An Analysis of Exact VRPTW Solutions on ITS Data-based Logistics Instances

In this paper, we propose an effective transport system using global information VICS and local information IVC. The VICS (vehicle information and communication system) which is one of the intelligent transport systems, has been developed to release the traffic jam. The VICS is able to give real-time global information to drivers and help drivers to find the fastest route. However, a spatial oscillation of traffic jam will happen, known as hunting phenomena. On the other hand, IVC (inter vehicle communication) is a developing technology which enables cars to give and receive various information by optical communication, millimeter electromagnetic waves and so on. We first investigate the relationship between hunting phenomena and the VICS user ratio, and then realize an effective transport system, in which each car selects a different route and communicate with others by IVC.

A radio over dense-wavelength-division-multiplexing (DWDM) transport system for personal handy system (PHS)/vehicle information and communication system (VICS)/electronic toll collection (ETC)/satellite broadcasting (SB) applications was proposed and demonstrated. Improved performance of bit error rate (BER) over a-100 km of large effective area fiber (LEAF) was obtained. Signal qualities meet the PHS demand with a data signal of 1.9 GHz/10 Mbps, VICS demand with a data signal of 2.5 GHz/12 Mbps, ETC demand with a data signal of 5.8 GHz/20 Mbps, and SB demand with a data signal of 12 GHz/25 Mbps.

We propose a new way to disperse planning route of transportation using IVC (Inter Vehicle Communication) in this paper. VICS (Vehicle Information and Communication System) has been developed to solve the traffic jam which is one of the ITS (Intelligent Transportation Systems) technologies. The VlCS is able to provide real-time global information with drivers and navigation system which helps drivers to find the fastest route according to the current traffic conditions. However, it is afraid that spatial oscillation of traffic jam "hunting phenomena" will happen if most of cars follow given global information with time delay from VICS. 0n the other hand, IVC is a developing technology which enables cars to exchange various information with others by optical communication, millimeter electromagnetic waves and so on. We therefore propose the combination way to decrease traffic congestion using IVC and VICS. At first we show the hunting phenomena in numerical simulations and the relationship between hunting phenomena and VICS user ratio. We propose a way to disperse planning route by IVC, and then confirm effect of the proposed way in city traffic models in numerical simulations.

The Radio Beacon System provides digital radio transmissions as part of the Vehicle Information and Communication System (VICS). Operating in the quasi-microwave (2.5 GHz) band at transmission speeds of 64 kbps, the system enables moving vehicles to be provided with roadway-traffic and location information. According to available reports on tests of this system, a service area of 70 m or more can be secured along open roadways where road station antennas are installed. Since the Metropolitan Expressway Public Corporation plans to construct a number of tunnels in the future, any practical Radio Beacon System will require that road stations also be located in tunnels. To confirm the possibility of communication in tunnels, the authors conducted an in-tunnel-evaluation test of the system to clarify in-tunnel transmission characteristics, which seem likely to be quite different from those on open roadways. >

The traffic congestion frequently occurs in urban transportations. In order to alleviate the traffic congestion, the vehicle information and communication system (VICS) has been developed. However, since each vehicle can obtain global information on traffic congestion using VICS, all vehicles in the congested areas tend to move to non-congested areas. As a result, the non-congested areas become congested areas. To avoid such the oscillation between the congested and non-congested areas, this paper proposes a novel method based on the vehicle ad hoc network (VANET) for alleviating traffic congestion in urban transportations. In the proposed method, since each vehicle independently collects local information on traffic congestion using VANET, traffic can be distributed from congested areas to non-congested areas. Through simulation experiments, this paper shows that the proposed method provides faster velocity and shorter trip time than VICS in the environments that traffic varies temporally and spatially, which occur in urban transportations.

In this paper vehicle routing problem with heterogeneous vehicle regarding the delivery time windows is examined . In the present case , each customer has a hard time window and a soft time window . Delivery outside the hardware time window is not possible. Delivery outside the soft time window earliness or delay costs imposed on the model . The mathematical model of the problem , innovative genetic algorithm for a given problem is developed . The results obtained from the sample solution by genetic algorithms , the exact solution of the model in the software Lingo is obtained and s uggests that genetic provided acceptable performance and low error gives an answer in a reasonable time .

Purpose. In order to study the impact of grey delivery time uncertainty on customer satisfaction and delivery costs, a vehicle routing problem with grey delivery time windows and multiobjective constraints is defined. Method. The paper first defines the uncertainty of the delivery vehicle’s arrival time to the customer as grey uncertainty and then whitens the grey time windows; at the same time, the customer’s hard time windows is expanded into a soft time windows to measure customer satisfaction when the vehicle arrives. Experiment. In order to verify the validity of the established model, numerical experiments are carried out in two groups based on the Solomon example, and the solution is solved based on the improved quantum evolution algorithm. Analysis. Distribution cost fluctuations and customer satisfaction fluctuations with grey time windows are relatively small; under different satisfaction threshold conditions, the distribution cost is increased gently with the satisfaction threshold. Conclusion. The grey delivery time windows have certain advantages in solving the random travel time vehicle routing problem.

A Multi-Depot Home Care Routing Problem with Time Windows and Fuzzy Demands Solving by Particle Swarm Optimization and Genetic Algorithm

A dynamic vehicle routing problem with time-dependent travel times

This paper investigates an integrated production scheduling and vehicle routing problem with soft time windows and fuzzy travel times, where orders are grouped into batches for production and delivered by a limited number of multi-trip heterogeneous vehicles. A bi-objective mixed integer nonlinear programming (MINLP) model is established, which takes total cost and total early and tardy weighted penalty time as optimization objectives. First, a fuzzy superposition operation is proposed to obtain the fuzzy weighted penalty time, and it is extended to a generalized fuzzy operation law in fuzzy sets and systems. Then, we propose a knowledge-driven co-evolutionary algorithm (KDCEA) to solve this problem. The algorithm fuses a dual-subpopulation co-evolution based on different update strategies and a knowledge-driven strategy based on dynamic knowledge sets and problem-specific knowledge. Finally, the correctness of the MINLP model is verified by the CPLEX solver using the ϵ-constraint method. A computational experiment is conducted based on different scale instances and a real-world case, and the results show the superiority of KDCEA in solving this problem.

Vehicle routing, with its many variants, is one of the most important and frequently solved problems in transportation engineering. The aim of this paper is to develop a decision-making support tool for addressing the issue of dispatching vehicles in scenarios characterized by uncertain demands within soft time windows. In real-world scenarios, it is not uncommon for customer demands to exhibit flexibility, where certain early arrivals or delays may be deemed acceptable. Therefore, this paper introduces vehicle routing in more realistic contexts, offering potential practical implementations. The methodology for solving the problem is based on a fuzzy logic system whose membership functions are additionally adjusted using a neural network. Such a tool, neuro-fuzzy logic, is suitable for solving a defined routing problem since it can consider all the mentioned uncertainties in the distribution systems. Each user is assigned a performance index that considers travel time, demand, and delivery time windows. Then, the performance index is used as input data in the proposed vehicle routing tool based on the Clarke-Wright algorithm. The described approach has been tested on a concrete example, mimicking a distribution network resembling real world conditions, incorporating estimated travel times between customers. The results demonstrate that the proposed approach can effectively handle customer demands, with an average delay of 5.05 minutes during the 80-minute distribution. In future research, some environmental factors could be included in the proposed model. In addition, one of the directions of future research could be vehicle re-routing using the ideas from this paper.

This paper studies an uncertain time-dependent vehicle routing problem with soft time windows. A novel mathematical model which considers both transportation costs (total traveling distance and number of vehicles) and service costs (early and late arrivals) is developed, and the equations for calculating the expected total service costs are deduced under uncertainty and time-dependency. A variation of artificial bee colony algorithm is designed to solve the problem; computational results show the feasibility of this approach.

The competition between companies requires finding the optimized vehicle routing, for this reason researches are more and more interested in transportation problems, this article attempts to address a practical variant of the vehicle routing problem (VRP), known as the VRP with soft time windows (VRPSTW), where deliveries are still possible outside the time windows, that often arise in practice.

An integrated location-routing problem with post-disaster relief distribution

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs.