Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain level. Therefore, the optimization model makes sure of the procurement cost, including purchasing cost and penalty cost, being restricted within an acceptable level according to the confidence and risk value initially set. The paper also provides a comparison between the conventional model (deterministic constraints) and proposed model (chance constraints) and shows the superiority of the latter.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017
PublisherNewswood Limited
Pages950-954
Number of pages5
Volume2228
ISBN (Electronic)9789881404770
Publication statusPublished - 2017 Jan 1
Event2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 - Hong Kong, Hong Kong
Duration: 2017 Mar 152017 Mar 17

Other

Other2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017
CountryHong Kong
CityHong Kong
Period17/3/1517/3/17

Fingerprint

Costs
Purchasing
Decision making
Uncertainty

Keywords

  • Chance constraint
  • Goal programming
  • Supplier selection
  • Uncertainty

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Li, X., & Murata, T. (2017). Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017 (Vol. 2228, pp. 950-954). Newswood Limited.

Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty. / Li, Xi; Murata, Tomohiro.

Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. Vol. 2228 Newswood Limited, 2017. p. 950-954.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, X & Murata, T 2017, Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty. in Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. vol. 2228, Newswood Limited, pp. 950-954, 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017, Hong Kong, Hong Kong, 17/3/15.
Li X, Murata T. Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. Vol. 2228. Newswood Limited. 2017. p. 950-954
Li, Xi ; Murata, Tomohiro. / Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty. Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. Vol. 2228 Newswood Limited, 2017. pp. 950-954
@inproceedings{3cbdf323c4c445b38cf8efbadfc56686,
title = "Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty",
abstract = "This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain level. Therefore, the optimization model makes sure of the procurement cost, including purchasing cost and penalty cost, being restricted within an acceptable level according to the confidence and risk value initially set. The paper also provides a comparison between the conventional model (deterministic constraints) and proposed model (chance constraints) and shows the superiority of the latter.",
keywords = "Chance constraint, Goal programming, Supplier selection, Uncertainty",
author = "Xi Li and Tomohiro Murata",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "2228",
pages = "950--954",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017",
publisher = "Newswood Limited",

}

TY - GEN

T1 - Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty

AU - Li, Xi

AU - Murata, Tomohiro

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain level. Therefore, the optimization model makes sure of the procurement cost, including purchasing cost and penalty cost, being restricted within an acceptable level according to the confidence and risk value initially set. The paper also provides a comparison between the conventional model (deterministic constraints) and proposed model (chance constraints) and shows the superiority of the latter.

AB - This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain level. Therefore, the optimization model makes sure of the procurement cost, including purchasing cost and penalty cost, being restricted within an acceptable level according to the confidence and risk value initially set. The paper also provides a comparison between the conventional model (deterministic constraints) and proposed model (chance constraints) and shows the superiority of the latter.

KW - Chance constraint

KW - Goal programming

KW - Supplier selection

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85041184237&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041184237&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85041184237

VL - 2228

SP - 950

EP - 954

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017

PB - Newswood Limited

ER -