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vehicle routing problem for cold-chain drug distribution with epidemic spread situation

EXPERT SYSTEMS WITH APPLICATIONS 2025年 262卷

作者： Zhang, Jie Li, Yanfeng Chongqing Jiaotong Univ Sch Traff & Transportat Chongqing 400074 Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China

We investigate a vehicle routing problem considering the influence of epidemic spread (VRP-ES) for the design of a novel cold-chain drug distribution system, in which the disease spread model is used to capture virus transmission characteristics and demand fluctuations. To this end, we aim to minimize the total travel time and transmission risk of the distribution network by incorporating realistic features including priority distribution and temperature control. We propose a hybrid tabu search heuristic (HTS) with a specifically designed initial solution, several neighborhood operators, and diversification strategies to solve this problem. A series of numerical experiments are conducted to test the proposed solution methodology. The virus spread model and vehicle routing results are discussed to analyze the VRP-ES optimization strategies through an empirical case study of Chongqing city in China. Sensitivity analysis is conducted to identify the impact of various parameters on the VRP-ES and provide some management implications as well.

关键词： Epidemic spread model Temperature control Drug distribution vehicle routing problem Hybrid tabu search

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vehicle routing problem for cold chain logistics based on data fusion technology to predict travel time

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OPERATIONAL RESEARCH 2024年第4期24卷 55页

作者： Bai, Qinyang Yuan, Yuxiang Fu, Xueqin Zhou, Zhili Xi An Jiao Tong Univ Sch Management Xian Peoples R China Xidian Univ Sch Econ & management Xian Peoples R China Nankai Univ Zhou Enlai Sch governemnt Tianjin Peoples R China

Cold chain logistics requires low-temperature transportation, which consumes more energy and has higher distribution costs than ordinary logistics. Moreover, as the scale of cities continues to expand, traffic congestion is becoming more frequent. Therefore, it is particularly important to plan the distribution route reasonably. In this paper, we study the problem of cold chain logistics vehicle path planning based on travel time prediction. First of all, multiple connected routes with real-time changes in traffic conditions between customers in the road network were considered to describe the distribution scene. Second, a genetic algorithm-optimized backpropagation algorithm fused travel time predictions for road segments based on fixed detector technology and floating car technology to improve the accuracy of road segment travel time prediction. Then, based on the prediction of road segment travel time, a method for predicting the travel time of the route is proposed, and the actual road network is transformed into a travel time network for each customer. Finally, the vehicle routing problem in cold chain logistics was investigated using predicted travel time as input. This problem is formulated as a bi-objective model aimed at minimizing costs and carbon emissions. To address this problem, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was proposed. The study provides support for cold chain logistics distribution companies to develop distribution strategies based on local environmental policies and their own operational conditions.

关键词： Cold chain Logistics vehicle routing problem Data fusion technology Travel time prediction GA-BP NSGA-II

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vehicle routing problem in Sustainable Horticulture Supply Chain for Food Security Enhancement: a Case Study

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2024年第5期8卷 1439-1463页

作者： Faraji, Negin Mohammadnazari, Zahra Rabbani, Masoud Aghsami, Amir Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran Iran Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry CV1 5FB England K N Toosi Univ Technol KNTU Sch Ind Engn Tehran Iran

The horticulture industry has a special role in the health of society due to its direct impact on the food security in society, so it is one of the important industries that affect the lives of all people. Also, due to the increase in the growth of the world population, the need for food is increasing day by day, so it is necessary to pay special attention to the horticulture industry to reduce the rate of hunger in the world, which causes various problems. This study focuses on the routing of vehicles to carry different kinds of horticultural products considering their shelf life to keep the stability and quality of these products at the highest level. This aim has been achieved through presenting a comprehensive multi-period MILP model for different types of horticultural products and various types of vehicles in the horticultural supply chain. A case study in European Union is also presented to analyze the efficacy of model. The outcome of this research could be proliferative for managers, decision makers, and policy designers of horticulture supply chain since it proffers avenues of sustainable production enhancement.

关键词： Horticulture industry Food security Environmental protection vehicle routing problem Location allocation Sustainability Shelf life

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vehicle routing problem with roaming locations for simultaneous pickup and delivery services

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OPSEARCH 2025年 1-21页

作者： Liao, Shirong Dai, Ying Ma, Zujun Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Zhejiang Univ Finance & Econ Sch Management Hangzhou Peoples R China

To improve delivery efficiency and reduce delivery costs in last-mile delivery, this study explores a vehicle routing problem in which the courier provides simultaneous pickup and delivery services to customers with roaming locations. The problem is formulated as a mixed integer linear programming model to minimize the total travel cost. We then develop a two-stage metaheuristic that combines a random selection greedy insertion algorithm and a large neighborhood search algorithm. The computational results show that our algorithm has certain advantages in solution quality and computational time compared to the commercial optimization solver. Moreover, incorporating simultaneous pickup and delivery into the vehicle routing problem with roaming delivery locations can significantly reduce the total delivery cost as the number of customers increases.

关键词： vehicle routing problem Simultaneous pickup and delivery Roaming locations Metaheuristic

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vehicle routing problem with uniform private fleet and common carrier: a node subset heuristic

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2022年第2期30卷 683-697页

作者： Pelikan, Jan Stourac, Petr Sokol, Ondrej Prague Univ Econ & Business Dept Econometr Nam W Churchilla 1938-4 Prague Czech Republic

Optimizing the distribution of goods to customers is discussed on non-split delivery modification of vehicle routing problem with uniform private fleet and common carrier. While private fleet costs are proportional to the sum of distances traveled by its vehicles, common carrier has no capacity limit and costs are proportional to the quantity of transported goods only. We show the transformation of the model onto the vehicle routing problem with optional enter and propose a modified insert heuristic. The main contribution is a node subset heuristic based on dividing nodes into two subsets. The heuristic uses the node pre-selection for the private fleet while the rest is served by the common carrier. In the second step, both subsets are solved separately. The performance of the integer linear program and both proposed heuristics are compared on testing instances. Both heuristics can be used for finding the initial solution which can be further improved by local search methods.

关键词： vehicle routing problem Integer programming Insert heuristic Non-split delivery model

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Solving the combined flexible job shop scheduling and vehicle routing problem with stochastic features

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JOURNAL OF SIMULATION 2025年第1期19卷 1-23页

作者： Torres-Tapia, William Montoya-Torres, Jairo R. Belmokhtar-Berraf, Sana Ruiz-Meza, Jose Univ La Sabana Sch Engn Km 7 Autopista Norte Bogot Chia DC Colombia Ecole Technol Super Dept Genie Syst Montreal PQ Canada Univ Gustave Eiffel COSYS GRETTIA ESIEE Paris Champs Sur Marne France Hub Innovat Caribe SAS Direcc Logist Innovac Sincelejo Sucre Colombia

Today's competitive market conditions forces companies to implement strategies for the integration of production and transportation activities, but this is studied little in the literature. This paper considers the combined production and transportation problem in which the production system is defined as a flexible jobshop (FJS) and the transportation stage is modelled as a vehicle routing problem (VRP). Stochastic production processing times and vehicle travel times are considered. A simheuristic solution procedure is proposed, as a class of simulation-optimisation approach, based on Ant Colony Optimisation (production stage) and an iterative local search (transportation stage). Randomised data sets are used to evaluate the performance of the proposed solution procedure, with metrics such as total production time and delivery time demonstrating its effectiveness. Experimental outputs show the impact of considering stochasticity of these two parameters for better decision-making. Compared to existing methods, our approach offers significant improvements in efficiency and reliability.

关键词： Production-distribution problem flexible job-shop vehicle routing problem simulation-optimisation stochastic

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Learning-based column generation approach for the vehicle routing problem with release dates and incompatible loading constraints

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COMPUTERS & OPERATIONS RESEARCH 2025年 183卷

作者： Gao, Mujin Chen, Yanru Zhang, Zongcheng Wahab, M. I. M. Taiyuan Inst Technol Dept Econ & Management 31 Xinlan Rd Taiyuan 030008 Peoples R China Southwest Jiaotong Univ Sch Econ & Management 1111st SectNorthern 2nd Ring Rd Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Key Lab Serv Sci & Innovat Sichuan Prov 1111st SectNorthern 2nd Ring Rd Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist 1111st SectNorthern 2nd Ring Rd Chengdu 610031 Sichuan Peoples R China Toronto Metropolitan Univ Dept Mech Ind & Mechatron Engn 350 Victoria St Toronto ON M5B2K3 Canada

This study introduces a variant of classical distribution problems, vehicle routing problems with release dates, and incompatible loading constraints (VRPR-ILC). The VRPR-ILC is derived from the practical application of a pharmaceutical distribution company based in China. It incorporates various real-world constraints, including release dates, product weights and volumes, incompatible loading, and time windows. The objective of the VRPR-ILC is to minimize the total travel distance. This variant can also find applications in diverse domains, such as e-commerce. Integrating the above constraints introduces the challenge of optimizing in both the temporal and spatial dimensions. To tackle this issue, we propose a learning-based column generation (LCG) approach. The LGG provides a new framework combining the deep learning (DL) technique with the column generation (CG) algorithm. By utilizing DL, the LCG effectively guides the CG in concentrating on the search space containing high-quality integer solutions. It helps to narrow the gap between linear and integer solutions and significantly enhances the convergence of the algorithm. Additionally, to address the challenges posed by the pricing problem of the VRPR-ILC, we develop the heuristic pricing, the dummy label dominance rule, and a lower bound evaluation strategy for labels. Computational results show that the LCG achieves competitive results compared with the GUROBI solver, the existing exact algorithm, and the heuristic algorithm. The results further indicate that utilizing the DL leads to improved solutions while reducing the time by 20%.

关键词： vehicle routing problem Release dates Incompatible loading Column generation Deep learning

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A reliability-extended simheuristic for the sustainable vehicle routing problem with stochastic travel times and demands

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JOURNAL OF HEURISTICS 2025年第2期31卷 1-39页

作者： Abdullahi, Hassana Reyes-Rubiano, Lorena Ouelhadj, Djamila Faulin, Javier Juan, Angel A. Univ Portsmouth Ctr Operat Res & Logist Lion Terrace Portsmouth PO1 3HF Hants England Cardiff Metropolitan Univ Cardiff Sch Management Western Ave Cardiff CF5 2YB Wales Univ Publ Navarra Inst Smart Cities Dept Stat Comp Sci & Math Campus Arrosadia Pamplona 31006 Spain Univ Politecn Valencia Dept Appl Stat & Operat Res Plaza Ferrandiz & Carbonell S-N Valencia 03801 Spain Chair Data & Business Analyt Kackertstr 10 D-52072 Aachen North Rhine-Wes Germany

Real-life transport operations are often subject to uncertainties in travel time or customers' demands. Additionally, these uncertainties greatly impact the economic, environmental, and social costs of vehicle routing plans. Thus, analysing the sustainability costs of transportation activities and reliability in the presence of uncertainties is essential for decision makers. Accordingly, this paper addresses the Sustainable vehicle routing problem with Stochastic Travel times and Demands. This paper proposes a novel weighted stochastic recourse model that models travel time and demand uncertainties. To solve this challenging problem, we propose an extended simheuristic that integrates reliability analysis to evaluate the reliability of the generated solutions in the presence of uncertainties. An extensive set of computational experiments is carried out to illustrate the potential of the proposed approach and analyse the influence of stochastic components on the different sustainability dimensions.

关键词： Transportation Uncertainty modelling Hybrid metaheuristics Simheuristics vehicle routing problem Reliability analysis

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Autonomous delivery vehicle routing problem with drones based on multiple delivery modes

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COMPUTERS & OPERATIONS RESEARCH 2025年 179卷

作者： Kong, Jili Wang, Hao Xie, Minhui Beijing Univ Posts & Telecommun Sch Econ & Management Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Intelligent Engn & Automat Beijing Peoples R China

Autonomous delivery vehicles (ADVs) and drones have gained widespread attention in the last-mile delivery due to their efficiency, environmental sustainability, and convenience. Moreover, the cooperative delivery between ADVs and drones is very complex, and most of the existing studies are focused on the cooperative delivery between trucks and drones in a single delivery mode. In contrast, this paper introduces a new vehicle routing problem for an unmanned delivery system consisting of ADVs and heterogeneous drones based on multiple delivery modes. A mixed integer programming (MIP) model is constructed for the autonomous delivery vehicle routing problem with drones based on multiple delivery modes (ADVRPD-MDM) with the objective of minimizing cost. We design a randomized variable neighborhood search (RVNS) algorithm that incorporates 12 specific neighborhood structures, a random variable neighborhood descent (RVND) mechanism and a random shaking strategy to solve the model. We evaluate the application effects of each operator and verify the effectiveness of the RVNS algorithm by the improved Solomon instances. Furthermore, when compared to the large neighborhood search (LNS) algorithm in 56 instances, the RVNS algorithm demonstrates an average improvement of 3.86% in its lowest solution, thereby confirming its superior performance. Through a series of experiments, it has been observed that the integration of collaborative drones and parallel drones within the unmanned delivery system can effectively reduce the cost. The results of the sensitivity analysis demonstrate that factors such as the multi-visit capability, the utilization of multiple drones, the high payload capacity, the long endurance, and the rapid charging rate are critical in reducing the cost. Finally, we verify through a case study that the unmanned delivery system with the ADV as carrier offers cost advantages compared to those employing trucks.

关键词： vehicle routing problem Multiple delivery modes Unmanned delivery system vehicle-drone system Variable neighborhood search

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DISTRIBUTIONALLY ROBUST OPTIMIZATION OF THE vehicle routing problem WITH UNCERTAIN CUSTOMERS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第3期21卷 1983-2006页

作者： Wang, Xiaopeng Zhao, Jinling Univ Sci & Technol Beijing Sch Math & Phys 30 Xueyuan Rd Beijing 100083 Peoples R China

With the rise of e-commerce, the express logistics industry has been developing rapidly, and the vehicle routing problem (VRP) has been widely concerned. In this paper, we focus on the vehicle routing problem with Uncertain Customers (VRPUC), which introduces the uncertain customers set on the basis of the capacitated vehicle routing problem, where the customers demands are uncertain. We first filter out potential uncertain customers using historical data, and then introduce a distributionally robust optimization model based on the cross-moment ambiguity set that minimizes the Essential Riskiness Index (ERI) of all vehicles, which can help mitigate the magnitude and probability of vehicle overload. The model can be reformulated as a mixed integer semidefinite optimization problem. We employ a branch-and-cut algorithm to tackle a relaxed version of this problem. Once the optimal solution is identified, a callback procedure is initiated to verify whether the current optimal solution meets the positive semidefinite constraint. If it does, the solution is confirmed as optimal for the original problem. If not, a lazy constraint is introduced to exclude the solution from the feasible region. Numerical experiments demonstrate that our model outperforms stochastic programming models across various performance indicators.

关键词： vehicle routing problem uncertain demands distributionally robust optimization essential riskiness index mixed integer semidefinite optimization

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