Optimising two-stage robust supplier selection and order allocation problem under risk-averse criterion

被引:15
作者
Feng, Yuqiang [1 ]
Chen, Yanju [1 ]
Liu, Yankui [2 ]
机构
[1] Hebei Univ, Coll Math & Informat Sci, Risk Management & Financial Engn Lab, Baoding, Peoples R China
[2] Hebei Univ, Coll Math & Informat Sci, Hebei Key Lab Machine Learning & Computat Intelli, Baoding 071002, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Supplier selection and order allocation problem; disruption risks; mean-CVaR criterion; distributionally robust optimisation; ambiguity set; RESILIENCE; MODEL; OPTIMIZATION; DECISION; SYSTEMS;
D O I
10.1080/00207543.2022.2127963
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper studies the supplier selection and order allocation (SS&OA) problem, where risks include a series of disruption scenarios with uncertain probability of occurrence. It is a challenge for industry decision-makers to balance the average cost and the level of risk under the ambiguity set for probabilities. To address this challenge, a two-stage distributionally robust (DR) Mean-CVaR model is presented for the SS&OA problem. A procedure is developed for constructing the ambiguity set, and Polyhedral and Box ambiguity sets are constructed to characterise the uncertain probabilities. The worst-case Mean-CVaR criterion is employed for the second-stage cost within the ambiguity set to trade off the expected cost and CVaR value. Three measures are incorporated to increase the resilience of the supply chain. The proposed robust model is reformulated into two mixed-integer linear programming models. A real case of the Huawei cell phone manufacturer is used to illustrate the validity of the proposed approach in numerical settings. Experimental results show that the new optimising approach can provide a robust SS&OA solution to immunise against the influence caused by uncertain probabilities. By comparative analyses, some management insights are obtained for industry decision-makers.
引用
收藏
页码:6356 / 6380
页数:25
相关论文
共 56 条
[11]   Multi-objective model for supplier selection and order allocation problem with fuzzy parameters [J].
Firouzi, Fatemeh ;
Jadidi, Omid .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 180
[12]  
Fortune, 2020, ABOUT US
[13]   Outsourcing: assessing the risks and benefits for organisations, sectors and nations [J].
Harland, C ;
Knight, L ;
Lamming, R ;
Walker, H .
INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 2005, 25 (9-10) :831-850
[14]   A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach [J].
Hosseini S. ;
Ivanov D. .
Annals of Operations Research, 2022, 319 (1) :581-607
[15]   Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review [J].
Hosseini, Seyedmohsen ;
Ivanov, Dmitry .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 161 (161)
[16]   Resilient supplier selection and optimal order allocation under disruption risks [J].
Hosseini, Seyedmohsen ;
Tajik, Nazanin ;
Ivanov, Dmitry ;
Sarder, M. D. ;
Barker, Kash ;
Al Khaled, Abdullah .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2019, 213 :124-137
[17]   Review of quantitative methods for supply chain resilience analysis [J].
Hosseini, Seyedmohsen ;
Ivanov, Dmitry ;
Dolgui, Alexandre .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2019, 125 :285-307
[18]   A Bayesian network model for resilience-based supplier selection [J].
Hosseini, Seyedmohsen ;
Barker, Kash .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 180 :68-87
[19]   Drug recall management and channel coordination under stochastic product defect severity: a game-Theoretic analytical study [J].
Hosseini-Motlagh, Seyyed-Mahdi ;
Nematollahi, Mohammadreza ;
Nami, Nazanin .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2021, 59 (06) :1649-1675
[20]  
Ivanov D., 2021, Introduction to supply chain resilience: Management, modelling, technology