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Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features. In this study, we propose a holistic solving approach to this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering the exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of "Vallée de la Chimie" in Lyon city, France. Instances involving more than 50 transport requests and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with lower vehicle kilometers traveled compared to the constrained K-means-based reduction. Reductions in terms of GHG emissions are estimated to be around 75% less than the private vehicle mode in our applied service. A sensitivity analysis is also provided. [ABSTRACT FROM AUTHOR]

Green manufacturing has become an important research topic owing to the dominant role of the manufacturing industry in environmental conservation, global energy consumption, and carbon emissions. Job scheduling is an active research area that supports industrial development and transformation as a part of industrial manufacturing management. Scheduling and just-in-time (JIT) production are complementary concepts that can help organizations optimize their production processes and achieve their goals more efficiently. The objective of these concepts is to reduce waste by focusing on the timely delivery of products or services to meet customer demand without holding excess inventory or wasting resources. Early/tardy job scheduling aligns with the primary goals of JIT production. This study jointly considers the early/tardy scheduling problem and carbon-emission optimization. A speed-scaling strategy is applied, where a machine has the ability to process jobs at discrete machining speeds. A heuristic method based on a genetic algorithm is proposed to solve the above problem. The proposed algorithm integrates a normal boundary intersection to reinforce the generation of a Pareto optimal solution. Numerical experiments show that the proposed approach provides an optimal and satisfactory Pareto solution within a relatively short computational time. [ABSTRACT FROM AUTHOR]

Product distribution for companies in the industrial sector is an important issue as it is necessary to increase customer satisfaction and reduce costs. In this study, Nondominated-Sorting Genetic Algorithm II is used to solve Capacitated Vehicle Routing Problem in a case study dataset from the biggest fertilizer company in Indonesia. NSGA-II algorithm reduced the need for vehicles in product distribution with an advantage of 44.7%, while GWO algorithm yielded better results with an increase of time of 16.9%. [ABSTRACT FROM AUTHOR]

The Capacitated Vehicle Routing Problem (CVRP) is an optimization problem that involves arranging vehicle routes while considering vehicle capacity. This research aims to compare the effectiveness of several heuristic (Path Cheapest Arc, Path Most Constrained Arc, Savings, Christofides) and metaheuristic (Greedy Descent, Guided Local Search, Simulated Annealing, Tabu Search) algorithms for determining the routing scenarios and vehicle types for faculty transportation between the male campus in Ponorogo and the female campus in Mantingan Ngawi at Universitas Darussalam Gontor. The research involves decision variables for vehicle routing determination and the objective of minimizing the distance traveled. The constraint function includes two options: one vehicle with a capacity of 60 passengers and four vehicles. This research utilizes Google OR Tools with the Python programming language using Google Colab to facilitate the calculation process. The research results indicate that metaheuristic algorithms outperform heuristics for complex case studies (four vehicles). This study recommends using metaheuristic methods, specifically Christofides Guided Local Search and Christofides Simulated Annealing, for determining the best routes with the shortest distance and time. Further research was developed using algorithms such as hyperheuristics or matheuristics. [ABSTRACT FROM AUTHOR]

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. The same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. Therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. This problem is highly relevant, especially for large hospitals. The number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. The method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. The results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. The expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost. [ABSTRACT FROM AUTHOR]

The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE's nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line's tangent. The trajectory shape, then, depends only on the final position of the AV's mass center and the final reference's curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated. [ABSTRACT FROM AUTHOR]

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution. [ABSTRACT FROM AUTHOR]

An improved Aquila optimizer (IAO) algorithm is proposed for the time dependent vehicle routing problem with time-varying speed, and the travel time calculation method of the time dependent road network is analyzed. An Aquilacustomer (A-C) coding and decoding method is designed based on the characteristics of vehicle routing problem. Combining the four hunting methods of Aquila: expanding exploration, narrowing exploration range, expanding exploitation range and narrowing exploitation range, its intelligent search behavior is redefined, adaptive large neighborhood search (ALNS) strategy is introduced, and various neighborhood destroy operators and repair operators are designed. The inferior solution acceptance criterion is added to the algorithm, and two stagnation perturbation strategies, circular heuristic perturbation mechanism and elite perturbation mechanism, are proposed. Solomon benchmark and simulations based on Figliozzi test cases with genetic algorithms, particle swarm algorithms and ant colony algorithms demonstrate the optimization performance of the IAO algorithm, while the experimental results of real cases verify the superiority of the IAO algorithm in terms of convergence speed and solution quality. It is shown that IAO algorithm has the application value for solving time dependent vehicle routing problem. [ABSTRACT FROM AUTHOR]

The article considers methods for managing metrological support for a fleet of measuring instruments (MIs). The present situation is described where the MI fleet is heterogeneous and includes obsolete MIs with a long service life and significant operating time, as well as modern high-tech MIs. Of note is that the proportions of these MI groups change over time as MIs age and move from one group to another. Also, the MI fleet is renewed as a result of procuring new and modernizing existing instruments. The heterogeneity of the MI fleet leads to the need to develop and apply new methods for managing its metrological support, including the use of mathematical modeling. A promising method was proposed for managing metrological support for the MI fleet that relies on the risk-based approach (RBA). As a risk factor for the MI fleet, the authors adopted the probability of finding a randomly selected MI from the fleet in an unavailable state at a random point in time. As per the RBA, the MI fleet was divided into risk classes. An algorithm was developed for assigning MIs to different risk classes that is based on solving a series of problems of fleet operation optimization, taking into account its aging and renewal. The results are presented of RBA application in modeling metrological support for the heterogeneous MI fleet, including both modern and obsolete instruments having different metrological characteristics, operating time to failure, and life. It was established that by dividing all MIs into risk classes and verifying them in each risk class at rational (near optimal) intervals and with tolerance for the monitored parameters, it is possible to minimize the total average risk for such a fleet while conserving instrument life. [ABSTRACT FROM AUTHOR]

As the aging population grows, the demand for long-term continuously Attended Home Healthcare (AHH) services has increased significantly in recent years. AHH services are beneficial since they not only alleviate the pressure on hospital resources, but also provide more convenient care for patients. However, how to reasonably assign patients to doctors and arrange their visiting sequences is still a challenging task due to various complex factors such as heterogeneous doctors, skill-matching requirements, continuity of care, and uncertain travel and service times. Motivated by a practical problem faced by an AHH service provider, we investigate a deterministic continuity-skill-restricted scheduling and routing problem (CSRP) and its stochastic variant (SCSRP) to address these operational challenges. The problem is formulated as a heterogeneous site-dependent and consistent vehicle routing problem with time windows. However, there is not a compact model and a practically implementable exact algorithm in the literature to solve such a complicated problem. To fill this gap, we propose a branch-price-and-cut algorithm to solve the CSRP and a discrete-approximation-method adaption for the SCSRP. Extensive numerical experiments and a real case study verify the effectiveness and efficiency of the proposed algorithms and provide managerial insights for AHH service providers to achieve better performance. [ABSTRACT FROM AUTHOR]

This research introduces the Multi-Depot Waste Collection Vehicle Routing Problem with Time Windows and Self-Delivery Option (MDWCVRPTW-SDO). The problem comes from the waste bank operation implemented in Yogyakarta City, Indonesia. A set of vehicles is dispatched from the waste banks to pick up waste from residents' locations within the time windows specified by the residents. Residents may be compensated for delivering their waste to a waste bank by themselves. The objective of MDWCVRPTW-SDO is minimizing the sum of investment costs, routing costs, and total compensation paid to the residents. We model this problem as a mixed integer linear programming model and propose Simulated Annealing (SA) as an effective solution approach. Extensive computational experiments confirm that SA is effective to solve MDWCVRPTW-SDO. Moreover, the number of waste banks, compensation paid to residents, and the distribution of residents of each type are crucial for the success of the implementation. [ABSTRACT FROM AUTHOR]

In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles. [ABSTRACT FROM AUTHOR]

This paper presents complex variants of the vehicle routing problem: the Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints (3L-VRPTW) and the Split Delivery Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints (3L-SDVRPTW). The difference between the two problems is whether a customer can be visited in two or more tours. Under the conditions of satisfying customer demands and loading constraints, the 3L-VRPTW model and the 3L-SDVRPTW model are constructed with the aim of minimizing transportation costs. To efficiently solve the above problems, a two-layer method is proposed in this study, including a routing stage and a packing stage. The Adaptive Large Neighborhood Search algorithm based on the Metropolis criterion is used to obtain the vehicle routing. The genetic algorithm is used to solve the packing stage, ensuring that the goods are packed in a way that minimizes pre-movements. The proposed algorithms are tested on different instances, verifying their effectiveness. Additionally, numerical experiments are conducted using instances with different customer distributions. The results show that when customers are located in multiple small areas, split delivery it distribution can significantly reduce penalty costs and be more economical. [ABSTRACT FROM AUTHOR]

We study the optimal booking limits in a three-class static revenue management model with customers' buy-up behavior, where a given fraction of customers with rejected seat reservation requests in each period try to book in the following periods at higher fares. However, it is difficult to apply the backward recursion of the dynamic programming approach to this optimization problem, owing to the dependence of demands for each period. Specifically, for the three-class model with a buy-up factor $ \beta $ from the third to second period and another factor $ \alpha $ from the second to first period, we derive a set of two simultaneous equations for the optimal booking limits for the third and second periods in terms of multiple definite integrals involving distribution functions for the original demand in each period. Numerical examples are provided to illustrate the dependence of optimal booking limits on $ \alpha $ and $ \beta $. The maximized expected revenue increases as $ \alpha $ and/or $ \beta $ increase by reducing the optimal booking limits. [ABSTRACT FROM AUTHOR]

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents' paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches. [ABSTRACT FROM AUTHOR]

The joint consideration of hydrogen supply chain networks and electric power systems can promote hydrogen-fueled transportation and the penetration of renewable energy. This paper develops a low-carbon scheduling approach for an Integrated Hydrogen Transport and Energy System (IHTES). To reduce carbon emissions, the environmental cost from the hydrogen supply chain network and electric power system is jointly minimized with the operational cost of IHTES. To consider the timeliness of hydrogen deliveries, hydrogen transport via tube trailers is innovatively modeled as an extended vehicle routing problem with time windows, and delays are penalized. The scheduling problem is formulated as a mixed-integer linear programming problem so that it can be efficiently solved using the branch-and-cut method. Case studies illustrate and validate the proposed model, and demonstrate the computational efficiency of our approach. Carbon emissions are reduced by 47.8% when the emission cost is co-optimized with the operational cost. The operational cost of the hydrogen supply chain network is reduced by 7.3% when the timeliness of hydrogen deliveries is modeled. • Emission-operation optimization of Integrated Hydrogen Transport and Energy System. • The timeliness of hydrogen deliveries is modeled with delays penalized. • The scheduling problem is formulated as a mixed-integer linear programming problem. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is related to determining the route of several vehicles to distribute goods to customers efficiently and minimize transportation costs or optimize other objective functions. VRP variations will continue to emerge as manufacturing industry production distribution problems become increasingly complex. Meta-heuristic methods have emerged as a powerful solution to overcome the complexity of VRP. This article provides a comprehensive review of the use of meta-heuristic methods in solving VRP and the challenges faced. A review of popular meta-heuristic methods is presented, including Simulated Annealing, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. The advantages of each method in solving the VRP and its role in solving complex distribution problems are discussed in detail. Challenges that may be encountered in using meta-heuristics for VRPs are analyzed, along with strategies to overcome these challenges. This article also recommends further research that includes adaptation to more complex VRP variants, incorporation of meta-heuristic methods, parameter optimization, and practical implementation in real-world scenarios. Overall, this review explains the important role of meta-heuristic methods as intelligent solutions to increasingly complex distribution and logistics challenges. [ABSTRACT FROM AUTHOR]

Order picking is one of the most important activities in warehouse management. By optimising the picking process, warehouse operations can be managed efficiently in terms of both time and logistics costs. In this work we apply operations research techniques to support the order picking process of an Italian retail distribution company. The picking process poses several challenges from an optimisation point of view, and the related optimization problems are very complex. Therefore, we define an Adaptive Large Neighbourhood Search (ALNS) algorithm that heuristically solves the problem. The proposed metaheuristic is tested on a set of 101 real orders processed by the company within one day. The computational experiments show that the ALNS shows good performances in terms of effectiveness, compared to the optimal solution, as well as allows to implement a better organisation of the order picking process than the one currently adopted by the company. [ABSTRACT FROM AUTHOR]

Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and widely-adopted approach toward thorough verification and validation of such algorithms in practical scenarios. We tackle this issue and propose a comprehensive validation approach that can shed more light on functional and non-functional abilities of the solvers. Additionally, we propose novel similarity metrics to measure the distance between the routing schedules that can be used in verifying the convergence abilities of randomized techniques. To reflect practical aspects of intelligent transportation systems, we introduce an algorithm for elaborating solvable benchmark instances for any vehicle routing formulation, alongside the set of quality metrics that help quantify the real-life characteristics of the delivery systems, such as their profitability. The experiments prove the flexibility of our approach through utilizing it to the NP-hard pickup and delivery problem with time windows, and present the qualitative, quantitative, and statistical analysis scenarios which help understand the capabilities of the investigated techniques. We believe that our efforts will be a step toward the more critical and consistent evaluation of emerging vehicle routing (and other) solvers, and will allow the community to easier confront them, thus ultimately focus on the most promising research avenues that are determined in the quantifiable and traceable manner. [ABSTRACT FROM AUTHOR]

Online shopping has become a common and popular form of shopping, so online attackers try to extract money from customers by creating online shops whose purpose is to compel the buyer to disclose credit card details or to pay money for goods that are never delivered. Existing buyer protection methods are based on the analysis of the content of the online shop, customer reviews, the URL (Uniform Resource Locator) of the website, the search in blacklists or whitelists, or the combination of the above-mentioned methods. This study aims to find the minimal set of publicly and easily obtainable features to create high-precision classification solutions that require little computing and memory resources. We evaluate various combinations of 18 features that belong to three possible categories, namely URL-based, content-based, and third-party services-based. For this purpose, the custom dataset is created, and several machine learning models are applied for the detection of fraudulent online shops based on these combinations of features. The results of this study show that even only four of the most significant features allow one to achieve 0.9342 classification accuracy, while 0.9605 accuracy is reached with seven features, and the best accuracy of 0.9693 is achieved using thirteen and fifteen features. [ABSTRACT FROM AUTHOR]

Since accidents of hazardous chemicals transportation will cause serious loss to the surrounding environment and lives and properties, this paper studies the transportation route optimization problem of hazardous chemicals under dynamic time-varying conditions. Combined with the goal of green sustainable development, a multiobjective nonlinear optimization model is constructed to minimize the transportation risk, transportation cost, and carbon emissions generated in the transportation. The model is solved by the improved Fast Non-Dominated Sorting Genetic Algorithm with Elite Strategy (NSGA-II) algorithm. The effectiveness of the model and the algorithm are tested on the Sioux Falls network. The experimental results show that under time-varying conditions, a vehicle's departure at different times will generate different transportation costs and risks. Therefore, enterprises need to rationally arrange the departure time of vehicles according to the time windows of customer nodes and road conditions. In additio, from the relationship between the optimization objectives, in order to achieve green, sustainable and low-risk transportation, enterprises should first reduce their transportation costs. [ABSTRACT FROM AUTHOR]

To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. Finally, combined with the numerical example, the optimization scheme of the vehicle-drone joint distribution path considering priority is analyzed. Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. Compared with the results of the scheme that does not consider priority, the results show that priority can significantly improve customer satisfaction. The efficiency of the vehicle-drone joint distribution was verified by comparing the three scenarios. [ABSTRACT FROM AUTHOR]

The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO's hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm's performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam. [ABSTRACT FROM AUTHOR]

Typical path planning algorithms are good for static obstacles avoidance, but not for dynamic obstacles, so path planning of intelligent vehicles in uncharted regions is a fundamental and critical problem. This study suggests an improved D* Lite algorithm to address the issues of large corner, node redundancy and close to obstacles in the path planned by D* Lite algorithm. Firstly, in order to increase the safety of the path, the D* Lite algorithm sets the safety distance between the intelligent vehicle and obstacles. Then, the kinematic constraints of intelligent vehicles are introduced to increase the path search direction and avoid path corners exceeding the steering maneuverability of intelligent vehicles. Next, the path is optimized, and the optimization process of removing redundant points is employed to tackle the problem of curved search path and redundant nodes, and the path is smoothed by using third-order Bezier curve to generate a path with continuous curvature. Finally, the enhanced D* Lite algorithm is fused with the improved dynamic window approach to achieve real-time obstacle avoidance based on the global optimal path for moving obstacles. Simulation studies in static and dynamic contexts are used to demonstrate the usefulness of the revised D* Lite algorithm. The results show that compared with other path planning methods, the path generated by the proposed method has more safety and smoothness features, and improves the path quality. Therefore, the proposed algorithm has certain effectiveness and superiority in path planning problems in static and dynamic environments. [ABSTRACT FROM AUTHOR]

Supply chain management speeds up the delivery system and product flow towards customers. It is required to update the delivery system and fulfill the customers' needs. Still, Centralized delivery management approaches are very risky and unsatisfactory, so nowadays, decentralized models are more secure and customer friendly. However, delivering a particular customer in decentralized models is a rigorous task with anonymity or pseudonym. Therefore we propose a location-based decentralized delivery system that provides accurate delivery of products to customers. It is crucial for sellers, Buyers, and Transport service providers that the claimed location is trustworthy and publicly verifiable. The proof of location guarantees the correct geographic location, and the witnesses provide location confirmation to the prover. Further, We introduce an off-chain multiparty payment channel with a supply chain for quicker and more secure payment, and it is free from "flood and loot attacks" and intermediary insecurities. The proposed scheme is protected against malicious activities all involved entities attempt, and smart contracts solve the monopoly. The implementation result uses the ethereum network and shows the gas consumption of different smart contracts with execution costs. Finally, we proved that location verification, disputes solved and successful payment completion are much better than all existing models. [ABSTRACT FROM AUTHOR]

Due to the economic infeasibility of establishing comprehensive healthcare facilities in sparsely populated areas and the responsibility of governments to ensure equal healthcare access for citizens, different approaches have been sought in healthcare service delivery. The Republic of Turkiye's Ministry of Health provides mobile healthcare services (MHS) to 9 million people monthly through 3400 healthcare facilities and 7500 doctors. This study focuses on the Mobile Healthcare Service Routing and Scheduling Problem (MHS-RSP). The objective of MHS-RSP is to determine the daily routes of doctors on a monthly basis to minimize total distance traveled, taking into account constraints such as working hours, route time, minimum service time per visit, and dedicated doctor assignments to villages. A mixed-integer mathematical model is developed to address the problem, which is NP-hard and equivalent to the multi-depot time-constrained periodic vehicle routing problem. To solve medium- and large-sized problems efficiently, a hybrid metaheuristic algorithm named "GCH-RRT" is introduced, based on greedy constructive heuristic(GCH) and record-to-record travel(RRT) metaheuristic. The performance of the mathematical model and the GCH-RRT algorithm is compared with existing service plans in Ankara, resulting in a 15.6% improvement with the mathematical model and a 22.3% improvement with the GCH-RRT algorithm in total distance traveled. [ABSTRACT FROM AUTHOR]

In this study, the Mixed Fleet Electric Vehicle Routing Problem (MF-E-VRP) with Partial Recharging by Multiple Chargers is considered. It composes of electric and internal combustion vehicles. The travelled distance and carried load on vehicles are taken into account in the emission function of internal combustion vehicles and the energy consumption function of electric vehicles. Additionally, selection of one of the multiple charging configurations at the charging station is addressed for the first time in the MF-E-VRP. The problem can be described as obtaining vehicle routes that minimize the total cost while meeting customer delivery demands. First, we develop a mixed integer mathematical programming formulation for the KF-EARP. Because of the NP-hardness of the problem, we propose an insertion based constructive heuristic algorithm to solve medium- and large-size instances in reasonable computation times. Computational analysis indicates that in a very short computation time, i.e., around 1 second, the proposed insertion based constructive heuristic attains feasible solutions on all medium- and large-size benchmark instances which mathematical model fails within 2 hours. [ABSTRACT FROM AUTHOR]

The Location-Routing Problem (LRP) becomes a more intricate subject when the limits of capacities of vehicles and warehouses are considered, which is an NP-hard problem. Moreover, as the number of vehicles increases, the solution to LRP is exacerbated because of the complexity of transportation and the combination of routes. To solve the problem, this paper proposed a Discrete Assembly Combination-Delivery (DACA) strategy based on, the Binary Equilibrium Optimizer (BiEO) algorithm, in addition, this paper also proposes a mixed-integer linear programming model for the problem of this paper. Our primary objective is to address both the route optimization problem and the assembly group sum problem concurrently. Our BiEO algorithm was designed as discrete in decision space to meet the requirements of the LRP represented by the DACA strategy catering to the multi-vehicle LRP scenario. The efficacy of the BiEO algorithm with the DACA strategy is demonstrated. through empirical analysis utilizing authentic data from Changchun City, China, Remarkably, the experiments reveal that the BiEO algorithm outperforms conventional methods, specifically GA, PSO, and DE algorithms, resulting in reduced costs. Notably, the results show the DACA strategy enables the simultaneous optimization of the LRP and the vehicle routing problem (VRP), ultimately leading to cost reduction. This innovative algorithm proficiently tackles both the assembly group sum and route optimization problems intrinsic to multi-level LRP instances. [ABSTRACT FROM AUTHOR]

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances. [ABSTRACT FROM AUTHOR]

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach's efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved. [ABSTRACT FROM AUTHOR]

The body constitutes the largest proportion of the total vehicle weight. Recently, increasing efforts have been made towards reducing its weight and improving its crashworthiness. By reducing its weight, fuel consumption will be reduced, and this will also translate into lower CO2 emissions. In terms of safety, vehicle body components use high strength steel which can absorb a substantial amount of impact energy. The present study pays attention to DP 600 and TRIP 400/700 stress–strain characteristics at quasi-static strain rates. The stress–strain characteristics of absorption capacity, stiffness, and deformation resistance force were investigated experimentally by tensile tests, three-point bending tests, and numerical simulations. The results indicate the potential for increasing the absorption capacity, stiffness, and deformation resistance force of the vehicle body's deformable steel components. The present study verified the possibility of replacing physical testing with numerical simulation. A reasonably satisfactory agreement between the experimentally determined stress–strain characteristics and the numerical simulation was achieved, which can reduce the development time of deformable vehicle body components, reduce costs and optimize the selection of materials. The results extend the state of knowledge on the deformation characteristics of high-strength materials and contribute to the optimization of body components in terms of passive safety and weight. [ABSTRACT FROM AUTHOR]

The rise of distributed energy resources has empowered consumers to become prosumers, generating their own electricity. This trend has led to the development of peer‐to‐peer (P2P) energy trading, where P2P participants trade energy among themselves. Since the integration of P2P energy trading can influence retail markets in distribution systems, the focus is on its effects on retail markets. Therefore, this paper proposes a method to determine the maximum P2P penetration levels on distribution systems while considering the effects on the benefits of stakeholders, including P2P participants, retail customers, and distribution system operators. The proposed method also assesses the impact of P2P energy trading on two schemes: the impact on buy‐from‐grid prices for retail customers and the impact on social benefits. The analysis revealed three distinct zones of photovoltaic and P2P penetration: the P2P restricted zone, the P2P with curtailment zone, and the P2P free trading zone. By examining these zones, the maximum P2P penetration levels—which ensure that buy‐from‐grid prices and social benefits remain within acceptable thresholds—can be determined. This study enables DSOs to find the maximum P2P penetration level at which a balance of stakeholders' benefits can be achieved and non‐beneficial effects on retail customers do not occur. [ABSTRACT FROM AUTHOR]

Topical treatments for onychomycosis are of interest to those seeking to avoid systemic drug interactions and to improve systemic safety. This work aimed to develop aqueous-based, simple, and cost-effective vehicles that provide high solubility for ciclopirox and enable the delivery of an active through channels created by nail microporation. Following solubility tests, aqueous gels and thermogels based on hydroxypropylmethylcellulose and poloxamer 407, respectively, were loaded with 8% and 16% ciclopirox. Their performance was then compared to the marketed lacquer Micolamina® in in vitro release tests with artificial membranes and in in vitro permeation tests with human nail clippings with and without poration. Finally, a microbiological assay compared the best gel formulations and the reference product. Little correlation was observed between the in vitro release and the permeation data, and the drug release was highly membrane-dependent. Ciclopirox nail retention in single-dose, porated nails tests was larger than in daily-dosing, non-porated nail conditions. The series of new gel and thermogel vehicles delivered ciclopirox more effectively than Micolamina® in single-dose, porated nail experiments. The inhibition of Trichophyton rubrum activity was significantly increased with microporated nails when the gel formulations were applied but not with Micolamina®. Overall, the results suggest that the new vehicles could be successfully combined with nail microporation to improve the drug delivery and efficacy of topical antifungal medication while reducing the dosing frequency, facilitating patients' adherence. [ABSTRACT FROM AUTHOR]

Fast growing e-commerce has a significant impact both on CEP providers and public entities. While service providers have the first priority on factors such as costs and reliable service, both are increasingly focused on environmental effects, in the interest of company image and the inhabitants' health and comfort. Significant additional factors are traffic density, pollution, and noise. While in the past direct delivery with distribution trucks from regional depots to the customers might have been justified, this is no longer valid when taking the big and growing numbers into account. Several options are followed in the literature, especially variants that introduce an additional break in the distribution chain, like local mini-hubs, mobile distribution points, or Automated Parcel Lockers (APLs). The first two options imply a "very last mile" stage, e.g., by small electrical vehicles or cargo bikes, and APLs rely on the customers to operate the very last step. The usage of this schema will significantly depend on the density of the APLs and, thus, on the density of the population within quite small regions. The relationships between the different elements of these technologies and the potential customers are studied with respect to their impact on the above-mentioned factors. A variety of scenarios is investigated, covering different options for customer behaviors. As an additional important point, reported studies with APLs only consider the section up to the APLs and the implied CO2 emission. This, however, fully neglects the potentially very relevant pollution created by the customers when fetching their parcels from the APL. Therefore, in this paper this impact is systematically estimated via a simulation-based sensitivity analysis. It can be shown that taking this very last transport step into account in the calculation significantly changes the picture, especially within areas in outer city districts. [ABSTRACT FROM AUTHOR]

Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, "traditional" deterministic approaches do not adequately fulfil constantly evolving customer expectations. To maintain competitiveness, logistic service providers must adopt proactive and anticipatory systems that dynamically model and evaluate probable (future) events, i.e., stochastic information. These events manifest in problem characteristics such as customer requests, demands, travel times, parking availability, etc. The Stochastic Dynamic Vehicle Routing Problem (SDVRP) addresses the dynamic and stochastic information inherent in urban logistics. This paper aims to analyse the key concepts, challenges, and recent advancements and opportunities in the evolving urban logistics landscape and assess the evolution from classical VRPs, via DVRPs, to state-of-art SDVRPs. Further, coupled with non-reactive techniques, this paper provides an in-depth overview of cutting-edge model-based and model-free reactive solution approaches. Although potent, these approaches become restrictive due to the "curse of dimensionality". Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. In the scope of this research, we observed that addressing real-world SDVRPs with a comprehensive resolution encounters a set of challenges, emphasising a substantial gap in the research field that warrants further exploration. [ABSTRACT FROM AUTHOR]

The large amounts of greenhouse gases generated in the logistics and transportation industry worsen global climate deterioration, jeopardizing the human living environment. A multi-objective nonlinear planning model for vehicle routing problems (VRPs) was established by considering VRPs with carbon emission constraints to realize the shortest vehicle mileage and the minimum carbon emission. An improved simulated annealing algorithm was proposed to solve the model. The farthest insertion heuristic method, combined with a relocation operator, was applied in the reinsertion operation to determine the preferred insertion back to the customer point and the optimal insertion point. The customer point with the largest minimum target increment was removed from the removed customer points and inserted into the optimal insertion point until all of them were inserted. Results demonstrate that the proposed algorithm outperforms the common genetic algorithms and forbidden search algorithms in similar studies with strong global optimization search capability. The improved algorithm could solve low-carbon VRP with high sensitivity and effectiveness. [ABSTRACT FROM AUTHOR]

In the context of the Amazon Last-Mile Routing Research Challenge, this paper presents a machine-learning framework for optimizing last-mile delivery routes. Contrary to most routing problems where an objective function is clearly defined, in the real-world setting considered in the challenge, an objective is not explicitly specified and must be inferred from data. Leveraging techniques from machine learning and classical traveling salesman problem heuristics, we propose a "pool and select" algorithm to prescribe high-quality last-mile delivery sequences. In the pooling phase, we exploit structural knowledge acquired from data, such as common entry and exit regions observed in training routes. In the selection phase, we predict the scores of candidate sequences with a high-dimensional, pretrained, and regularized regression model. The score prediction model, which includes a large number of predictor variables such as sequence duration, compliance with time windows, earliness, lateness, and structural similarity to training data, displays good prediction accuracy and guides the selection of efficient delivery sequences. Overall, the framework is able to prescribe competitive delivery routes, as measured on out-of-sample routes across several data sets. Given that desired characteristics of high-quality sequences are learned and not assumed, the proposed framework is expected to generalize well to last-mile applications beyond those immediately foreseen in the challenge. Moreover, the method requires less than three seconds to prescribe a sequence given an instance and, thus, is suitable for very large-scale applications. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research was funded by The Dutch Research Council (NWO) Data2Move project under [Grant 628.009.013] and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [Grant 754462]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0029. [ABSTRACT FROM AUTHOR]

Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristics. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research was funded by the Netherlands Organization for Scientific Research under the City Logistics Living Laboratory project [Grant 439.18.424]. [ABSTRACT FROM AUTHOR]

Last-mile routing refers to the final step in a supply chain, delivering packages from a depot station to the homes of customers. At the level of a single van driver, the task is a traveling salesman problem. But the choice of route may be constrained by warehouse sorting operations, van-loading processes, driver preferences, and other considerations rather than a straightforward minimization of tour length. We propose a simple and efficient penalty-based local search algorithm for route optimization in the presence of such constraints, adopting a technique developed by Helsgaun to extend the Lin–Kernighan–Helsgaun algorithm for the traveling salesman problem to general vehicle routing problems. We apply his technique to handle combinations of constraints obtained from an analysis of historical routing data, enforcing properties that are desired in high-quality solutions. Our code is available under the open-source Massachusetts Institute of Technology license. An earlier version of the code received the $100,000 top prize in the Amazon Last Mile Routing Research Challenge organized in 2021. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. [ABSTRACT FROM AUTHOR]

For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques from the decision diagram literature to generate and strengthen novel route relaxations for obtaining dual bounds without using column generation. Our approach is generic and can be applied to various vehicle routing problems in which corresponding dynamic programming models are available. We apply our framework to the traveling salesman with drone problem and show that it produces dual bounds competitive to those from the state of the art. Applied to larger problem instances in which the state-of-the-art approach does not scale, our method outperforms other bounding techniques from the literature. Funding: This work was supported by the National Science Foundation [Grant 1918102] and the Office of Naval Research [Grants N00014-18-1-2129 and N00014-21-1-2240]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0170. [ABSTRACT FROM AUTHOR]

This paper is the fruit of a partnership with Renault. Their reverse logistic requires solving a continent-scale multiattribute inventory routing problem (IRP). With an average of 30 commodities, 16 depots, and 600 customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale, so we propose a large neighborhood search (LNS). To make it work, (1) we generalize existing split delivery vehicle routing problems and IRP neighborhoods to this context, (2) we turn a state-of-the-art matheuristic for medium-scale IRP into a large neighborhood, and (3) we introduce two novel perturbations: the reinsertion of a customer and that of a commodity into the IRP solution. We also derive a new lower bound based on a flow relaxation. In order to stimulate the research on large-scale IRP, we introduce a library of industrial instances. We benchmark our algorithms on these instances and make our code open source. Extensive numerical experiments highlight the relevance of each component of our LNS. Funding: This work was supported by Renault Group. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0342. [ABSTRACT FROM AUTHOR]

Home Health Services (HHS) are gaining increasing importance with the aging population and the rise in chronic illnesses. However, the effective and efficient planning and management of these services face numerous constraints, such as stochastic patient demands, variable road conditions, and resource capacities. In particular, the stochastic nature of patient demands creates a significant uncertainty factor in planning, often requiring the reorganization of plans. Furthermore, HHS has a multi-periodic structure; that is, different types of services may be needed for each patient at different time slots. The main aim of this study is to propose a mathematical model that takes into account stochastic patient demands and multiperiodic planning needs to make the provision of HHS more effective and efficient. The proposed model will contribute to the more effective allocation of resources, the improvement of service quality, and better handling of emergency situations. This translates to both better service provision for patients and lower costs for the healthcare system. [ABSTRACT FROM AUTHOR]

The family of Rounded Capacity (RC) inequalities is one of the most important sets of valid inequalities for the Capacitated Vehicle Routing Problem (CVRP). This paper considers the problem of separation of violated RC inequalities and develops an exact procedure employing mixed integer linear programming. The developed routine is demonstrated to be very efficient for small and medium‐sized problem instances. For larger‐scale problem instances, an iterative approach for exact separation of RC inequalities is developed, based upon a selective variable pricing strategy. The approach combines column and row generation and allows us to introduce variables only when they are needed, which is essential when dealing with large‐scale problem instances. A computational study demonstrates scalability of the proposed separation routines and provides exact RC‐based lower bounds to some of the publicly available unsolved CVRP instances. The same computational study provides RC‐based lower bounds for very large‐scale CVRP instances with more than 3000 locations obtained within appropriate computational time limits. [ABSTRACT FROM AUTHOR]

In recent years, increasingCO2 emissions and resource utilization has adversely affected the environment. Sustainability efforts have been initiated to decrease these effects, including environmentally friendly electric vehicles in vehicle fleets used for transportation. The electric vehicle routing problem (EVRP) has emerged in the literature, and numerous studies have been conducted, considering specific constraints related to electric vehicles. Due to various charging feature constraints, EVRP diverges from the classical vehicle routing problem (VRP) and becomes more complex. In addition to the load capacity constraints of classical VRP, electric vehicles must deliver products to customers via an optimal vehicle route while considering battery capacity limitations. This study addresses the integrated single-machine scheduling and electric vehicle routing problem. After scheduling and processing customer product requests on a single machine, electric vehicle routes must be created to deliver these products to customers. To meet customer expectations, the objective function of the problem aims to minimize the costs associated with customer product delivery delays. Two mathematical models, i.e., mixed-integer linear programming (MILP) and constraint programming (CP) models, are presented to solve this problem. The results and performances of these models are compared on a set of instances. Numerical results indicate that the CP model has superior performance than the MILP model for the problem. [ABSTRACT FROM AUTHOR]

The two ends of urban rail transit stations are often curved, the turn-back line controlled by the curve is far from the platform, and the turn-back capacity is low, which cannot meet the requirements of the train organization. In order to solve the problem that the length of the straight section in front of the station is insufficient to set up the turn-back crossover line, a scheme was proposed to set up a single crossover line or a scissors crossover line for turn-back in combination with the line spacing in front of the station. Through the calculation of the single crossover line in front of the station with different line angles, different turnout models and different vehicle formations, combined with the technical operation process of the single track, the turn-back capacity of the single crossover line in front of the station can reach 13~20 pair/h; By calculating scissors crossover line in front of the station with different turnout models, different vehicle formations, and different station line spacing, combined with the technical operation process of the double track, the turn-back of the scissors crossover line in front of the station can achieve a capacity of 19~25 pair/h. For linear motor metro and other medium and low capacity systems using short vehicle formations, the turn-back capacity will be further improved. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the variables that influence freight cost in the forest supply chain and to fit nonlinear regression models to estimate the freight cost of wood transportation from the production areas to the processing units. The data used for cost analysis considered the following variables: volume of wood (m3); average fuel consumption (km/l) and average speed of the complete journey (km/h) per round trip. The results showed that there is a strong positive correlation between the variables transport distance and the freight cost of the wood, as well as between the moisture content of the wood and the freight cost of the wood. The load capacity of the vehicle combinations showed a mean negative correlation with the freight cost of the wood, and the length of the wood did not show a correlation with the cost of the transported wood. The fitted models for estimating the value of wood freight showed a high degree of adjustment of the dependent variable, indicating a good capacity for extrapolating the results from these variables. Such results emphasize the importance of optimizing transport routes, controlling wood moisture, maximizing vehicle volumetric load capacity, and using cost forecast models in order to make correct and efficient decisions in the forest supply chain context. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, there is an urgent need to build more transportation facilities to alleviate travel pressure. However, during the construction of a subway station, a large amount of muck is generated and must be transported to a treatment center. In response to transportation policies, this paper establishes a regional and time-limited transportation model for muck trucks based on their departure time points. The model aims to dispatch the least number of vehicles and complete all transportation tasks as quickly as possible, taking into account constraints such as restricted travel time. This paper uses the NSGA-II algorithm with multi-segment encoding to solve this problem, and numerical experiments are conducted to analyze the performance of the proposed method. The results indicate that the improved algorithm has better convergence and distribution than the standard NSGA-II. The study also validates the effectiveness of the proposed method through a real-world example of muck transportation at subway stations in a specific city. The collaborative scheduling schemes developed through the methods proposed in this paper have effectively avoided the travel restriction period, providing managers with multiple decision-making options. [ABSTRACT FROM AUTHOR]

In order to realize the efficient collection and low-carbon transport of municipal garbage and accelerate the realize the "dual-carbon" goal for urban transport system, based on the modeling and solving method of vehicle routing problem, the municipal solid waste (MSW) collection and transport routing optimization of an Improved Ant Colony-Shuffled Frog Leaping Algorithm (IAC-SFLA) is proposed. In this study, IAC-SFLA routing Optimization model with the goal of optimization collection distance, average loading rate, number of collections, and average number of stations is constructed. Based on the example data of garbage collection and transport in southern Baohe District, the comparative analysis with single-vehicle models, multiple-vehicle models, and basic ant colony algorithms. The multi-vehicle model of collection and transportation is superior to the single-vehicle model and the improved ant colony algorithm yields a total collection distance that is 19.76 km shorter and an average loading rate that rises by 4.15% from 93.95% to 98.1%. Finally, the improved ant colony algorithm solves for the domestic waste collection and transportation path planning problem in the north district of Baohe. Thus, the effectiveness and application of the proposed algorithm is verified. The research result can provide reference for vehicle routing in the actual collection and transport process, improve collection and transport efficiency, and achieve the goal of energy conservation and emission reduction [ABSTRACT FROM AUTHOR]

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction. [ABSTRACT FROM AUTHOR]