Search

The development of sharing economy and new information technologies has promoted the emergence of third-party shared manufacturing platforms (TPSMPs). In the shared manufacturing context, one main challenge to TPSMPs is the matching of manufacturing enterprises with insufficient production capacity (capacity demanders) and those with overcapacity (capacity suppliers), where available capacities of capacity suppliers are within time ranges, and orders of capacity demanders can be split. In this capacity matching problem with capacity time windows and order splitting (CMPCTW-OS), each capacity demander's order needs to be delivered on time, while each capacity supplier can also match with multiple capacity demanders and fulfil orders of the capacity demanders by sequence. A mathematical model for the CMPCTW-OS is developed to maximise the total profit of the TPSMP. Then, we design a two-stage heuristic algorithm to solve this model. In the first stage, the inserting algorithm (IA) is used to obtain an initial feasible solution. In the second stage, the iterated local search (ILS) is applied to optimise and improve the initial feasible solution. Finally, in numerical simulation experiments, the effectiveness of IA-ILS has been verified by comparison with the GUROBI solver. [ABSTRACT FROM AUTHOR]

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent's cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs. [ABSTRACT FROM AUTHOR]

This study examines the complicated logistics optimization issue known as the vehicle routing problem for multi-product and multi-suppliers (VRP-MPMS), which deals with the effective routing of a fleet of vehicles to convey numerous items from multiple suppliers to a set of consumers. In this problem, products from various suppliers need to be delivered to different customers while considering vehicle capacity constraints, time windows, and minimizing transportation costs. We propose a hybrid approach that combines a generalized reduced gradient method to identify feasible regions with a feasible neighborhood search to achieve optimal or near-optimal solutions. The aim of the exact method is to get the region of feasible solution. Then we explore the region using feasible neighborhood search, to get an integer feasible optimal (suboptimal) solution. Computational experiments demonstrate that our model and method effectively reduce transportation costs while satisfying vehicle capacity constraints and relaxed time windows. Our findings provide a viable solution for improving logistics operations in real-world scenarios. [ABSTRACT FROM AUTHOR]

Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk. [ABSTRACT FROM AUTHOR]

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm. [ABSTRACT FROM AUTHOR]

With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints. [ABSTRACT FROM AUTHOR]

In this paper, we address the vehicle routing problem (VRP) with occasional drivers (ODs) and time windows (TWs). The problem (VRP-OD-TW) is an extension of the VRP-OD, where ODs serve customers within given TWs. Differently from the basic version of VRP-OD-TW, we assume that ODs not only accept single requests, but they can also serve bundles of requests. To deal with the bundle-to-driver assignment problem, an auction-based system has been designed; a company offers a set of bundles to the ODs, who bid for all the bundles they consider attractive. There is no limit on the number of bids a driver can place, but at most one bid per OD can be assigned to avoid infeasible workloads. This system could yield a large cost reduction for the company, but its success is strongly related to the bundles offered. Hence, determining bundles which are attractive for ODs and profitable for the company, becomes a crucial issue. We propose two different bundling strategies, which make use of a spatial-temporal representation of customers in a three-dimensional (3D) space. The former is based on the generation of 3D corridors, while the latter relies on 3D clustering techniques. Through extensive computational results, we show that the former technique outperforms the latter in terms of both solution quality and computational times and that both the approaches strongly outperform bundle generation techniques that neglect the temporal dimension and rely only on spatial information. [ABSTRACT FROM AUTHOR]

This paper investigates the vehicle routing problem faced by a third‐party logistics (3PL) provider that performs the last‐mile delivery service in omni‐channel retailing for a number of companies engaged in household appliances and furniture. In the problem, each company is associated with a set of warehouses, physical stores, and customers that provide or require various commodities. The request of a company is to transport commodities from the selected warehouses and stores to the customers. In addition, the pickup or delivery service at each location should be started in a predefined time window. Since the loading and unloading follow the last‐in‐first‐out fashion, the handling time incurred by additional operation is considered. The studied problem is formulated as a multicommodity pickup and delivery problem with time windows and handling time (MPDPTWH). MPDPTWH aims to satisfy the requests on the 3PL platform with the minimum total duration of the routes. We present a mixed integer programming formulation of the problem and propose a multirestart randomized tabu thresholding algorithm (MRTTA) as well as a memetic algorithm (MA) to solve this problem heuristically. Then, the performance of the mathematical model and the two heuristics is assessed over three sets of MPDPTWH instances. After this, we compare the proposed MRTTA and MA with algorithms from the literature on a related problem. Furthermore, we perform the sensitivity analysis in a case study by discussing the impact of picking cost and handling time on the solution, and give the managerial insights. [ABSTRACT FROM AUTHOR]

We introduce a variant of the cumulative scheduling problem (CuSP) characterized by continuous modes, time windows, and a criterion that involves safety margin maximization. The study of this variant is motivated by the Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies Horizon 2020 Project, which is devoted to the design of evacuation plans in the face of natural disasters and more specifically, wildfire. People and goods have to be transferred from endangered places to safe places, and evacuation planning consists of scheduling evacuee moves along precomputed paths under arc capacities and deadlines. The resulting model is relevant in other contexts, such as project or industrial process scheduling. We consider here several formulations of the continuous time-resource trade-off scheduling problem (CTRTP-TW) with a safety maximization objective. We establish a complete complexity characterization distinguishing polynomial and NP-hard special cases depending on key parameters. We show that the problem with fixed sequencing (i.e., with predetermined overlap or precedence relations between activities) is convex. We then show that the preemptive variant is polynomial, and we propose lower and upper bounds based on this relaxation. A flow-based mixed-integer linear programming formulation is presented, from which a branch-and-cut exact method and an insertion heuristic are derived. An exact dedicated branch-and-bound algorithm is also designed. Extensive computational experiments are carried out to compare the different approaches on evacuation planning instances and on general CTRTP-TW instances. The experiments also show the interest of the continuous model compared with a previously proposed discrete approximation. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was funded by the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange European Project 691161 GEO-SAFE (Geospatial based Environment for Optimisation Systems Addressing Fire Emergencie). This work has also been supported by ANITI, the Artificial and Natural Intelligence Toulouse Institute. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0142) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0142). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods. [ABSTRACT FROM AUTHOR]

Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative helicopter rescue routing problem with time window and limited survival time constraints, solving it using an improved iterative greedy (IIG) algorithm. In the proposed algorithm, a heuristic initialization strategy is designed to generate an efficient and feasible initial solution. Then, a feasible-first destruction-construction strategy is applied to enhance the algorithm's exploration ability. Next, a problem-specific local search strategy is developed to improve the algorithm's local search effectiveness. In addition, the simulated annealing (SA) method is integrated as an acceptance criterion to avoid the algorithm from getting trapped in local optima. Finally, to evaluate the efficacy of the proposed IIG, 56 instances were generated based on Solomon instances and used for simulation tests. A comparative analysis was conducted against six efficient algorithms from the existing studies. The experimental results demonstrate that the proposed algorithm performs well in solving the post-disaster rescue helicopter routing problem. [ABSTRACT FROM AUTHOR]

Objective: To compare the effectiveness of different tirofiban administration time windows in patients with no-reflow myocardial infarction (MI) during percutaneous coronary intervention (PCI). Methods: This single centre retrospective observational study included patients with no-reflow MI, undergoing PCI at the Hanyang Hospital affiliated to Wuhan University of Science and Technology from March 2020 to May 2023. All patients were administered tirofiban. Patients who received tirofiban with postinterventional thrombolysis in myocardial infarction (TIMI) flow ≥ 1 were grouped as Group-I, and patients who were directly given tirofiban through the guiding catheter without forward blood flow were grouped as Group-II. TIMI blood flow classification, levels of cardiac troponin T (cTnT) and creatine kinase isoenzyme MB (CK-MB), incidence of complications and major adverse cardiovascular events (MACE) in the two groups before and after the treatment were statistically analyzed. Results: A total of 156 patients were included in this study, including 79 patients in Group-I and 77 patients in Group- II. There was no significant difference in the baseline data between the two groups (P>0.05). After treatment, TIMI blood flow classification of the two groups improved and was significantly better in Group-I compared to Group-II (P<0.05). After treatment, levels of Serum cTnT and CK-MB in the two groups decreased, and were significantly lower in Group-I than in Group-II (P<0.05). There was no significant difference in the incidence of complications between Group-I (3.80%) and Group-II (6.49%) (P>0.05). The incidence of MACE in Group-I (3.80%) was lower than that in Group-II (12.99%) (P<0.05). Conclusions: Compared with the direct application of tirofiban, tirofiban given when TIMI Grade≥ 1 for patients with no-reflow MI during PCI can more effectively regulate the blood flow status of target vessels, reduce myocardial injury, and reduce the risk of MACE. [ABSTRACT FROM AUTHOR]

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Time-dependent vehicle routing problem with time windows (TDVRPTW) is a pivotal problem in logistics domain. In this study, a special case of TDVRPTW with temporal-spatial distance (TDVRPTW-TSD) is investigated, which objectives are to minimize the total travel time and maximize customer satisfaction while satisfying the vehicle capacity. To address it, an improved multiobjective evolutionary algorithm (IMOEA) is developed. In the proposed algorithm, a hybrid initialization strategy with two efficient heuristics considering temporal-spatial distance is designed to generate high-quality and diverse initial solutions. Then, two crossover operators are devised to broaden the exploration space. Moreover, an efficient local search heuristic combing the adaptive large neighborhood search (ALNS) and the variable neighborhood descent (VND) is developed to improve the exploration capability. Finally, detailed comparisons with several state-of-the-art algorithms are tested on a set of instances, which verify the efficiency and effectiveness of the proposed IMOEA.

The Inventory-Routing Problem (IRP) deals with the joint optimisation of inventory and the associated routing decisions. The IRP with time windows (IRPTW) considers time windows for the deliveries to the customers. Due to its importance and several real-world applications, in this paper, we develop an intricate solution algorithm for this problem. A combination of tools ranging from established groups of valid inequalities, pre-processing techniques, local search procedures, and a local branching algorithm is utilised to solve the IRPTW efficiently. We compare the performance of our algorithm on a benchmark set of instances and show how our solution algorithm provides promising results against a competing algorithm from the literature. Moreover, the results of our study provide an overview of the performance of several already proposed techniques and their integration in the literature. [ABSTRACT FROM AUTHOR]

The increasing demand for perishable food and the popularity of electric vehicles have promoted the integration research of perishable food delivery services and electric vehicles. Aiming at minimizing the total delivery cost, a new model is formulated for perishable food delivery problems by electric vehicles (PFDP-EV), which considers vehicle capacity constraints, travel time constraints, time window constraints, and so on. An improved particle swarm optimization with particle refactor operator (IPSO-PRO) is developed to solve the proposed model. For the IPSO-PRO, a particle refactor operator is designed to help reconstruct the unqualified particles, and an elite selection strategy and an adaptive weighted strategy are used to improve the performance. Then, extensive efforts are conducted to verify the proposed method. First, the parameters of IPSO-PRO are tuned based on the Taguchi method. Second, small-scale, medium-scale, and large-scale perishable food delivery instances (19 instances) are simulated to evaluate the performance, and the results show that IPSO-PRO achieves the best average gap of 0%. Finally, based on a simulation case, the result and sensitivity analysis are conducted to reveal insightful management insights, which provides decision support for perishable food delivery problems. [ABSTRACT FROM AUTHOR]

In manufacturing and service industries, transportation often faces uncertain conditions. While current research on the truck and trailer routing problem (TTRP) mostly uses deterministic methods, they fall short in addressing uncertainties in travel and service times. This study aims to improve TTRP models by incorporating randomness in travel and service durations and specific time windows, better mirroring real-world scenarios. The enhanced model uses the multipoint simulated annealing (M-SA) method for practical application. The study involves 144 benchmark instances across six levels, starting with generating feasible solutions, then refining them using M-SA. A stochastic programming model with recourse (SPR) was used for problem formulation. Sensitivity analysis assessed the impact of various parameters and compared solutions obtained from M-SA and the analysis, showing minimal differences and thus the effectiveness of the proposed algorithm in solving the stochastic TTRP. The paper concludes with suggestions for future research. [ABSTRACT FROM AUTHOR]

We introduce PyVRP, a Python package that implements hybrid genetic search in a state-of-the-art vehicle routing problem (VRP) solver. The package is designed for the VRP with time windows (VRPTW) but can be easily extended to support other VRP variants. PyVRP combines the flexibility of Python with the performance of C++ by implementing (only) performance-critical parts of the algorithm in C++ while being fully customizable at the Python level. PyVRP is a polished implementation of the algorithm that ranked first in the 2021 DIMACS VRPTW challenge and, after improvements, ranked first on the static variant of the EURO meets NeurIPS 2022 vehicle routing competition. The code follows good software engineering practices and is well documented and unit tested. PyVRP is freely available under the liberal MIT license. Through numerical experiments, we show that PyVRP achieves state-of-the-art results on the VRPTW and capacitated VRP. We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: Funding was provided by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0055) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0055). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. There is a video associated with this paper. Click here to view the Video Overview. To save the file, right click and choose "Save Link As" from the menu. [ABSTRACT FROM AUTHOR]

Attended home deliveries (AHDs) are characterized by dynamic customer acceptance and narrow customer‐specific delivery time windows. Both impede efficient routing and thus make AHDs very costly. In this article, we explore how established horizontal collaborative transportation planning methods can be adapted to render AHDs more efficient. The general idea is to enable request reallocation between multiple collaborating carriers after the order capture phase. We use an established centralized reallocation framework that allows participating carriers to submit delivery requests for reallocation. We extend this framework for AHD specifics such as the dynamic arrival of customer requests and information about delivery time windows. Using realistic instances based on the city of Vienna, we quantify the collaboration savings by solving the underlying routing and reallocation problems. We show that narrow time windows can lower the savings obtainable by the reallocation by up to 15%. Therefore, we suggest enhancing the decision processes of request selection and request bundling using information about delivery time windows. Our findings demonstrate that adapting methods of request selection and bundle generation to environments with narrow time windows can increase collaboration savings by up to 25% and 35%, respectively in comparison to methods that work well only when no time windows are imposed. [ABSTRACT FROM AUTHOR]

The shipper lane selection problem (SLSP) is determining which lanes are to be served by the shipper's vehicle fleet or outsourced to the carriers through auction. While the SLSP in previous studies assumes that each lane is associated with a set of discrete times at which it can be served, this study considers a generalized version assuming a lane service time window for each lane, which is named the SLSP with time windows (SLSPTW). The SLSPTW is formulated as a mixed integer linear programming model that minimizes the sum of the transportation and the service/setup costs incurred by the shipper to auction off the lanes served by the carriers. In the proposed two‐stage solution approach, the first stage is designed to generate quickly a set of possible solutions, the best of which is then verified by a decomposed model of the SLSPTW at the second stage. Besides, an iterative‐improvement mechanism for the proposed algorithm is adopted to achieve the efficiency of the solution quality improvement. The effectiveness and efficiency of the developed solution method is demonstrated by the conducted numerical experiments. [ABSTRACT FROM AUTHOR]

The aim of a Green Vehicle Routing Problem is to find an optimal route for alternative fuel vehicles to minimize the overall travelling distances while reducing energy consumption and CO2emissions. In this problem each vehicle handles a subset of customers/orders, leaving from and returning to the depot, with respect to maximum distance travelled, while minimizing energy consumptions. We proposed a time window‐based GVRP solution (GVRP‐TW) with exact routing approach. Wherein, each route serves a subset of customers/orders with minimum intermediate refuelling/recharges. To test our method, we conducted experiments using MATLAB software on a set of three reference problem specimens (C‐101, R‐101 and RC‐11). Our approach, evaluated on these benchmark problems, par exceeds in performance in relation to the existing VRP methods (like tabu search, variable neighbourhood search, and GRASP). Moreover, the proposed GVRP‐TW can be further optimized to solve other VRP problems with energy minimisations and less intermediate stops. [ABSTRACT FROM AUTHOR]

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs.

Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk.

With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints.

The article deals with the problem of increasing the productivity of trucks in the performance of international road freight transportation in Eastern Europe and the European Union. There are restrictions on cabotage transportation (domestic cabotage), as well as time limits for the execution of orders and penalties for the execution of the entire volume of transportation in this case in addition to the restrictions of the European Agreement E/ECE/TRANS/564. The task of optimizing the required number of vehicles, drivers/driving crews, and route configuration was formulated and solved by mathematical programming with time constraints. At the same time, a variable method of organizing the work of drivers on adjacent routes was applied, by which drivers/crews are not assigned to a specific vehicle but are changed after a certain number of work hours. This minimized the non-productive idling of trucks, ensured compliance with the work and rest regulations of drivers, and minimized truck mileage on routes. In contrast to known methods and research results, the problem is solved with a guaranteed achievement of the optimum in an acceptable search time. This result was achieved due to the appropriate formulation of the solution conditions. The results can be applied in the logistic planning of transport processes. [ABSTRACT FROM AUTHOR]

The traveling salesman problem with time windows (TSPTW) has wide practical applications in transportation and scheduling operations. We study the TSPTW in the deterministic case as well as under travel time uncertainty. We consider the three classical TSPTW variants with the objectives of minimizing cost, completion time, and tour duration. In addition, a new TSPTW variant that maximizes the minimum slack of a tour, i.e., the smallest time buffer between the arrival time and the end of the time window over all nodes visited on the tour, is introduced. We further address a variant of the TSPTW in which the arc travel times are uncertain. This problem is modeled by means of an uncertainty set, and the goal is to determine a tour that remains feasible in the worst case within a budget stipulating the sum of all travel time deviations from their nominal values. To solve all targeted problem variants, we develop a two-phase general variable neighborhood search (GVNS) that applies an efficient move evaluation approach within the local search. Extensive numerical experiments on benchmark instances from the literature show that our GVNS finds high-quality solutions that are competitive with those obtained by the state-of-the-art heuristics for all problem variants. [ABSTRACT FROM AUTHOR]

Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several uncertainties: future customer requests are unknown, final service plans are not yet determined, and when fulfillment is outsourced to a third party, the firm has limited control over routing procedures and eventual fulfillment times. Even when routing is performed in-house, time windows often do not receive explicit consideration. In this paper, we show how companies can communicate reliable and narrow time windows to customers in the face of arrival time uncertainty when time window decisions are decoupled from routing procedures. Under assumptions on the shape of arrival time distributions, our main result characterizes the optimal policy, identifying structure that reduces a high-dimensional stochastic nonlinear optimization problem to a root-finding problem in one dimension. The result inspires a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service. Our results show that time windows should be tailored to individual customers, time window sizes should be proportional to the service level, larger time windows should be assigned to earlier requests and smaller time windows to later requests, larger time windows should be assigned to customers further from the depot of operation and smaller time windows to closer customers, high quality time windows can be identified even with limited data, and cost savings afforded by routing efficiency should be measured against potential losses to customer convenience. Funding: M. W. Ulmer's work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Emmy Noether Programme, [project 444657906]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0004. [ABSTRACT FROM AUTHOR]

Addressed in this study is a vehicle routing problem with two‐dimensional loading constraints and time windows (2L‐CVRPTW), aiming to minimize the transportation cost while satisfying the two‐dimensional loading and routing constraints with time windows. To solve this problem, for the first time a mixed‐integer linear programming model is formulated with considering practical last‐in‐first‐out loading constraints, and a branch‐and‐price‐based (BP‐based) heuristic is proposed based on a set partitioning formulation. In the heuristic, a modified labeling algorithm is proposed for the complex pricing problem, which is a relaxation of the elementary shortest path problem with resource constraints and two‐dimensional loading constraints. Therein, an effective Tabu‐maximum open space packing heuristic is proposed to verify the feasibility of the two‐dimensional packing problem of each route generated by the labeling algorithm. In addition, effective accelerating and branching strategies are introduced to improve the solving efficiency of the heuristic. To evaluate the effectiveness and the advantages of the proposed heuristic, extensive computational experiments are performed based on the generated instances. The computational results show that the proposed BP‐based heuristic can effectively solve the 2L‐CVRPTW, in which the optimal solutions can be achieved much faster than CPLEX in small‐scale problems. Relationships between the transportation cost and the characteristics of the instances are analyzed. The stability of the algorithm and the effectiveness of the accelerating strategies are verified and discussed. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem is essential in logistics for optimizing customer routes, especially in timesensitive variants. This paper presents a two-stage algorithm for Vehicle Routing Problem with Time Windows. It effectively minimizes the number of vehicles, with transportation costs resulting just 0,38% above the best solution found on Solomon test instances. The approach limits search time to about 10 minutes, effectively balancing complexity and solution quality.

The availability of low-cost unmanned aerial vehicles (UAVs), or drones, has made their organisation in fleets more feasible. The required coordination for managing these fleets comes with an increased complexity. When used for long-durability, autonomous inspection missions, it is necessary to recharge the drones due to their limited battery capacity. By providing a set of nearby charging stations, the fleets can autonomously recharge and sustain indefinite missions. In order to reduce congestion at these charging stations, effective scheduling of charging cycles can have a significant impact on the mission execution time. In this paper, we propose a novel centralized method for scheduling charging time windows, taking into account the travel distances and occupation of charging stations. We formulate a mixed-integer linear program (MILP) model with two extensions to reduce the computational complexity. The solution to this problem assigns a set of charging windows to each drone, minimizing the mission execution time and ensuring batteries will not fully deplete. The performance of our proposed method is evaluated through a series of experiments, based on a discrete-event simulator. Our results reveal a clear benefit over a greedy approach, reducing the mission execution time by up to 39.8%. Through careful parameter selection, a trade-off between mission execution time and scheduling time can be found.

In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods.

Cross-Docking (CD) technique was initiated in the 1930s to make a cost-effective supply chain. Vehicle Routing Problem (VRP) is one of the widely discussed optimization problems. The research on integration of VRP with CD (VRPCD) was initiated at the beginning of 2000s. Moving Shipments (MS) from receiving doors to shipping doors is an activity inside a Cross-Dock Centre (CDC). This study mainly considers MS as an additional aspect in the literature of VRPCD. In this study, not only loading or unloading shipments at all the nodes including CDC and homogenous fleets of vehicles within pickup or delivery process are considered, but also aspects of heterogeneous fleets of vehicles between pickup and delivery processes are considered. Furthermore, Time Windows (TW) characteristics are also considered here. A mixed integer nonlinear programming model is developed to obtain the optimal solutions to hard time windows vehicle routing problem with moving shipments at the cross-dock centre (TW-VRPCD-MS). The compatibility of the proposed model is tested using sixteen randomly generated small scale instances. Since the average computational time is reasonably less for the tested instances, it can be concluded that this proposed model can be used for last time planning for similar small-scale problems. Further analysis revealed that the convergence rate to reach the optimal solution rises exponentially with the scale of the problem. Therefore, this study recommends in applying heuristic or metaheuristic techniques to solve large scale instances of TW-VRPCD-MS to obtain a near optimal solution in a reasonable computational time.

With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education management of urban rail transit students needs further reform. At the same time, digital information technology is widely used in various fields, and digital management of education has become one of the major development directions of education reform. The study proposes a specific construction path based on the analysis of the necessity of digital management of education for urban rail transportation majors, and then optimizes the K-medoids algorithm in the clustering algorithm and validates its education digital management effect. The outcomes show that the clustering precision of the upgraded K-medoids algorithm in the selected dataset is up to 92.68%, and the running time is all below 5s, with the lowest value being 3.9s; In the digital management of urban rail transit majors in universities, the precision obtained by the algorithm is all maintained at around 95%, and the satisfaction rate is all higher than 90%. The effectiveness of the proposed method has been verified, providing a new method for the management of digital education systems for urban rail transit students. It can better understand the needs and characteristics of students, help improve their learning effectiveness and educational quality, and achieve more targeted allocation of educational resources. [ABSTRACT FROM AUTHOR]

Scheduling drilling activities for oil and gas exploration involves solving a problem of optimal routing of a fleet of vehicles that represent drilling rigs. Given a set of sites in some geographic area and a certain number of wells to drill in each site, the problem asks to find routes for all the rigs, minimizing the total travel time and respecting the time windows constraints. It is allowed that the same site can be visited by many rigs until all the required wells are drilled. An essential part of the considered problem is the uncertain drilling time in each site due to geological characteristics that cannot be fully predicted. A mixed integer programming model and a parallel greedy algorithm proposed in an earlier study can be used for solving very small-sized instances. In this paper, a graphics processing unit (GPU) accelerated genetic algorithm is developed for using in the greedy algorithm as a subroutine. This approach was implemented and tested on a high-performance computing cluster and the experiments have shown good ability to solve large-scale problems. [ABSTRACT FROM AUTHOR]

As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows. [ABSTRACT FROM AUTHOR]

We propose a vehicle routing problem with time windows (VRPTW) with compatibility-matching constraints and total completion time as the objective function, with applications in home healthcare routing and scheduling. Mixed integer linear programming is provided with total completion time minimization as the objective function. The solution approach has two objectives, total completion time (primary objective) and total distance (secondary objective). A heuristic is proposed comprising three phases: initializing to find an initial feasible routing (inserting the procedure with a modified K-means algorithm), swapping and moving the procedure to find a local optimal routing, and shooting the procedure to move away from the local optimum. Proof of feasibility for the inserting procedure is provided to prevent unnecessary insertions. Phases 2 and 3 will be repeated as needed to ensure solution quality. Solving our model with the proposed heuristic algorithm increases the total distance by 90.00% but reduces the total completion time by 25.86%. To test our model and heuristic, we examined a system with 400 home-healthcare cases in Chiang Mai. The heuristic quickly solved the problem. When total completion time is minimized, some caretakers serve up to twice as many patients as their coworkers; when total distance is minimized, workload discrepancies can increase up to seven-fold. [ABSTRACT FROM AUTHOR]