A REVIEW OF DISRUPTION MANAGEMENT IN VEHICLE ROUTING PROBLEM (VRP), TRANSPORT DESIGN AND SCHEDULING

In the case of an unexpected occurrence, disruption management is a method of rescheduling activities and it has been used in a variety of fields, including organized carrier scheduling and project management. The purpose of this review is to examine Ant Colony Optimization (ACO), the problems of Heuristics for Delivery Waste Collection (VRP), ARC Routing, Node Routing, and Container/skip. Other issues and problems examined in the paper were Non-Skip, Algorithms for the VRP, Improvement Algorithms, Simulated Annealing, and ACO for Capacitated Vehicle Routing, Clustering Analysis, and Probabilistic-D Cluster Analysis. It covers the fundamental characteristics of disruption management as well as the related goals and kinds of disruption that may occur in this setting. The various formulations and solution techniques are discussed in facets. A collection of relevant articles has been summarized and categorized according to the kind of disruption problem being addressed, the relevant goals, and the solution method used to resolve the problem. Vehicles must be emptied at a trash disposal facility before they may be used to collect garbage from further clients. The growing amount of solid waste generated as a result of p

Dynamic Capacitated Arc Routing Problem in E-Bike Sharing System: A Monte Carlo Tree Search Approach

This paper studies a dynamic capacitated arc routing problem for battery replacement in an e-bike sharing system, where workers replace batteries for underpowered e-bikes along street segments dynamically. The objective is to replace as many batteries as possible and minimize pickup failures. The temporal dependency of the routing decisions, the conflict of the workers’ operations, and the stochastic and dynamic nature of user demands all make this a difficult problem. To cope with these difficulties, a “Partition-First, Route-Second” bi-level solution framework is adopted to describe the problem in two different time scales. On the large time scale, a spatiotemporal partitioning method, which divides the road network into nonoverlapping subzones, is proposed to decompose the problem. On the small time scale, this paper models the routing decision process of individual worker as a Markov Decision Process. We adopt a lookahead policy that simulates future information and decisions over some horizons to evaluate the long-term influence of current feasible decisions. A Monte Carlo Tree Search algorithm is also used to improve the simulation efficiency. By performing numerical computation experiments on a test case study and comparing with some benchmarking policies, we demonstrate the effectiveness and efficiency of the suggested method.

A Multi Logistics Network Optimization Model of Power System based on Stochastic Demand and Inventory Decision

According to the disposal demand for packets and all kinds of constraints in the current condition, this paper discusses a modeling method of extended double-layer capacitated arc routing problems with Capacitated Arc Routing Problem (CARP) optimization model based on express logistics. The model is described in detail, and the solution of the model is discussed. The paper analyses and discusses the solution to complexity of the model, and proposes a better solution to the double-layer CARP optimization model. According to the scheme, the paper chooses a sort of improved ant colony algorithm to solve the model. And the results show that the scheme is beneficial to controlling the cost of logistics links, to minimizing purchase cost, transportation cost and delivery time. The scheme plays a very imperative role in enhancing the competitiveness of enterprises

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.