Search

In this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions. [ABSTRACT FROM AUTHOR]

AbstractThis comparative study, constituting Part B of our extensive investigation into robust optimization (RO) in inventory management, builds on the foundational insights from Part A’s survey. It conducts a thorough analysis of various RO formulations and algorithms, emphasizing their practical application, efficacy, and computational considerations in inventory management contexts. This study is meticulously structured to address the pivotal inquiries identified in Part A, encompassing the effective representation of uncertainty, the selection of optimal decision criteria, the influence of decision rules on inventory management performance, computational challenges, and the adaptability of these methods to evolving technological and market conditions. This research juxtaposes theoretical findings with empirical data, offering a comprehensive evaluation of the strengths, limitations, and practical implications of each robust inventory model discussed in the literature. This investigation not only complements the survey in Part A but also serves as a standalone, in-depth contribution to robust inventory management. [ABSTRACT FROM AUTHOR]

AbstractThis work, the first part (Part A) of a comprehensive study, presents a survey on Robust Optimization (RO) in inventory management, highlighting its role in addressing uncertainties. This survey reviews various modeling frameworks, types of uncertainties, decision-making criteria, and decision rules essential to RO. The subsequent part (Part B) offers a comparative study, analyzing robust inventory models and highlighting key analytical and numerical contributions. This survey critically evaluates the effectiveness of RO in managing model uncertainty, enhancing decision-making processes, deriving structural insights, and boosting computational efficiency. Additionally, it discusses the limitations and challenges of applying RO to inventory management. While acknowledging the foundational role of traditional inventory literature in establishing essential theories, optimal policies, and efficient algorithms, this paper addresses a significant gap by focusing on inventory management through the lens of RO methodology. Together with the comparative study, these works encapsulate the current state of robust inventory management, shedding light on future research directions and ongoing challenges in this evolving field. [ABSTRACT FROM AUTHOR]