386 research outputs found
On green routing and scheduling problem
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
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 โpartโ 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
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 Mapsโข
(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 โwhat-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
๊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
Ant colony optimization and its application to the vehicle routing problem with pickups and deliveries
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 โdeliveryโ and โpickupโ 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
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
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
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
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
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