386 research outputs found

    On green routing and scheduling problem

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    The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

    OPTIMAL ROUTE DETERMINATION FOR POSTAL DELIVERY USING ANT COLONY OPTIMIZATION ALGORITHM

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    There are a lot of optimization challenges in the world, as we all know. The vehicle routing problem is one of the more complex and high-level problems. Vehicle Routing Problem is a real-life problem in the Postal Delivery System logistics and, if not properly attended to, can lead to wastage of resources that could have been directed towards other things. Several studies have been carried out to tackle this problem using different techniques and algorithms. This study used the Ant Colony Optimization Algorithm along with some powerful APIs to find an optimal route for the delivery of posts to customers in a Postal Delivering System. When Ant Colony Optimization Algorithm is used to solve the vehicle routing problem in transportation systems, each Ant's journey is mere โ€œpartโ€ of a feasible solution. To put it in another way, numerous ants' pathways might make up a viable solution. Routes are determined for a delivery vehicle, with the objective of minimizing customer waiting time and operation cost. Experimental results indicate that the solution is optimal and more accurat

    A web spatial decision support system for vehicle routing using Google Maps

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    This article presents a user-friendly web-based Spatial Decision Support System (wSDSS) aimed at generating optimized vehicle routes for multiple vehicle routing problems that involve serving the demand located along arcs of a transportation network. The wSDSS incorporates Google Mapsโ„ข (cartography and network data), a database, a heuristic and an ant-colony meta-heuristic developed by the authors to generate routes and detailed individual vehicle route maps. It accommodates realistic system specifics, such as vehicle capacity and shift time constraints, as well as network constraints such as one-way streets and prohibited turns. The wSDSS can be used for โ€œwhat-ifโ€ analysis related to possible changes to input parameters such as vehicle capacity, maximum driving shift time, seasonal variations of demand, network modifications, imposed arc orientations, etc. Since just a web browser is needed, it can be easily adapted to be widely used in many real-world situations. The system was tested for urban trash collection in Coimbra, Portugal

    ๊ฐœ๋ฏธ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ๋“œ๋ก ์˜ ์ œ์„ค ๊ฒฝ๋กœ ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2022.2. ๊น€๋™๊ทœ.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.๋“œ๋ก ์€ ํ˜ผ์žก์‹œ๊ฐ„๋Œ€๋ฅผ ๋Œ€์ฒดํ•˜๊ณ  ๋น ๋ฅธ ์„œ๋น„์Šค๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์œผ๋กœ์จ ์ง€์ƒ์ฐจ๋Ÿ‰์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ฐ‘์ž‘์Šค๋Ÿฐ ๊ฐ•์„ค์˜ ๊ฒฝ์šฐ์—, ๋“œ๋ก ๊ณผ ๊ฐ™์ด ๋น ๋ฅด๊ฒŒ ํˆฌ์ž…ํ•  ์ˆ˜ ์žˆ๋Š” ์„œ๋น„์Šค๋Š” ์šดํ–‰ ๊ฒฝ๋กœ์™€ ๋…ธ๋™๋น„์šฉ์„ ๊ณ ๋ คํ–ˆ์„ ๋•Œ๋„ ์œ ์—ฐํ•œ ์šด์˜ ์˜ต์…˜์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋“œ๋ก  ์•„ํฌ ๋ผ์šฐํŒ…(D-ARP)์„ ์ตœ์ ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋Š” ์ œ์„ค์— ํ•„์š”ํ•œ ๋„๋กœ๋ฅผ ์„œ๋น„์Šคํ•˜๋Š” ๊ฒฝ๋กœ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋“œ๋ก  ์•„ํฌ ๋ผ์šฐํŒ…์€ ํŠนํžˆ ํฐ ๋„คํŠธ์›Œํฌ์—์„œ ์ปดํ“จํ„ฐ ๋ถ€ํ•˜๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ๋‹ค์‹œ ๋งํ•ดD-ARP๋Š” ํฐ ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ์ด๋Š” ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ํ›„๋ณด ๊ฒฝ๋กœ ๋ฐ ํ˜ธ์˜ ๋ฐฉํ–ฅ ๊ฒฐ์ • ๊ทธ๋ฆฌ๊ณ  ์—ฐ์†์ ์ธ ํ˜ธ์˜ ๊ณต๊ฐ„์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธํ•œ๋‹ค. ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ์ค„์ด๊ธฐ ์œ„ํ•ด, ์šฐ๋ฆฌ๋Š” ๊ฐœ๋ฏธ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋ณด์กฐ๋ณ€ํ™˜๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์„ ๋„์ž…ํ•˜์˜€์œผ๋ฉฐ ๋˜ํ•œ ๋žœ๋ค์›Œํฌ ๊ธฐ๋ฒ•์„ ์ฑ„ํƒํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ธฐ์—ฌ๋Š” ์ œ์„ค ์šด์˜์— ์žˆ์–ด D-ARP๋ผ๋Š” ์ƒˆ๋กœ์šด ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜๊ณ  ์ตœ์ ํ™” ์ ‘๊ทผ๋ฒ•์„ ๋„์ž…ํ•˜์˜€์œผ๋ฉฐ ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ์ตœ์†Œํ™”ํ•œ ๊ฒƒ์ด๋‹ค. ์ตœ์ ํ™” ๊ฒฐ๊ณผ, ๋“œ๋ก ์€ ์ง€์ƒํŠธ๋Ÿญ์— ๋น„ํ•ด ์•ฝ 5% ~ 22%์˜ ๊ฒฝ๋กœ ๋น„์šฉ ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ๊ธธ์ด ์ œ์•ฝ ๋ชจ๋ธ์—์„œ๋„ ๋“œ๋ก ์€ 4%์˜ ๋น„์šฉ ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์‹คํ—˜๊ฒฐ๊ณผ๋Š” ์ ์šฉํ•œ ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ํฐ ๋„คํŠธ์›Œํฌ์—์„œ๋„ ํ•ฉ๋ฆฌ์  ์‹œ๊ฐ„ ๋‚ด์— ์ตœ์ ํ•ด๋ฅผ ์ฐพ์Œ์„ ์ž…์ฆํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋“œ๋ก ์„ ์ œ์„ค์— ๋„์ž…ํ•˜๋Š” ๊ฒƒ์€ ๋ฏธ๋ž˜์— ์ œ์„ค ์šด์˜ ๋น„์šฉ์„ ์‹ค์งˆ์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.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„

    Ant colony optimization and its application to the vehicle routing problem with pickups and deliveries

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    Ant Colony Optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. It was first introduced for solving the Traveling Salesperson Problem. Since then many implementations of ACO have been proposed for a variety of combinatorial optimization. In this chapter, ACO is applied to the Vehicle Routing Problem with Pickup and Delivery (VRPPD). VRPPD determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The vehicles are not only required to deliver goods but also to pick up some goods from the customers. The objective is to minimize the total distance traversed. The chapter first provides an overview of ACO approach and presents several implementations to various combinatorial optimization problems. Next, VRPPD is described and the related literature is reviewed, Then, an ACO approach for VRPPD is discussed. The approach proposes a new visibility function which attempts to capture the โ€œdeliveryโ€ and โ€œpickupโ€ nature of the problem. The performance of the approach is tested using well-known benchmark problems from the literature

    Workload Equity in Vehicle Routing Problems: A Survey and Analysis

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    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

    An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

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    This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular metaheuristics, namely a genetic algorithm, a particle swarm optimization algorithm, an enhanced ABC algorithm and a hybrid particle swarm optimization algorithm. It is also found that the midway disposal pattern should be used in practice because it reduces the carbon emission at most 7.16% for the investigated instances

    Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

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    The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customersโ€™ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

    An updated annotated bibliography on arc routing problems

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    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), 50โ€“69), 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
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