Balanced dynamic multiple travelling salesmen: Algorithms and continuous approximations

Garn, Wolfgang

doi:10.1016/j.cor.2021.105509

Cited by 9 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Daganzo [31] provided an comprehensive view of CA functions of TSP and VRP. Garn [32] used a machine learning approach to derive a CA model for the balanceddynamic MTSP. Several scholars adopted the CA to solve the large-scale routing problem, such as disaster relief [33], school bus routing [34], and drone routing [35].…”

Section: Shared Bikesmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Zheng

Yang

Jiang

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost; which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k -means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes.

show abstract

Section: Shared Bikesmentioning

confidence: 99%

“…(28) end if (29) end while (30) Update the position of the center of each cluster. (31) end for (32) Recompute the adjustment volume of each staff. (33) end while (34) Output: the travel route, travel distance, and working time of each staff.…”

mentioning

confidence: 99%

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Zheng

Yang

Jiang

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…It is possible to observe in Figure 3 and Table 1 the many key points associated with the Clusters in question. (16) 121, 145, 215, 73 Parque Industrial Canhoso (1) 60 Canhoso (7) 162 Teixoso (10) 148, 229, 261 Gibraltar (1) 103 Vila do Carvalho ( 5) 52…”

Section: North Zonementioning

confidence: 99%

“…The solution considered splitting the 26 × 26 distance matrix in two: an outer Route, including the points far away with crooked roads with high slopes, and a smaller Route, encompassing the points closer to the city. (16) 121, 145, 215, 73 Parque Industrial Canhoso (1) 60 Canhoso (7) 162 Teixoso (10) 148, 229, 261 Gibraltar (1) 103 Vila do Carvalho ( 5) 52 (1) Number of establishments associated with the cluster.…”

Section: North Zonementioning

confidence: 99%

“…It is also usual to observe that some of these routing problems are dynamic, in which the cities or places to visit are known over time and not in advance as in conventional vehicle routing problems. Gam [16] addressed two different heuristics, proposing in one the assignment of the vehicle with the shortest distance traveled to the client, and in the other, the vehicle that is closest to the new location that arose in the problem. These heuristics also pay attention to the number of locations to visit so that all salesmen visit roughly the same number.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying a Genetic Algorithm to a m-TSP: Case Study of a Decision Support System for Optimizing a Beverage Logistics Vehicles Routing Problem

et al. 2021

View full text Add to dashboard Cite

Route optimization has become an increasing problem in the transportation and logistics sector within the development of smart cities. This article aims to demonstrate the implementation of a genetic algorithm adapted to a Vehicle Route Problem (VRP) in a company based in the city of Covilhã (Portugal). Basing the entire approach to this problem on the characteristic assumptions of the Multiple Traveling Salesman Problem (m-TSP) approach, an optimization of the daily routes for the workers assigned to distribution, divided into three zones: North, South and Central, was performed. A critical approach to the returned routes based on the adaptation to the geography of the Zones was performed. From a comparison with the data provided by the company, it is predicted by the application of a genetic algorithm to the m-TSP, that there will be a reduction of 618 km per week of the total distance traveled. This result has a huge impact in several forms: clients are visited in time, promoting provider-client relations; reduction of the fixed costs with fuel; promotion of environmental sustainability by the reduction of logistic routes. All these improvements and optimizations can be thought of as contributions to foster smart cities.

show abstract

Hybrid search with neighborhood reduction for the multiple traveling salesman problem

He¹,

Hao²

2022

Computers & Operations Research

View full text Add to dashboard Cite

Balanced dynamic multiple travelling salesmen: Algorithms and continuous approximations

Cited by 9 publications

References 29 publications

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Applying a Genetic Algorithm to a m-TSP: Case Study of a Decision Support System for Optimizing a Beverage Logistics Vehicles Routing Problem

Hybrid search with neighborhood reduction for the multiple traveling salesman problem

Contact Info

Product

Resources

About