(392v) Enhancing Supply Chain Reliability Via Flexibility Analysis

Modern supply chains operate in increasingly volatile environments, where uncertainties in market demand, supplier reliability, and production capacity significantly complicate optimization and scheduling decisions [1]. Traditional approaches, such as stochastic programming [2] and robust optimization [3], typically rely on probabilistic distributions or worst-case scenarios, which may either oversimplify system uncertainties or result in overly conservative solutions. In contrast, process flexibility offers a systematic framework to evaluate a system's ability to adapt to perturbations without relying on explicit probability distributions [4,5]. Building on this idea, this study proposes a flexibility-analysis-based framework for assessing supply chain reliability, in which the variability of uncertain parameters is captured through deterministic feasibility margins rather than stochastic bounds.

The main contribution of this work is to present a novel Reliability Index (RI), inspired by the geometric interpretation of flexibility analysis. The RI quantifies the maximum allowable deviation in uncertain parameters while maintaining operational feasibility, thus serving as a deterministic measure of system reliability. When the RI falls below a predefined threshold (e.g., due to tight operational constraints), a two-tiered enhancement strategy is employed: (i) optimizing the nominal values of uncertain parameters [6], and (ii) performing profit expectation concessions guided by flexibility-tradeoff.

This approach innovatively integrates the flexibility metrics into supply chain decision-making, enabling a priori reliability guarantees without stochastic modeling. The effectiveness of the proposed reliability enhancement methodology is demonstrated through three case studies: a state-task network[7], a multi-stage supply chain model, and an integrated production-scheduling model.

References:

[1] Simangunsong E, Hendry LC, Stevenson M. Supply-chain uncertainty: a review and theoretical foundation for future research. International Journal of Production Research, 2012, 50(16): 4493-4523.

[2] Hu Z, Hu G. A two-stage stochastic programming model for lot-sizing and scheduling under uncertainty. International Journal of Production Economics, 2016, 180: 198-207.

[3] Qiu H, Gu W, Liu P, Sun Q, Wu Z, Lu X. Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective. Energy, 2022, 251: 123942.

[4] Swaney RE, Grossmann IE. An index for operational flexibility in chemical process design. Part I: Formulation and theory. AIChE Journal, 1985, 31(4): 621-630.

[5] Swaney RE, Grossmann IE. An index for operational flexibility in chemical process design. Part II: Computational algorithms. AIChE Journal, 1985, 31(4): 631-641.

[6] Zhao F, Paz Ochoa M, Grossmann IE, García-Muñoz S, Stamatis SD. Novel formulations of flexibility index and design centering for design space definition. Computers & Chemical Engineering, 2022, 166: 107969.

[7] Kondili E, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch operations-I. MILP formulation. Computers & Chemical Engineering, 1993, 17(2): 211-227.