Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows

Yan, Fang; Wang, Yuanyuan

doi:10.1007/978-3-319-59280-0_69

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…. , Wi} (15) Formula (3) aims to maximize the mean consumer satisfaction; formula (4) aims to minimize the total distribution cost; formula (5) ensures that no vehicle surpasses its load capacity; formula (6) controls the total travel distance (time) within the preset range; formula (7) guarantees that each consumer is served by only one vehicle; formula (8) eliminates sub-loops; formula (9) specifies the chronological order of the multiple time windows of each consumer; formulas (10) and (11) regulates that each consumer is served within a time window; formula (13) shows the expected travel speed of a vehicle in each period of the day; formulas (14) and (15) provide the intervals of different variables.…”

Section: Model Constructionmentioning

confidence: 99%

“…Based on consumer satisfaction and vehicle transport cost, Sun and Ma [13] constructed a multi-objective VRP with fuzzy time window, and solved the problem with hybrid bat algorithm. In the light of consumer demand in actual distribution, Yan and Wang [14] studied the VRP with multiple fuzzy time windows, solved the problem with particle swarm optimization (PSO), and verified the cost effectiveness of the solution.…”

Section: Introductionmentioning

confidence: 99%

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A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow

Zheng

2020

IEEE Access

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In actual distribution process, the traffic flow varies with time, and each consumer has multiple fuzzy windows. To minimize the total distribution cost and mean consumer dissatisfaction, this paper sets up a vehicle routing problem (VRP) model with multiple fuzzy time windows, based on time-varying traffic flow. In addition, the Ito algorithm was improved based on time-varying traffic flow. The model and algorithm were verified through example simulation, in comparison with ant colony optimization (ACO). During the simulation, the improved Ito algorithm effectively reduced the distribution cost and consumer dissatisfaction, and outperformed the ACO in solving efficiency and solution quality. The results fully demonstrate the feasibility and effectiveness of the proposed algorithm. The research findings provide a desirable solution to VRPs with multiple fuzzy windows and time-varying traffic flow in the real world. INDEX TERMS Time-varying traffic flow, multiple fuzzy time windows, Ito algorithm, vehicle routing problem (VRP), consumer satisfaction.

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Section: Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow

Zheng

2020

IEEE Access

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“…Для комплексного варианта VRPFTW с множеством депо и разнородным парком предлагалась многоэтапная эвристика [18]: кластеризация клиентов, маршрутизация, определение типов транспортных средств, планирование и улучшение маршрутов с использованием имитации отжига и повышения уровня обслуживания клиентов. Хорошие результаты также показали метод оптимизации роем частиц [19] и модифицированный алгоритм стаи волков [20].…”

Section: Advanced Engineering Research 2020 т 20 № 3 с 325−331 unclassified

Overview of fuzzy vehicle routing problems

Чернышев

Kubil

Требухин

2020

Advanced Engineering Research

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Introduction. Various algorithms for solving fuzzy vehicle routing problems are considered. The work objective was to study modern methods for the optimal solution to fuzzy, random and rough vehicle routing problems. Materials and Methods. The paper reviews fuzzy vehicle routing problems, existing methods and approaches to their solution. The most effective features of some approaches to solving fuzzy vehicle routing problems considering their specificity, are highlighted. Results. The Fuzzy Vehicle Routing Problem (FVRP) occurs whenever the routing data is vague, unclear, or ambiguous. In many cases, these fuzzy elements can better reflect reality. However, it is very difficult to use Vehicle Routing Problem (VRP) solving algorithms to solve FVRP since several fundamental properties of deterministic problems are no longer fulfilled in FVRP. Therefore, it is required to introduce new models and algorithms of fuzzy programming to solve such problems. Thus, the use of methods of the theory of fuzzy sets will provide successful simulation of the problems containing elements of uncertainty and subjectivity. Discussion and conclusions. As a result of reviewing various methods and approaches to solving vehicle routing problems, it is concluded that the development and study of new solutions come into sharp focus of researchers nowadays, but the degree of elaboration of various options varies. Methods for the optimal solution of FVRP are limited, in general, to some single fuzzy variable. There is a very limited number of papers that consider a large number of fuzzy variables.

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“…ere exist many extensions of the VRP, which can be classified into different categories according to configurations, problem modelling, solving algorithms, and objectives [6][7][8][9][10][11][12][13][14][15][16]. For example, this problem can be formulated differently considering homogeneous vehicles or heterogeneous vehicles [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…For example, this problem can be formulated differently considering homogeneous vehicles or heterogeneous vehicles [6,7]. It can be further extended by adding different constraints to the original definition such as time constraints, traveling distance constraints, and capacity constraints [11,12,15,17].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

Ding

et al. 2021

Mathematical Problems in Engineering

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With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

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Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows

Cited by 3 publications

References 11 publications

A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow

A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow

Overview of fuzzy vehicle routing problems

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

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