Knowledge that Transforms

The decarbonization of power systems facilitates the electrification of appliances, many of which can be operated in a flexible way. Demand response (DR) programs can exploit this flexibility with retail price adjustment, thereby addressing several operational challenges. In this paper, we address the welfare optimization problem of local utilities that procure electricity for their customers at the wholesale market. We demonstrate how DR programs can be designed for local electricity systems where electricity demand and its response to temporary price changes is unknown. For this purpose, we address a novel and complex pricing problem—pricing under unknown, time-interdependent, and discontinuous demand—leveraging Deep Reinforcement Learning. Using a numerical case study calibrated on Californian electricity market data, we show that such a “Deep DR program” helps to identify effective prices that improve social welfare. The performance of the program is consistently positive across a variety of system conditions. We further demonstrate that our approach beats Time-of-Use tariff-based benchmarks already after five and a parametric benchmark after 19 simulation days, on average. Second, we provide novel insights regarding an important but frequently overlooked aspect of DR program design: The length of the notification interval, that is the timespan for which future prices must be set in advance. We find that the timing of price information is important and that longer notification intervals can improve social welfare. Finally, we provide insights into DR price setting and find that DR prices co-move with wholesale market prices but are lower for longer notification intervals and shorter event sequences. The presented Deep DR program provides an example of how advances in machine learning-based algorithms can help to meet the complex operational requirements of future local electricity systems.

Maximizing profit and minimizing waste (leftover inventory) can be two conflicting objectives during clearance sales. B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>x</mml:mi> </mml:math> G <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>y</mml:mi> </mml:math> policies (buy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>x</mml:mi> </mml:math> units at full price and get <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>y</mml:mi> </mml:math> units free) are popular promotion policies for achieving these objectives. We consider four scenarios depending on whether customers are homogeneous/heterogeneous in their valuations and whether the retailer’s objective is to maximize profit/minimize waste. In each scenario, we characterize the structure of the optimal promotion policy (OPP). We categorize the two kinds of OPPs based on the retailer’s objective as profit-based and clearance-based OPPs. If customers are homogeneous, we show that B1G <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>y</mml:mi> </mml:math> policies are always optimal. However, if they are heterogeneous, B1G <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>y</mml:mi> </mml:math> policies may not maximize profit, resulting in a complex structure for profit-based OPPs. Therefore, we focus on B1G0, B1G1, and B2G1 policies due to their prevalence among retailers. We then derive how both kinds of OPPs change with quantity. We explain the impact of quantity using the ideas of bundling effect and differentiation effect . We perform a numerical study to examine (i) the impact of the two kinds of OPPs on profit and waste, and (ii) the impact of demand uncertainty. Finally, we discuss how our results apply to several practical instances.

We study the impact of planogram design (i.e., placement of products on shelves) and display fees (i.e., fees manufacturers pay retailers for prime shelf space) on a retailer’s category management strategy and interactions with national brand manufacturers (NBMs). We consider a game-theoretic model with one retailer and multiple NBMs. Each NBM offers one product. In addition, the retailer has the option to introduce a store brand (SB) product. If the retailer decides to introduce its SB, it needs to drop one of the national brand (NB) products from its assortment because it has limited shelf space. The retailer’s planogram has one prime shelf space and multiple nonprime shelf spaces. The prime shelf space has a demand-stimulating impact. The NBMs determine their wholesale prices and how much they are willing to pay for the prime shelf space. The retailer makes SB introduction and planogram decisions. It also sets the quantities for each product in its assortment, resulting in market-clearing retail prices. Our analysis leads to three key findings: first, the presence of a prime shelf space may not only prevent the retailer from introducing its SB but also lead to sizable changes in the retail and wholesale prices in the category. For example, an extensive numerical study reveals that receiving the prime shelf space increases an NB product’s retail price by 5.48%, on average. The average increase in the same product’s wholesale price is 2.52%, as the prospect of charging a higher wholesale price incentivizes its manufacturer to pay a display fee for securing the prime shelf space. Second, there are cases in which the retailer uses SB introduction as a strategic lever to intensify the competition for the prime shelf space and thereby increase its revenue from display fees. Third, despite being an additional expense, display fees can increase the NBMs’ profits by allowing them to influence the retailer’s assortment and planogram decisions in their favor. We discuss the implications of these findings for managers and researchers.

We collaborate with a leading mushroom producer in North America to investigate the potential benefits of redesigning agribusiness operations, specifically, harvesting processes, to enhance both firm performance and working conditions of employees. We consider two harvesting protocols: The dominant status-quo practice, namely Harvest-all and an alternative, namely Selective Harvesting. In the Harvest-all protocol, workers pick all crop units in the areas assigned to them, regardless of the crops’ size and maturity, resulting in higher productivity but lower average value (quality) of the harvested produce. Workers also remain in demanding physical postures for longer periods of time, resulting in ergonomic stress. In contrast, under the Selective Harvesting protocol, workers take multiple rounds to completely harvest the assigned area, picking in each round only the select crop units that are near their peak monetary value. This results in lower productivity but higher average quality of the harvested produce and better ergonomic conditions. We develop mathematical models to analyze the two harvesting protocols, provide a complete analytical characterization of the optimal managerial decisions under each protocol, and examine how these decisions are influenced by relevant contextual factors. In addition, we characterize the performance of each harvesting protocol along three key performance metrics of interest: Firm profitability, worker monetary welfare, and worker ergonomic welfare. Using both analytical and calibrated numerical methods we show that adopting Selective Harvesting over Harvest-all can create win–win scenarios where both the firm and workers are better off. However, our analysis reveals that the firm’s and workers’ interests may not always be fully aligned. We subsequently demonstrate that the misalignment can be reduced by making adjustments to the compensation structure so that workers’ earnings match the maximum potential value while having minimal impact on firm profitability. Our models illustrate the benefits of careful process redesign in creating better working conditions for employees and advancing firms’ social responsibility practices.

Order splitting is one of the key issues in the e-commerce order fulfillment process. It increases operational costs, elevates carbon emissions, and compromises customer satisfaction. This article focuses on determining the product assortments to store within the multi-warehouse logistics network to minimize the total number of split orders subject to cardinality constraints. We show that this minimizing split orders (MSO) problem is NP-hard and demonstrate that even finding an optimal order fulfillment strategy with a given assortment selection is NP-hard. To further analyze the MSO problem, we introduce a concept termed the second-order dominant indexing rule . This indexing rule corresponds to a group of demand distributions, under which we are able to characterize the structure of the optimal assortment selection for various scenarios. In particular, when assortment overlapping is prohibited, the optimal selection can be explicitly derived. When the demand exhibits a total nested structure, an optimal selection is non-overlapping with more popular products allocated to larger warehouses. We also bridge the two-warehouse order splitting minimization problem with the single-warehouse assortment selection problem in the literature. Building upon this connection, we propose an extended marginal choice indexing (MCI) policy, which is proven to achieve optimality when the demand has a second-order dominant MCI. In addition, we propose an Iterative Improvement Heuristic that refines any existing assortment selection. The efficiency of the proposed heuristics is validated by extensive numerical experiments, demonstrating that the extended MCI policy performs near-optimally even when customer demand is not ideal, and both heuristics outperform the best benchmark in existing literature. Additional experiments on real-world data further confirm their effectiveness and scalability. Finally, we extend our findings to a two-tier multi-warehouse scenario with a back-end warehouse.

This study examines a patient scheduling problem with multiple appointment types and priority levels, where certain appointments must precede others and lead times play a crucial role. Although both factors significantly influence the quality of care-particularly when specialist assessments depend on timely diagnostic tests-they have been largely overlooked in existing healthcare scheduling models. To address this gap, we propose a dynamic scheduling model that explicitly incorporates appointment dependencies, lead times, and patient heterogeneity across multiple priority levels. The model reflects the real-world complexities of coordinating diagnostic and consult appointments in time-sensitive clinical settings. Using Approximate Dynamic Programming techniques, we develop an Approximate Optimal Policy (AOP) that efficiently allocates clinical resources, minimizes patient wait times, and ensures the availability of test results prior to consult appointments. We further derive a closed-form solution for the optimal approximation parameters, supported by a mathematical proof, which offers significant computational advantages. We evaluate the performance of the proposed AOP through simulation and compare it against a set of benchmark policies, including heuristics adapted from existing scheduling logic and current clinical practice. The solution is applied to a case study created based on data from a Stroke Prevention Clinic (SPC), where the complexity of care protocols and high demand present substantial scheduling challenges. The results demonstrate that the AOP consistently outperforms all benchmarks in terms of reducing wait times, ensuring timely diagnostic completion before consults, and meeting wait-time targets. We also introduce a practical, easy-to-implement heuristic called (MSP), which is derived from the AOP and designed for operational use. While simpler in structure, MSP performs comparably well and is well-suited for adoption in real healthcare settings due to its interpretability and minimal computational requirements. Finally, although the proposed approach is demonstrated in the context of an SPC, it has broader applicability to other areas such as cancer care, kidney transplant scheduling, and other complex care pathways involving interdependent appointments and prioritization.

Leveraging government tax incentives to prompt corporate charitable giving has gained considerable popularity over the past decade. This paper sheds light on the broader consequences of the U.S. government's tax-subsidy policy for charitable food donations, which is determined based on the fair market value (FMV) of the donated products. We incorporate the tax-subsidy into a monopolist food retailer's after-tax profit function. Market demand is both price- and quality-dependent, and the shelf-life of the goods is determined by their initial quality and deterioration rate. The retailer makes joint quantity and pricing decisions over two periods, procuring goods at the start of the selling season and (possibly) donating at the end of period 1. We characterize the retailer's optimal policy and specify conditions under which she donates some, all, or none of her leftover inventory. We explore the impact of government tax-subsidies on the retailer's actions, consumer surplus, quantity of donations, and total welfare. We show that tax-subsidies may motivate retailers to intentionally create supply scarcity (by donating more) to increase FMV (determined by the second-period price), thereby enhancing tax deductions. While tax-subsidies encourage donations, they can also unintentionally harm consumers by reducing supply and raising prices. Interestingly, we show that a higher subsidy does not necessarily lead to more donations; the retailer may choose to donate fewer units to achieve the same tax deduction while increasing sales revenue. We investigate conditions under which tax-subsidies can simultaneously increase donations, consumer surplus, and retail profit. We show that this outcome is possible only when retailers donate low-quality goods in modest quantities. Our findings reveal how FMV-dependent tax-subsidies can backfire, reducing both consumer surplus and total welfare while benefiting the retailer. Governments must carefully weigh the benefits of donations against potential harm caused to consumers.

We study the effects of hospital consolidations on access to care in a competitive healthcare market in the presence of performance incentives. We consider a transition from a “pre-merger” market configuration where a payer delegates services to a market with two competing hospitals to a “horizontally consolidated” configuration under which the same entity manages two hospitals. We focus on two research questions: How should the payer set access-based performance incentives in response to a horizontal market consolidation? What are the effects of consolidation on patient access to care? We use queueing dynamics to describe patient care processes and analyze the strategic interactions between hospitals and the optimal design of performance-based incentives in different market configurations. Specifically, we consider contracts where hospital compensation for delivering care includes a combination of fee-for-service payments and bonus components tied to the level of access to care patients receive. We derive the optimal bonus-type contracts that the payer can use to adapt to changes in market concentration, and we quantify the resulting impact on patient access to care and social welfare. In our analysis, we consider two alternative settings: the “fixed-FFS” setting, where the fee-for-service component cannot be altered, and the “flexible-FFS” setting, where both the fee-for-service and the bonus components can be adjusted in response to changes in the market composition. Our analysis shows that the bonus-type performance incentives provide the payer with the ability to protect patient access to care upon hospital consolidation in a wide range of problem settings, provided that the intensity of competition between the hospitals is sufficiently strong. If, otherwise, the intensity of competition is relatively weak, horizontal consolidation may compromise access to care.

This study examines the relation between political uncertainty arising from state-level election cycles and the timing of employee dismissal and plant closure notices filed by US firms under the Worker Adjustment and Retraining Notification (WARN) Act of 1988 (hereafter, WARN notices). We appeal to a real options framework to predict that firms delay layoff decisions and the issuance of WARN notices until the resolution of political uncertainty. Using establishment-level data on layoffs disclosed in WARN notices and state elections occurring between 1994 and 2022, we document that the likelihood of issuing WARN notices declines during the election quarter but increases in the subsequent quarter. Cross-sectional findings show that political uncertainty plays a significant role in the timing of WARN notices during election periods while other factors, including partisanship, economic conditions, union strength, and firm visibility, may also play a role. Further, firms that delay WARN notices do not experience a significant deterioration in their medium-term financial performance. Overall, our findings provide evidence that firms delay labor adjustment decisions and the announcements of such decisions in response to political uncertainty.

We analyze how <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>N</mml:mi> </mml:math> firms producing a commodity good with a polluting by-product respond to environmental taxes. These firms vary in operational and environmental efficiency. Under Cournot competition, we examine two demand functions: iso-elastic and linear. We establish the existence and uniqueness of the equilibrium and examine how it changes with tax levels and environmental efficiency. Our findings suggest that taxes may not always benefit firms with green, low-cost technologies, as they erode the advantages of operational efficiency. Additionally, total and per-unit emissions may increase with higher taxes or improved environmental efficiency. Finally, the observed effects can qualitatively differ based on the form of market demand.