Dynamic Vehicle Scheduling for Working Service Network with Dual Demands
This study aims to develop some models to aid in making decisions on the combined fleet size and vehicle assignment in working service network where the demands include two types (minimum demands and maximum demands), and vehicles themselves can act like a facility to provide services when they are...
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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2017/7217309 |
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doaj-1dc52c75e63a4e70ab7ac193cac1565e2020-11-25T00:01:31ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/72173097217309Dynamic Vehicle Scheduling for Working Service Network with Dual DemandsBing Li0Wei Xu1Hua Xuan2Chunqiu Xu3School of Management Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, ChinaThis study aims to develop some models to aid in making decisions on the combined fleet size and vehicle assignment in working service network where the demands include two types (minimum demands and maximum demands), and vehicles themselves can act like a facility to provide services when they are stationary at one location. This type of problem is named as the dynamic working vehicle scheduling with dual demands (DWVS-DD) and formulated as a mixed integer programming (MIP). Instead of a large integer program, the problem is decomposed into small local problems that are guided by preset control parameters. The approach for preset control parameters is given. By introducing them into the MIP formulation, the model is reformulated as a piecewise form. Further, a piecewise method by updating preset control parameters is proposed for solving the reformulated model. Numerical experiments show that the proposed method produces better solution within reasonable computing time.http://dx.doi.org/10.1155/2017/7217309 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bing Li Wei Xu Hua Xuan Chunqiu Xu |
spellingShingle |
Bing Li Wei Xu Hua Xuan Chunqiu Xu Dynamic Vehicle Scheduling for Working Service Network with Dual Demands Journal of Advanced Transportation |
author_facet |
Bing Li Wei Xu Hua Xuan Chunqiu Xu |
author_sort |
Bing Li |
title |
Dynamic Vehicle Scheduling for Working Service Network with Dual Demands |
title_short |
Dynamic Vehicle Scheduling for Working Service Network with Dual Demands |
title_full |
Dynamic Vehicle Scheduling for Working Service Network with Dual Demands |
title_fullStr |
Dynamic Vehicle Scheduling for Working Service Network with Dual Demands |
title_full_unstemmed |
Dynamic Vehicle Scheduling for Working Service Network with Dual Demands |
title_sort |
dynamic vehicle scheduling for working service network with dual demands |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
0197-6729 2042-3195 |
publishDate |
2017-01-01 |
description |
This study aims to develop some models to aid in making decisions on the combined fleet size and vehicle assignment in working service network where the demands include two types (minimum demands and maximum demands), and vehicles themselves can act like a facility to provide services when they are stationary at one location. This type of problem is named as the dynamic working vehicle scheduling with dual demands (DWVS-DD) and formulated as a mixed integer programming (MIP). Instead of a large integer program, the problem is decomposed into small local problems that are guided by preset control parameters. The approach for preset control parameters is given. By introducing them into the MIP formulation, the model is reformulated as a piecewise form. Further, a piecewise method by updating preset control parameters is proposed for solving the reformulated model. Numerical experiments show that the proposed method produces better solution within reasonable computing time. |
url |
http://dx.doi.org/10.1155/2017/7217309 |
work_keys_str_mv |
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