Search

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.