209 research outputs found
An updated annotated bibliography on arc routing problems
The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberรกn and Prins (Networks 56 (2010), 50โ69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio
Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm
Investigating edge-reordering procedures in a tabu search algorithm for the capacitated arc routing problem
This paper presents two ideas to guide a tabu search algorithm for the Capacitated Arc Routing Problem to a promising region of the solution space. Both ideas involve edge-reordering, although they work in different ways. One of them aims to directly tackle deadheading cycles, and the other tries to reorder edges with the aim of extending a scope of solutions that can be reached from a given solution. Experiments were performed on 134 benchmark instances of various sizes, and the two ideas were shown to have an ability to guide the search to good solutions. Possible issues that may arise when implementing these ideas are also discussed
Workload Equity in Vehicle Routing Problems: A Survey and Analysis
Over the past two decades, equity aspects have been considered in a growing
number of models and methods for vehicle routing problems (VRPs). Equity
concerns most often relate to fairly allocating workloads and to balancing the
utilization of resources, and many practical applications have been reported in
the literature. However, there has been only limited discussion about how
workload equity should be modeled in VRPs, and various measures for optimizing
such objectives have been proposed and implemented without a critical
evaluation of their respective merits and consequences.
This article addresses this gap with an analysis of classical and alternative
equity functions for biobjective VRP models. In our survey, we review and
categorize the existing literature on equitable VRPs. In the analysis, we
identify a set of axiomatic properties that an ideal equity measure should
satisfy, collect six common measures, and point out important connections
between their properties and those of the resulting Pareto-optimal solutions.
To gauge the extent of these implications, we also conduct a numerical study on
small biobjective VRP instances solvable to optimality. Our study reveals two
undesirable consequences when optimizing equity with nonmonotonic functions:
Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all
tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent,
i.e. composed of tours whose workloads are all equal to or longer than those of
other Pareto-optimal solutions. We show that the extent of these phenomena
should not be underestimated. The results of our biobjective analysis are valid
also for weighted sum, constraint-based, or single-objective models. Based on
this analysis, we conclude that monotonic equity functions are more appropriate
for certain types of VRP models, and suggest promising avenues for further
research.Comment: Accepted Manuscrip
The stochastic vehicle routing problem : a literature review, part II : solution methods
Building on the work of Gendreau et al. (Oper Res 44(3):469โ477, 1996), and complementing the first part of this survey, we review the solution methods used for the past 20 years in the scientific literature on stochastic vehicle routing problems (SVRP). We describe the methods and indicate how they are used when dealing with stochastic vehicle routing problems. Keywords: vehicle routing (VRP), stochastic programmingm, SVRPpublishedVersio
๊ฐ๋ฏธ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ ๋๋ก ์ ์ ์ค ๊ฒฝ๋ก ์ต์ ํ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ, 2022.2. ๊น๋๊ท.Drones can overcome the limitation of ground vehicles by replacing the congestion time and allowing rapid service. For sudden snowfall with climate change, a quickly deployed drone can be a flexible alternative considering the deadhead route and the labor costs. The goal of this study is to optimize a drone arc routing problem (D-ARP), servicing the required roads for snow removal. A D-ARP creates computational burden especially in large network. The D-ARP has a large search space due to its exponentially increased candidate route, arc direction decision, and continuous arc space. To reduce the search space, we developed the auxiliary transformation method in ACO algorithm and adopted the random walk method. The contribution of the work is introducing a new problem and optimization approach of D-ARP in snow removal operation and reduce its search space. The optimization results confirmed that the drone travels shorter distance compared to the truck with a reduction of 5% to 22%. Furthermore, even under the length constraint model, the drone shows 4% reduction compared to the truck. The result of the test sets demonstrated that the adopted heuristic algorithm performs well in the large size networks in reasonable time. Based on the results, introducing a drone in snow removal is expected to save the operation cost in practical terms.๋๋ก ์ ํผ์ก์๊ฐ๋๋ฅผ ๋์ฒดํ๊ณ ๋น ๋ฅธ ์๋น์ค๋ฅผ ๊ฐ๋ฅํ๊ฒ ํจ์ผ๋ก์จ ์ง์์ฐจ๋์ ํ๊ณ๋ฅผ ๊ทน๋ณตํ ์ ์๋ค. ์ต๊ทผ ๊ธฐํ๋ณํ์ ๋ฐ๋ฅธ ๊ฐ์์ค๋ฐ ๊ฐ์ค์ ๊ฒฝ์ฐ์, ๋๋ก ๊ณผ ๊ฐ์ด ๋น ๋ฅด๊ฒ ํฌ์
ํ ์ ์๋ ์๋น์ค๋ ์ดํ ๊ฒฝ๋ก์ ๋
ธ๋๋น์ฉ์ ๊ณ ๋ คํ์ ๋๋ ์ ์ฐํ ์ด์ ์ต์
์ด ๋ ์ ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ๋๋ก ์ํฌ ๋ผ์ฐํ
(D-ARP)์ ์ต์ ํํ๋ ๊ฒ์ด๋ฉฐ, ์ด๋ ์ ์ค์ ํ์ํ ๋๋ก๋ฅผ ์๋น์คํ๋ ๊ฒฝ๋ก๋ฅผ ํ์ํ๋ ๊ฒ์ด๋ค. ๋๋ก ์ํฌ ๋ผ์ฐํ
์ ํนํ ํฐ ๋คํธ์ํฌ์์ ์ปดํจํฐ ๋ถํ๋ฅผ ์์ฑํ๋ค. ๋ค์ ๋งํดD-ARP๋ ํฐ ๊ฒ์๊ณต๊ฐ์ ํ์๋ก ํ๋ฉฐ, ์ด๋ ๊ธฐํ๊ธ์์ ์ผ๋ก ์ฆ๊ฐํ๋ ํ๋ณด ๊ฒฝ๋ก ๋ฐ ํธ์ ๋ฐฉํฅ ๊ฒฐ์ ๊ทธ๋ฆฌ๊ณ ์ฐ์์ ์ธ ํธ์ ๊ณต๊ฐ์ผ๋ก๋ถํฐ ๊ธฐ์ธํ๋ค. ๊ฒ์๊ณต๊ฐ์ ์ค์ด๊ธฐ ์ํด, ์ฐ๋ฆฌ๋ ๊ฐ๋ฏธ์๊ณ ๋ฆฌ์ฆ์ ๋ณด์กฐ๋ณํ๋ฐฉ๋ฒ์ ์ ์ฉํ๋ ๋ฐฉ์์ ๋์
ํ์์ผ๋ฉฐ ๋ํ ๋๋ค์ํฌ ๊ธฐ๋ฒ์ ์ฑํํ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๊ธฐ์ฌ๋ ์ ์ค ์ด์์ ์์ด D-ARP๋ผ๋ ์๋ก์ด ๋ฌธ์ ๋ฅผ ์ค์ ํ๊ณ ์ต์ ํ ์ ๊ทผ๋ฒ์ ๋์
ํ์์ผ๋ฉฐ ๊ฒ์๊ณต๊ฐ์ ์ต์ํํ ๊ฒ์ด๋ค. ์ต์ ํ ๊ฒฐ๊ณผ, ๋๋ก ์ ์ง์ํธ๋ญ์ ๋นํด ์ฝ 5% ~ 22%์ ๊ฒฝ๋ก ๋น์ฉ ๊ฐ์๋ฅผ ๋ณด์๋ค. ๋์๊ฐ ๊ธธ์ด ์ ์ฝ ๋ชจ๋ธ์์๋ ๋๋ก ์ 4%์ ๋น์ฉ ๊ฐ์๋ฅผ ๋ณด์๋ค. ๋ํ ์คํ๊ฒฐ๊ณผ๋ ์ ์ฉํ ํด๋ฆฌ์คํฑ ์๊ณ ๋ฆฌ์ฆ์ด ํฐ ๋คํธ์ํฌ์์๋ ํฉ๋ฆฌ์ ์๊ฐ ๋ด์ ์ต์ ํด๋ฅผ ์ฐพ์์ ์
์ฆํ์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก, ๋๋ก ์ ์ ์ค์ ๋์
ํ๋ ๊ฒ์ ๋ฏธ๋์ ์ ์ค ์ด์ ๋น์ฉ์ ์ค์ง์ ์ผ๋ก ๊ฐ์์ํฌ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.Chapter 1. Introduction 4
1.1. Study Background 4
1.2. Purpose of Research 6
Chapter 2. Literature Review 7
2.1. Drone Arc Routing problem 7
2.2. Snow Removal Routing Problem 8
2.3. The Classic ARPs and Algorithms 9
2.4. Large Search Space and Arc direction 11
Chapter 3. Method 13
3.1. Problem Statement 13
3.2. Formulation 16
Chapter 4. Algorithm 17
4.1. Overview 17
4.2. Auxilary Transformation Method 18
4.3. Ant Colony Optimization (ACO) 20
4.4. Post Process for Arc Direction Decision 23
4.5. Length Constraint and Random Walk 24
Chapter 5. Results 27
5.1. Application in Toy Network 27
5.2. Application in Real-world Networks 29
5.3. Application of the Refill Constraint in Seoul 31
Chapter 6. Conclusion 34
References 35
Acknowledgment 40์
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