead of the regular one can reduce the distance driven by tree maintenance truck by 60%, fuel consumption by 62%, and GHG emissions by 62% per month. In addition, the optimized route shows a 10% reduction in distance travelled, 11% in fuel consumption, and 10% in GHG emission per month. This study also compares fixed-mission and changing-mission trucks. . The results in this part show that the fixed mission truck is much more efficient. Therefore, implementing any efficient vehicle routing system will have considerable impacts on the changing-mission trucks.

Identifying the Optimal Packing and Routing to Improve Last-Mile Delivery Using Cargo Bicycles

Efficient vehicle routing is a major concern for any supply chain, especially when dealing with last-mile deliveries in highly urbanized areas. In this paper problems considering last-mile delivery in areas with the restrictions of motorized traffic are described and different types of cargo bikes are reviewed. The paper describes methods developed in order to solve a combination of problems for cargo bicycle logistics, including efficient packing, routing and load-dependent speed constraints. Proposed models apply mathematical descriptions of problems, including the Knapsack Problem, Traveling Salesman Problem and Traveling Thief Problem. Based on synthetically generated data, we study the efficiency of the proposed algorithms. Models described in this paper are implemented in Python programming language and will be further developed and used for solving the problems of electric cargo bikes’ routing under real-world conditions.

Sustainable vehicle routing problem for coordinated solid waste management

The quick growth of urbanization and population, as well as the transformation of industrial and materials, have pushed the management of municipal solid waste into a crisis especially for developing markets based on the grand challenge of sustainable development. The compounding complexity of the multiple objectives and dynamic problem constraints required to represent coordinated solid waste management (CSWM) problem in practice is a hugely significant issue for vehicle routing problem studies. The purpose is to introduce a new coordinated framework for a practical and efficient vehicle routing problem considering the triple bottom line of sustainability. The CSWM multiple objective functions applied in this study incorporate financial, environmental and social considerations to develop a sustainable vehicle routing problem considering heterogeneous vehicle fleets operating across a multi-echelon logistics network with the optimization goals. An entirely novel development and application of the adaptive memory social engineering optimizer (AMSEO) is introduced and is shown to perform significantly better than the simulated annealing (SA) as well as the social engineering optimizer (SEO) itself. Finally, the potential overall waste disposal cost savings achievable through increased recycling (revealed by framing the logistics problem across several echelons) is of particular significance. The main findings are the practical solutions with the use of sustainability goals for the CSWM and further application and development of the AMSEO in the routing optimization.

An online cost allocation model for horizontal supply chains

Emerging technologies in cargo shipping delivery have provided a way to facilitate horizontal cooperation in the transportation of goods to reduce the shipping cost of the cooperating firms, but an open question in this cooperation is how to allocate the costs fairly among the participants. In this paper, we focus on routing in real time a fleet of capacitated vehicles to satisfy requests submitted by a set of customers with some of the requests unknown while assigning the service cost fairly among the requested customers. We propose a Hybrid Proportional Online Cost-Sharing (HPOCS) mechanism to tackle the cost-sharing problem and analyze its performance using simulation instances. Although HPOCS does satisfy the desirable properties, namely online fairness, budget balance, immediate response, individual rationality and ex-post incentive compatibility, it has a few drawbacks in certain scenarios. Therefore, we make two extensions to HPOCS: 1) we introduce the idea of discounts to encourage customers to request in advance to facilitate efficient vehicle routing; 2) we incorporate periodical re-optimization within the dynamic vehicle routing framework. Experimental analysis are made in both extensions to see the tradeoff between the performance and the loss of certain desirable properties.

Effectiveness and Efficiency of Solid Waste Services in Lagos State, Nigeria

Lagos is the largest producer of municipal solid waste in Nigeria, yet it is the smallest of Nigeria’s 36 states with the highest projected population, highest level of urbanization and it houses Nigeria’s largest chief port, and principal economic and cultural centres. Out of the total waste generated, only about 73 percent actually got to the final disposal site. There is a possibility of inefficient and ineffective municipal solid waste management system in place which can damage the ecosystem services, contaminate ground and surface water, creates greenhouse gas emissions, increase disease transmission, damages ecosystem services and discourages tourism and other businesses. To this end, this study analysed the efficiency and effectiveness of solid waste services and; the efficiency and effectiveness of solid waste services in Lagos State using primary data and conducting statistical tests. The study found a general agreement on what constitutes efficient and effective solid waste services. However, the study concluded that the waste management system in Lagos State is inefficient, ineffective and therefore; unsustainable. The study therefore recommends among others the urgent need to re-organize the waste management institutions for effective and efficient performance. This requiressubstantial planning ability, appropriate equipment and continuous managerial optimization of vehicles and workers productivity,efficient vehicle routing, better designed vehicles, managerial incentives, faster vehicle repairs, vehicle standardization.

Fuzzy logic-based emergency vehicle routing: An IoT system development for smart city applications

Reducing the travel time of an Emergency Vehicle (EV) is essential to increase the chance of casualty’s survival. An efficient vehicle routing solution can avoid instantaneous road blockages such as construction works, strikes, and accidents. This paper proposes an Internet of Things (IoT) network model for an EV routing using fuzzy logic-based data fusion technique. The data fusion process estimates the location-specific congestion by considering sensory data and human inputs from the crowd. Further, an open source routing engine (Open Source Routing Machine (OSRM)) computes the shortest congestion-aware route by responding to live traffic updates in a road network. Moreover, a sensor node has been designed for gathering the emissions and speeds of moving vehicles on the road. An Android application is developed to collect road blockage information from the crowd. Leveraging the Android application service, a driver in the EV is guided towards a medical center through the shortest congestion-aware route.

Flexible Assignment of Loading Bays for Efficient Vehicle Routing in Urban Last Mile Delivery

Urban freight deliveries are often subject to many access restrictions which creates the need to establish a system of loading bays and to split the last mile delivery into driving and walking parts. A new model based on hard and soft clustering approach is developed to solve the loading bay assignment problem for efficient vehicle routing and walking in last mile delivery. The flexibility of the model is provided by the soft clustering approach based on different membership degrees of customers to loading bays. Especially for instances with large numbers of loading bays, soft clustering seems to give better results, it leads to higher flexibility of city logistics systems, minimal driving distances, and adequately short walking paths, which contribute to the goal of reaching sustainable urban freight deliveries.

The value of information sharing for platform-based collaborative vehicle routing

Cooperation is important in order to find efficient vehicle routing solutions for the growing transportation market. Increasingly, platforms emerge as facilitators for this kind of collaborative transportation. However, individual actors connected to a platform might refuse to share (parts of) their information due to reasons of competition. Though the need for realistic information sharing models is widely acknowledged by transportation researchers and practitioners, the precise value of such information is mostly unknown. We investigate the quality of solutions that can be obtained when different types and levels of carrier data are available. We consider an auction-based Multi-Agent System to solve large-scale, dynamic pickup and delivery problems, and vary whether carriers’ positions or route plans are available, and whether carriers are fully cooperative or more competitive in placing their bids by sharing or hiding their marginal costs. In total, we evaluate 9 different information sharing policies. The availability of vehicle position and route plan information turns out to be important for decreasing total route costs, and sharing marginal costs has a positive impact on service level. We provide detailed insights into trade-offs of carriers’ confidentiality concerns and a range of system performance objectives (service level, travel costs, and carrier profits) under different circumstances (various numbers of auctions, penalties for rejected orders, emission or congestion fees, and different problem characteristics). Based on these results, platform providers can stimulate sharing of certain information to improve the total system efficiency.

Supply planning for shelters and emergency management crews

This paper addresses the problem of supplying provisions to civilians affected by an emergency and to the intervention groups that provide post emergency relief. We define the Emergency Supply using Heterogeneous Fleet Problem (ESHFP) using a Mixed Integer Linear Programming mathematical model that describes the complexities involved in these operations. Furthermore, we propose a novel heuristic algorithm which constructs a plan comprising a set of efficient vehicle routes in order to minimize the total supply time, respecting constraints concerning timing, demand, capacity and supply. The characteristics of the problem have been studied by solving an extensive set of test cases. The efficiency and practicality of the algorithm has been tested by applying it to a large scale ESHFP instance and to a case study involving a forest fire in the Province of Teruel, Spain.

Search for optimal routes on roads applying metaheuristic algorithms

Abstract The design of efficient routes for vehicles visiting a significant number of destinations is a critical factor for the competitiveness of many companies. The design of such routes is known as the vehicle routing problem. Indeed, efficient vehicle routing is one of the most studied problems in the areas of logistics and combinatorial optimization. The present study presents a memetic algorithm that evolves using a mechanism inspired by virus mutations. Additionally, the algorithm uses Taboo Search as an intensification mechanism.