A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers
This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that supp...
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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/894573 |
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doaj-b5e18ed576e4498eb3f1980e71a1b59d2020-11-25T01:22:56ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/894573894573A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple ManufacturersGuo Li0Fei Lv1Xu Guan2School of Management and Economics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaEconomics and Management School, Wuhan University, Wuhan 430072, ChinaThis paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.http://dx.doi.org/10.1155/2014/894573 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guo Li Fei Lv Xu Guan |
spellingShingle |
Guo Li Fei Lv Xu Guan A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers The Scientific World Journal |
author_facet |
Guo Li Fei Lv Xu Guan |
author_sort |
Guo Li |
title |
A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers |
title_short |
A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers |
title_full |
A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers |
title_fullStr |
A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers |
title_full_unstemmed |
A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers |
title_sort |
collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. |
url |
http://dx.doi.org/10.1155/2014/894573 |
work_keys_str_mv |
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