Operations Research

doi: 10.1287/opre.2024.inthisissue.v72.n5pmid: N/A

doi: 10.1287/opre.2024.eb.v7205pmid: N/A

Han, Jinhui; Li, Xiaolong; Sethi, Suresh P.; Siu, Chi Chung; Yam, Sheung Chi Phillip

doi: 10.1287/opre.2022.0191pmid: N/A

Analyzing Production-Inventory Systems with General Demand: Cost Minimization and Risk AnalyticsFrequent production rate changes are prohibitive because of high setup costs or setup times in producing such items as sugar, glass, computer displays, and cell-free proteins. Thus, constant production rates are deployed for producing these items even when their demands are random. In “Production Management with General Demands and Lost Sales,” Han, Li, Sethi, Siu, and Yam obtain the optimal constant production rate for a production-inventory system with Lévy demand for long-run average and expected discounted cost objectives, explicitly in some cases and numerically in general with a Fourier-cosine scheme they develop. This scheme can help in computing risk analytics of the inventory system, such as stockout probability and expected shortfall. These measures are particularly significant for assessing supply resilience, especially for emergency products or services like medicines and healthcare equipment. This study’s analytical and numerical findings contribute to enhancing efficiency and decision making in production management.

Bu, Jinzhi; Gong, Xiting; Chao, Xiuli

doi: 10.1287/opre.2022.0488pmid: N/A

Asymptotic Optimality of Simple Heuristic Policies for Multidimensional Inventory SystemsStochastic inventory systems with multidimensional state spaces, such as lost-sales system with positive lead time and perishable inventory system, are challenging to manage because of the curse of dimensionality, and their optimal control policies are extremely complex. In “Asymptotic Scaling of Optimal Cost and Asymptotic Optimality of Base-Stock Policy in Several Multidimensional Inventory Systems,” Bu, Gong and Chao consider three classes of such systems in the regime of large unit penalty cost, and they establish the asymptotic optimality of (modified) simple base-stock policies as well as an explicit expression for the optimal cost rate in each of these systems. These results justify the applications of such policies in real-world applications, and they are achieved by constructing tight newsvendor upper and lower bounds on the systems’ costs and analyzing the asymptotic scaling of newsvendor costs with large unit penalty cost. This approach is expected to be useful in studying other multidimensional stochastic inventory systems.

Lo, Andrew W.; Wu, Lan; Zhang, Ruixun; Zhao, Chaoyi

doi: 10.1287/opre.2023.0400pmid: N/A

Using Induced Order Statistics to Construct Optimal Impact Portfolios with General Dependence and MarginalsWe develop a mathematical framework for constructing optimal impact portfolios and quantifying their financial performance by characterizing the returns of impact-ranked assets using induced order statistics and copulas. Our results apply to any joint distribution of impact factors and residual returns, making them broadly applicable to a wide range of contexts. We develop significant extensions of the theory of induced order statistics, with which we are able to characterize the distribution of residual returns of individual assets ranked by the impact factor. Our framework provides a toolkit for practitioners to construct impact portfolios and quantify their performance based on real data. This allows impact investors to achieve higher risk-adjusted returns than those with impact portfolios constructed using simpler heuristics such as negative or positive screening.

van Jaarsveld, Willem; Arts, Joachim

doi: 10.1287/opre.2021.0032pmid: N/A

Inventory Projection and Asymptotic Optimalityvan Jaarsveld and Arts propose a new policy for the canonical periodic review lost sales inventory problem. Under this policy, orders are placed in each period such that the expected inventory level at the moment of replenishment order arrival is at a fixed level. This single-parameter policy is called the projected inventory-level (PIL) policy. The PIL inherits asymptotic optimality properties from the constant-order policy for long lead time and from the base-stock policy when the cost of losing a sale is large. The PIL policy has lower cost than competing heuristics in a numerical study. The PIL seems to be a promising approach for other inventory systems with a high-dimensional state space, such as the perishable item inventory management problem.

Yan, Xiaoyue; Chen, Li; Ding, Xiaobo

doi: 10.1287/opre.2022.0196pmid: N/A

Payables finance, also known as reverse factoring or supply chain finance, is a form of trade finance offered by a bank that provides a supplier with the option to receive a buyer’s payables early while allowing the buyer to extend its payment due date. There has been an increasing adoption of payables finance by various industries in recent years. In “Optimal Cash Management with Payables Finance,” X. Yan, L. Chen, and X. Ding characterize the supplier’s optimal cash policy under the payables finance arrangement with a buyer and a bank. The authors show that it is the supplier’s future cash flow uncertainty, together with the supplier’s risk averseness, that drives the cash liquidity value of payables finance. Numerical results of applying the analysis to data sets obtained from a major U.S. chemical company suggest that adopting payables finance can generate considerable value for the company and its suppliers.

Hu, Feihong; Mitchell, Daniel; Tompaidis, Stathis

doi: 10.1287/opre.2022.0272pmid: N/A

In “Robust Financial Networks,” F. Hu, D. Mitchell, and S. Tompaidis study networks of financial institutions where only aggregate information on liabilities is available. The authors introduce the robust liability network, that is, the network consistent with the available information that exhibits the worst expected losses. They provide an algorithm to identify the robust liability network and, using aggregate data provided by bank holding companies to the Federal Reserve in form FR Y-9C, determine robust liability networks for U.S. banks under various network configurations. They show that the robust liability network is sparse, with links between institutions that hold highly correlated portfolios. They illustrate the methodology in two applications. (1) They look at how robust liability networks changed around the onset of the COVID-19 pandemic. (2) They evaluate the impact of a potential regulation that limits risk-taking based on each institution’s conditional value-at-risk. Their results can be used by regulators to monitor systemic risk in financial networks.

Wang, Shouqiang; de Véricourt, Francis; Sun, Peng

doi: 10.1287/opre.2022.0289pmid: N/A

When companies or organizations can evade audits on their harmful incidents, how should the affected entities design their audit and penalty policies? In “Audit and Remediation Strategies in the Presence of Evasion Capabilities” by Wang, de Véricourt, and Sun, the authors find random audits may be needed in the optimal policy. Specifically, the optimal policy alternates between ascending monetary penalties (without any audits) and random audits at a constant rate (when the penalty reach its maximum level). Only when the evasion is ineffective or the self-correction is too costly do deterministic audits become optimal. They tackle the problem in a continuous-time principal-agent framework with both adverse selection and moral hazard.

Feng, Yiding; Niazadeh, Rad; Saberi, Amin

doi: 10.1287/opre.2020.0687pmid: N/A

Near-Optimal Bayesian Online Assortment of Reusable ResourcesMotivated by rental services in e-commerce, we consider revenue maximization in the online assortment of reusable resources for different types of arriving consumers. We design competitive online algorithms compared with the optimal online policy in the Bayesian setting, where consumer types are drawn independently from known heterogeneous distributions over time. In scenarios with large initial inventories, our main result is a near-optimal competitive algorithm for reusable resources. Our algorithm relies on an expected linear programming (LP) benchmark, solves this LP, and simulates the solution through independent randomized rounding. The main challenge is achieving inventory feasibility efficiently using these simulation-based algorithms. To address this, we design discarding policies for each resource, balancing inventory feasibility and revenue loss. Discarding a unit of a resource impacts future consumption of other resources, so we introduce postprocessing assortment procedures to design and analyze our discarding policies. Additionally, we present an improved competitive algorithm for nonreusable resources and evaluate our algorithms using numerical simulations on synthetic data.