Dynamic selection and order allocation of resilient suppliers based on improved fuzzy multi-criteria decision method

ABSTRACT

A complex supply chain can improve the economic benefits of an enterprise to a certain extent, whereas its vulnerability also occurs. How to select resilient suppliers to improve enterprise risk resistance has become hot issue in supply chain management. Furthermore, due to the complexity of the supply chain and the instability of the market, the supplier selection in different periods and customer demands is dynamic. We use the best-worst approach to model the initial selection of suppliers taking into account risk factors. Based on this result, our study applies the fuzzy multi-criteria decision method to establish a multi-objective model that aims to solve the problem of dynamic supplier selection considering resilient criteria. We apply the improved wolf pack algorithm to measure the optimal order allocation strategy between profit and pollution. The supplier selection and order allocation of new energy vehicle manufacturing companies are used as a case study for verification. The results show the multi-objective model of supplier selection considering risk and resilience factors is feasible, and the dual-objective model of supplier selection and order allocation considering different demands in different periods are effective. Compared with the original algorithm, the improved Wolf pack algorithm has better comprehensive performance.

CO EDITOR-IN-CHIEF:

• Hsieh, Sun-Yuan

ASSOCIATE EDITOR:

• Ho, Chien-Chung

KEYWORDS:

• Resilience
• supplier selection
• dynamic order allocation
• improved fuzzy multi-criteria decision method, improved wolf pack algorithm

Nomenclature

Cf	=	Solid emissions per unit of product
Cg	=	Gas emissions per unit of product
${\tilde{c}}_{\mathit{s}}$	=	Material transportation cost from supplier S
$\tilde{d}$	=	Total demand for the required material
$\mathit{d}{s}^{\mathit{ab}}$	=	Distance between selected supplier a and selected supplier b ($\mathit{a}\ne \mathit{b}$)
dt	=	Product demand at period t
${\tilde{k}}_{\mathit{s}}$	=	Minimum acceptable purchase quantity of supplier S
$\mathit{l}$	=	Number of objective functions, $\mathit{l}=1,2,\dots ,\mathit{L}$
$\mathit{m}{x}_s$	=	Maximum number of suppliers selected for required materials
${\tilde{p}}_{\mathit{s}}$	=	Percentage of rejected materials delivered by supplier S
$P_{\mathit{ts}}$	=	Sales price per unit of product for supplier s at period t
$q_s$	=	Quantity of material purchased from supplier S
$Q_{\mathit{ts}}$	=	Supply capacity of supplier s at period t
$\mathit{r}{s}_s$	=	Elasticity score of supplier S
s	=	Alternative suppliers, $\mathit{s}=1,2,\dots ,\mathit{S}$
t	=	Phase time, $\mathit{t}=1,2,\dots ,\mathit{T}$
${\tilde{t}}_{\mathit{s}}$	=	Haulage time of supplier S
${\tilde{u}}_{\mathit{s}}$	=	The cost of purchasing the required materials from supplier $\mathit{s}$
${\tilde{v}}_{\mathit{s}}$	=	Capacity of the supplier S
$\begin{array}{c}{w}_{\mathit{ab}}\\ {\mathit{w}{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{ab}}\end{array}=\left\{\begin{array}{c}1\\ 0\end{array}\right.$	=	1 if suppliers a and b are selected, 0$\mathrm{\forall }\left(\mathit{a},\mathit{b}\right)\in \mathit{s},\mathit{a}\ne \mathit{b}$ otherwise
$x_{\mathit{ts}}$	=	Order quantity for supplier s at period
$y_{\mathit{ts}}$	=	1 means the supplier s is selected in period t and 0 means that it is not selected
$\mathit{\gamma }$	=	Unit inventory cost
${\pi }_{\mathit{ts}}$	=	Transportation cost per unit of product for supplier s at period t
${\rho }_{\mathit{ts}}$	=	Production cost per unit of product for supplier s at period t
${\varphi }_s=\left\{\begin{array}{c}1\\ 0\end{array}\right.$	=	1 if supplier S is selected, 0 otherwise

Acknowledgments

The authors are very much indebted to the Editor-in-Chief who greatly helped to improve this paper with their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is supported by China Scholarship Council (File No. (201908210398)). The authors gratefully acknowledge the financial supports.