A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem

This work presents the Vehicle Routing with Time Windows and Loading Problem (VRTWLP) as a multi-objective optimization problem, implemented within a Genetic Algorithm. Specifically, the three dimensions of the problem to be optimized – the number of vehicles, the total travel distance and volume utilization – are considered to be separated dimensions of a multi-objective space. The quality of the solution obtained using this approach is evaluated and compared with results of other heuristic approaches previously developed by the author. The most significant contribution of this work is our interpretation of VRTWLP as a Multi-objective Optimization Problem

A Multi-Start Algorithm for Solving the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints

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A hybrid algorithm for the vehicle routing problem with three-dimensional loading constraints and mixed backhauls

In this paper, a variant of the vehicle routing problem with mixed backhauls (VRPMB) is presented, i.e. goods have to be delivered from a central depot to linehaul customers, and, at the same time, goods have to be picked up from backhaul customers and brought to the depot. Both types of customers can be visited in mixed sequences. The goods to be delivered or picked up are three-dimensional (cuboid) items. Hence, in addition to a routing plan, a feasible packing plan for each tour has to be provided considering a number of loading constraints. The resulting problem is the vehicle routing problem with three-dimensional loading constraints and mixed backhauls (3L-VRPMB)

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

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

Vehicle routing problem considering reconnaissance and transportation

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 문일경.Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This thesis proposes vehicle routing problem considering reconnaissance and transportation (VRPCRT) for troop movements in wartime. VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements. For this thesis, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed and computational experiments were conducted to compare the performance of the ACO algorithm and that of the mixed-integer programming model. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.Abstract iii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Research Motivation and Contribution 4 1.2 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Review of pickup and delivery problem 6 2.2 Review of ant colony optimization algorithms 9 Chapter 3 Mathematical model 10 3.1 Problem description 10 3.2 The model formulation 14 3.3 Numerical example 17 Chapter 4 Ant colony optimization algorithm 20 4.1 Construction of a solution 21 4.2 Pheromone updating 23 Chapter 5 Computational experiment 26 5.1 Experiment 1 26 5.2 Experiment 2 29 Chapter 6 Conclusion 34 5.1 Findings 34 5.2 Future direction 35 Bibliography 36 국문초록 40 감사의 글 41Maste