Risk-Averse Co-Decision for Lower-Carbon Product Family Configuration and Resilient Supplier Selection

Abstract

Due to global pandemics, political unrest and natural disasters, the stability of the supply chain is facing the challenge of more uncertain events. Although many scholars have conducted research on improving the resilience of the supply chain, the research on integrating product family configuration and supplier selection (PCSS) under disruption risks is limited. In this paper, the centralized supply chain network, which contains only one major manufacturer and several suppliers, is considered, and one resilience strategy (i.e., the fortified supplier) is used to enhance the resilience level of the selected supply base. Then, an improved stochastic bi-objective mixed integer programming model is proposed to support co-decision for PCSS under disruption risks. Furthermore, considering the above risk-neutral model as a benchmark, a risk-averse mixed integer program with Conditional Value-at-Risk (CVaR) is formulated to achieve maximum potential worst-case profit and minimum expected total greenhouse gases (GHG) emissions. Then, NSGA-II is applied to solve the proposed stochastic bi-objective mixed integer programming model. Taking the electronic dictionary as a case study, the risk-neutral solutions and risk-averse solutions that optimize, respectively, average and worst-case objectives of co-decision are also compared under two different ranges of disruption probability. The sensitivity analysis on the confidence level indicates that fortifying suppliers and controlling market share in co-decision for PCSS can effectively reduce the risk of low-profit/high-cost while minimizing the expected GHG emissions. Meanwhile, the effects of low-probability risk are more likely to be ignored in the risk-neutral solution, and it is necessary to adopt a risk-averse solution to reduce potential worst-case losses.

Ordering Decisions with an Unreliable Supplier under the Carbon Cap-and-Trade System

“…On the other hand, ref. [38] tackled low-carbon disruptions through the formulation of a resilience strategy. Unlike this paper, refs.…”

“…Unlike this paper, refs. [35,38] approach the carbon emissions reduction issue under supply disruptions from a supply chain perspective. This paper focuses on how the manufacturer undergoing a low-carbon transition under supply disruptions can formulate order decisions to achieve emissions reduction while ensuring the sustainability of his operations.…”

Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming

“…These proposed optimization approaches for low-carbon product design in the above researches have mainly focused on a single product. Compared with the low-carbon design of a single product, the low-carbon design of a product family is more complex [11][12][13][14][15][16].…”

“…Moreover, in order to cope with global warming, a serious problem facing human society, reducing carbon emissions has become an important industrial development goal. How to pay attention to low-carbon design optimization in the process of product design, so as to maximize the low-carbon sustainability of design and make products more in line with low-carbon ecological requirements, has become a research hotspot [11][12][13][14][15][16].…”

“…(3) Goal preferences. As previously described, low-carbon PFP and MaaS design decisionmaking processes are essentially multi-objective optimization problems [5,6,[11][12][13][14][15][16]20]. In the traditional weight-based techniques used for this kind of multi-objective optimization problem [11], the process of determining appropriate weights or priorities is uncertain and time-consuming, and thus the practicality of these approaches is diminished [23].…”