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 ...
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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 vehiclerouting 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.
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 congest...
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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.
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 t...
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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.
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 r...
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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.
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...
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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.
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 th...
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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.
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 ...
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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%.
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 rou...
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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 vehiclerouting 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.
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 betw...
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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.
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 Unc...
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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.
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