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Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed. [ABSTRACT FROM AUTHOR]

Experimental learning is one kind of contextual learning, which places learners in a variety of virtual or real contexts to acquire knowledge, gain ability and experience, and generates emotion through experiencing and perceiving, communicating and exploring, and self-reflecting. Applying VR technology to experiential learning involves both technology and teaching. Based on the Learning Pyramid, Constructivist Learning Theory, and the characteristics of virtual technology, this study proposes a theoretical model of virtual reality experiential learning, which takes the international business negotiation scene as an example and employs such software as 3Ds Max and VR-platform to implement an experiential learning context. It is found that, compared with traditional teaching, this learning context has effectively increased the learners' memorization and understanding ability, their interest and motivation in learning, enhanced their learning experience and improved the learning effects. This study not only provides a theoretical framework for and verifies the promotion effect of virtual technology on learners but also enriches the research examples of virtual reality experiential learning, which has certain theoretical value and practical significance. [ABSTRACT FROM AUTHOR]

Ship routing is a fundamental component of maritime passenger logistics, involving the careful planning of sea routes to facilitate the efficient and comfortable transportation of passengers. However, the ship routing problem presents unique challenges in the transit network design, encompassing various Vehicle Routing Problem (VRP) variants. These challenges arise from complex characteristics including multiple depots, ship types, and asymmetric distances between ports. This study aims to minimize passenger transfers in ship routing by incorporating constraints on maximum distance and travel time. To address these challenges, we propose a two-step approach: a hybrid Genetic Algorithm (GA) that incorporates the Fixed Radius Near Neighbor (FRNN) heuristic method alongside GA optimization. Moreover, a comparison was conducted between our proposed hybrid GA and both the standalone FRNN and conventional GA. The comparative analysis demonstrated that the hybrid GA outperformed both the FRNN and the standard GA in the reduction of passenger transfers and attainment of optimal fitness values. This highlights the significance of incorporating various optimization methodologies to enhance performance. Subsequent studies should investigate the mechanisms underlying the success and suitability of hybrid GA in practical maritime logistics scenarios.

Last-mile delivery plays a crucial role in improving the service level of express delivery, as it involves direct contact with customers. Providing personalized last-mile delivery services is an important means of improving customer satisfaction. Massive consumer data makes it possible to mine customers’ personalized logistics preferences. The paper studies the vehicle routing problem in last-mile delivery considering customers' preferences. The paper first quantifies customers' preferences for delivery time, location, and mode, and obtains preference probabilities based on historical data. Then, an optimization considering customer satisfaction and enterprise delivery costs is established, and a vehicle routing problem model considering customer preferences is proposed. To solve the problem, we designed an adaptive large neighborhood search (ALNS) algorithm with virtual delivery points to solve the problem and proposed specific destroy and repair operators. Through the case analysis of an express delivery company, this article provides the optimal routs and analyzes the customer preferences on each route. In addition, this article explores the impact of the customer preference constraint and complaint constraint on cost and gives the appropriate customer preference constraint and complaint rate constraint from the perspective of cost-saving.

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks.

This article presents research results on a multi-aspect assessment of delivery planning in the urban transport network. The distribution of goods and the operation of a network of small services in urban agglomerations is a very important decision-making problem. This is due, on one hand, to the dense development of urban areas and, on the other hand, to the increasing restrictions on minimizing harmful exhaust gases emitted by cars. Hence, many researchers are looking for decision support methods that take into account many partial points of view in the field of transport services for small service companies located in urban areas. The authors present a method of multi-criteria decision support in the planning of urban supplies, taking into account the minimization of emissions of harmful compounds (carbon dioxide, nitrogen oxides, and particulate matter), the cost, time, and amount of ecosystem exposure to these compounds. An important aspect of this research was to identify partial criteria of the decision support methodology and the definition of decision variants. The partial criteria included cost, time, and the amount of exposure of the ecosystem to harmful compounds. The Saaty method was used to determine the weights of the criteria. [ABSTRACT FROM AUTHOR]

Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed.

Active volcanoes are a regular occurrence in nature and are well known for the risk and devastation they cause when they erupt. In India, Barren Island volcano (BIV) has started erupting actively after the great earthquake (9.3 magnitude) and tsunami. In the current study, we present a satellite based approach to monitor the volcano dynamics at BIV using multispectral data sets along with volcanic radiative power (VRP) and field evidence for three various eruptive phases, i.e. 2005, 2015 and 2021. The results of VRP clearly depicts the changes from low to high thermal regimes that indicate switching from open vent to active activity of Barren Island volcano. In addition, HYSPLIT and ash modelling also shows that the ash transport pathways that flow in lower atmosphere travel over the neighbouring countries and also within Indian states. Further, we also conducted a field visit in 2021 November to validate the lab observations and geophysical studies. The current study has provided significant information regarding the location of primary, secondary vents, and ash transport pathways. Further it is inferred that, for a comprehensive risk assessment of the region, volcanism, environmental and coastal dynamics along with crustal deformation need to be considered. [ABSTRACT FROM AUTHOR]

The vehicle routing problem (VRP) and its variants are a class of network problems that have attracted the attention of many researchers in recent years, owing to their pragmatic approach to solving issues in logistics management. Most surveys/reviews of the extant literature often focus on specific variants or aspects of the VRP. However, a few reviews of the overall VRP literature are available. The focus of these papers is to identify which VRP literature characteristics are the most popular in recent studies. To this end, we analyze 229 articles published between 2015 and 2017. We provide a systematic literature review evaluating the Scenario Characteristics and Problem Physical Characteristics that are most frequently addressed by VRP researchers, the Type of Study and Data Characteristics that they address, the most cited works that constitute the theoretical pillars of the field, and details of three specific problem variants that have been studied extensively in recent years and their opportunities for future research. [ABSTRACT FROM AUTHOR]

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%.

Without the cold-chain network, it would be impossible in today's world to transport large quantities of perishable goods, primarily food and medicine, over long distances, but it is still very important that the necessary materials reach their destination on time and with adequate cooling. This is absolutely true for the vaccines created to stop the Covid-19 pandemic, which had to be distributed quickly and at certain storage temperatures to all parts of the world. This is a rather difficult task with NP-hard complexity. In this research, we are investigating the optimal way to get from a distribution center in Budapest to the county towns with transport vehicles, so that neither the vaccines deteriorate, nor we violate regulations. For this, we used vehicle route planning principles and Excel-Solver software. [ABSTRACT FROM AUTHOR]

Corona pandemic has revealed that the uncertainty associated with disaster management has rippling effects, resulting in severe human suffering across the globe. The severity and complexity of the relief operations increase the authority’s response time. The Humanitarian Supply Chain (HSC) can be helpful and is one of the critical branches of Operations Research. This paper presents a systematic literature review using PRISMA in the Vehicle Routing Problem (VRP) for HSC papers published in SCOPUS and Web of Science databases during the last two decades. It aims to classify and review the literature on VRP models in HSC using fifteen different categorizations. This paper extensively describes objective functions and solution methodologies, including heuristics, commercial and exact solvers, uncertainties, resource types, and vehicle types. Several research questions are framed using the PICOS (Population, Intervention, Comparison, Outcomes, and Study) tool. Future research directions are accordingly proposed based on objective functions, uncertain approaches, heuristic approaches, vehicle types, resource types, model dimensions, sustainability, and scheduling. The results of this study show that there is still plenty of room for research and development in VRP for HSC.

The COVID-19 pandemic has, among other things, disrupted global supply chains, through a large increase in demand for medical supplies with simultaneous movement restrictions, increased transportation costs, and reduced labour availability. For many years, transport, distribution, and logistics processes have been optimized by solving the Vehicle Routing Problem (VRP). Optimizing logistics processes is especially important during a pandemic when resources are limited, and disruptions can endanger human lives. This paper aims to investigate the impact of COVID-19 on the Vehicle Routing Problem in practice due to new limitations and challenges by reviewing the literature.

An electric vehicle, as the name suggests, runs purely on electricity provided by battery packs located under its floor. This electricity fuels the engine and converts electric power to mechanical motion. Accordingly, electric vehicles do not emit pollutants as no combustion process takes place within, making it an eco-friendly alternative to combustion vehicles. However, electric vehicles face one major issue and that is their limited range. Recharging electric vehicles’ batteries requires an important amount of time. Since time is an important factor, recent works started focusing more and more on optimizing the use of recharge within an electric vehicle to make the most of it while avoiding frequent stops at the charging stations. Another riddle to solve, is the routing of vehicles which gets more complicated when electric vehicles are thrown into the mix. Not only an electric vehicle has a limited range, adding charging stations location to the problem affects the final path, the time and the cost of the routing problem. To help researchers advance in this direction, our work discuss the recent methods used to solve the vehicle routing problem, as well as the issues facing charging of electric vehicles. This will help establish a picture of where improvements need to be made and inspire future works to accelerate the rate at which electric vehicles will be roaming our roads. Our review shows that literature focuses increasingly on adapting electric VRP to real life settings by adding more and more constraints. Moreover, recent works are considering the recharging patterns of electric vehicles in order to reduce the overall travel time, distance and cost. The results of this review prove that complexity of electric VRP stems from the difficulty of satisfying all imposed constraints, as well as the inability to predict the outcome of the solution proposed face to real-life scenarios.

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence. [ABSTRACT FROM AUTHOR]

One category of vehicle routing problems involving groups of people where there can be multiple possible drop-off locations is the School Bus Routing Problem (SBRP). In this paper, we investigate a form of SBRP where each passenger has a well-ordered set of preferred stops, e.g., the preferences can be based on the distance to their destination. The passengers continue their journey from the stop to their destination by an alternative mode, such as walking. Different drop-off choices allow for a different set of passengers to be dropped off together, affecting the total vehicle driving distance. As the transport provider and passengers have their own objectives, inevitably, there are two opposing goals (tradeoff aspects), reducing total driving distance versus passenger walking distance. Unlike typical SBRP, we focus on practical problems where the set of passengers is not known well in advance and there is only a short time window to compute a solution (a real-time requirement). Hence, runtime efficiency is more important than optimality. We introduce this variant as SBRP-RT, a school bus routing problem with tradeoff aspects and real-time requirements. We propose a model with an efficient greedy local search for such problems, and demonstrate the efficacy with experiments on real-world datasets with quick solving that balances tradeoffs. [ABSTRACT FROM AUTHOR]

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%. [ABSTRACT FROM AUTHOR]

This paper enhanced the autonomy of a mobile agricultural robot in a structured environment to find an optimal path to perform selective and variable spraying of pesticides. To materialize these, a robust VRP (Vehicle Routing Problem) scheme with deterministic variable demand and nonlinear travel time is designed to navigate the robot while making decisions to fulfill the pesticide demand at each node. The nonlinear time function obtained from experiments necessitates the consideration of three objectives simultaneously. The NSGA (non-dominated sorting genetic algorithm)-III algorithm is adopted to determine the optimal path for the robot. To achieve this, the mixed integer algorithm is modified to handle the full integer complex routing problem to optimize three test case scenarios. Finally, the NSGA-III has shown the capability to solve the full integer constraint problem based on the proposed modifications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications [ABSTRACT FROM AUTHOR]

This research aims to show the results of applying optimization in the distribution network of high consumption products, using metaheuristics for the design of distribution routes, applying genetic algorithms, performing the simulation in the HeuristicLab software. Achieving the optimization of the distribution processes of high consumption products which has grown in recent years to the increase of the population and the increase of the vehicular congestion, in the big cities, hindering the achievement of common objectives between the different actors in Urban Distribution of Merchandise (DUM). The research was quantitative cross-sectional non-experimental, because the situations and subjects of study, could not be manipulated by the researcher, the information was provided by the company, which, for reasons of confidentiality, will not be named. [ABSTRACT FROM AUTHOR]

The shared bicycle scheduling path planning problem is essentially a VRP problem with capacity constraints. This paper proposes a vehicle routing problem considering the priority of distribution and not returning to the supply point after completing the distribution task (VRPPNR for short). Establishes a combined optimization model for this problem with the goal of minimizing the total transportation distance. And the ant colony algorithm is used to solve the model. Then, this paper verifies the feasibility of the model and method with an actual case. Finally, the model is extended and applied to the case where all demand points have the same priority. The results show that the model and method can be applied to the scheduling path planning of shared bikes and can provide a good reference for shared bike enterprises in selecting the best distribution path scheme. [ABSTRACT FROM AUTHOR]

This Study aims to show the impact of covid-19 on employees’ transportation in Morocco, particularly in industrial field, the daily routes between homes and workplace. Moroccan government is led to make strong decisions unexpectedly with immediate turnaround time depending on pandemic state in function of the propagation speed of the virus and its mutated versions and capacity of the health ministry to hold the active and serious cases. These decisions should be made taking into consideration the economic situation, defining restriction to manage the pandemic situation and at the same time avoid hindering of slowing the industrial activity. The frequency of changing the restrictions is not known, it is revealed at unexpected time which make the transportation planning a difficult task. Besides, these decisions have huge impact on transport management for both sides, companies, and service providers. Through this work we will quantify the impact of restrictions on transport budget (20% up to 96% of increase) and propose a decision-making tool to plan daily routes of vehicles using a sequential coupling of vehicle routing problem (VRP) and Bin packing problem in a real case study. This tool have allowed to limit the increase of budget by optimizing the routing process. [ABSTRACT FROM AUTHOR]

This paper presents a method for the fast prototyping of no-wait cyclic schedules for periodic material handling systems with a Grid-like Material Transportation Network (GMTN). A distribution network is modeled as a grid-like system of cyclic processes performing regular pick-up and delivery operations between workstations in separate grid modules. The considered problem boils down to a job shop cyclic scheduling problem with no-buffer and no-wait constraints, which is NP-hard. The main contribution of this research is a novel method of grid-like system decomposition into subsystems with two processes using one common resource and construction of the necessary and sufficient conditions for the existence of a no-wait cyclic schedule (N-WCS) of the system for given start times of the processes. The approach's novelty involves implementing a linear-complexity procedure, which allows quick testing of whether any N-WCS may exist. Due to the low computational complexity of the procedure, it is possible to determine the start times of processes initiation for delivery networks of the scale encountered in practice and calculate corresponding cyclic schedules. A set of initial states of no-wait cyclic schedules (N-WCSs) for the systems with a grid structure of arbitrary dimensions is determined based on computational examples. • Considered problem is no-wait cyclic schedules for a Grid-like Material Transportation Network. • Cyclic scheduling problem with no-wait constraints, is NP-hard especially for grid-like systems. • The contribution is a method of grid-like system decomposition into subsystems. • The novelty of the method lies in its implementation as a linear-complexity procedure. • The method was verified of the examples of the practical grid-like systems. [ABSTRACT FROM AUTHOR]