Search

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform's pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform's expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133. [ABSTRACT FROM AUTHOR]

Service and manufacturing firms often attempt to mitigate demand-supply mismatch risks by deploying flexible resources that can be adapted to serve multiple demand classes. It is critical to evaluate the trade-off between the cost of investing in such resources and the resulting benefits. In this paper, we show that the heavily advocated “chaining” heuristic can sometimes perform unsatisfactorily when resources are not perfectly flexible. Alternatively, we propose an integer stochastic programming formulation as an attempt to optimize the flexibility structure. Although it is intractable to compute the optimal solution exactly, we propose a Lagrangian-relaxation heuristic that generates high-quality solutions efficiently. Using computational experiments, we identify conditions under which our approach can outperform the popular chaining solution.

Abstract. Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are [...]

Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients' overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.1210. [ABSTRACT FROM AUTHOR]

Key to the mass adoption of electric vehicles (EVs) is the establishment of successful business models based on sound understanding of consumer behavior in adopting this new technology. In this [...]

The benefits of inventory risk pooling are well known and documented. It has been proven in the literature that the expected costs of a centralized system are increasing in the [...]

Managing appointments for service systems with random job durations is a challenging task. We consider a class of appointment planning problems that involve two sets of decisions: job sequencing, i.e., [...]

Distributing scarce resources such as COVID‐19 vaccines is often a highly time‐sensitive and mission‐critical operation. Our research was prompted by a significant obstacle that the United States and other nations encountered during the early months of the COVID‐19 vaccination campaign: Most COVID‐19 vaccines require two doses given 3 or 4 weeks apart. Given the severely limited supply and mounting pressure on many countries to reduce hospitalizations and mortality, how to effectively roll out two‐dose vaccines was a critical policy decision. In this paper, we first model and analyze inventory dynamics of the rollout process under three rollout strategies: (1) holding back second doses, (2) releasing second doses, and (3) stretching the lead time between doses. Then we develop an SEIR (susceptible, exposed, infectious, recovered) model that incorporates COVID‐19 asymptomatic and symptomatic infections to evaluate these strategies in terms of infections, hospitalizations, and mortality. Among our findings, we show releasing second doses reduces infections but creates uneven vaccination patterns. In addition, to ensure second doses are given on time without holding back inventory, strictly less than half of the supply can be allocated to first‐dose appointments. Stretching the between‐dose lead time flattens the infection curve and reduces both hospitalizations and mortality compared with the strategy of releasing second doses. We also consider an alternative single‐dose vaccine with lower efficacy and show that the vaccine can be more effective than its two‐dose counterparts in reducing infections and mortality. We conduct extensive sensitivity analyses related to age composition, risk‐based prioritization, supply disruptions, and disease transmissibility. Our paper provides important implications for policymakers to develop effective vaccine rollout strategies in developed and developing countries alike. More broadly, our paper sheds light on how to develop effective operations strategies for distributing time‐sensitive resources in times of crisis. [ABSTRACT FROM AUTHOR]

Recent development of new technologies and data analytics has unleashed the so-called smart city (SC) movement, which has been transforming the ways that cities (and their services) operate. This movement is in line with the operations management (OM) community's pursuit of innovative research questions, and part of our community has already started working on this exciting area of SC OM. In this article, we shall discuss (i) the potential contribution that OM can make to the SC movement, that is, how OM research may help inform discussion and decision making in the public and private sectors. In particular, the core principle of making cities smarter is in line with the OM literature. We then discuss (ii) how and why SC operations can be an interesting topic that possibly expands the scope and depth of OM theory, through drawing links to the existing and new streams of OM literature. Finally, we present (iii) a few promising research directions and examples, in domains such as energy, transportation (mobility), and retail. [ABSTRACT FROM AUTHOR]

The Triple‐A supply chain (Lee 2004) has become one of the most influential concepts for practitioners and researchers in supply chain management. It stipulates that supply chains should strive to improve along the dimensions of agility, adaptability, and alignment, as opposed to focusing exclusively on cost and efficiency improvements. While various strategies have since been adopted across different industries to foster the Triple‐A, the digitalization movement poses both new challenges and opportunities for developing Triple‐A supply chains. In this article, we shall discuss an emerging mode of supply chain innovation, known as the consumer‐to‐manufacturer (C2M) model, and how it enables new possibilities for achieving Triple‐A "digital" supply chains. C2M establishes digital links between end consumers and upstream manufacturers and product designers, and provides a variety of tactics to shorten the information flow process of the supply chain. Our discussion focuses on the implementation of C2M at JD.com, a leading online retailer in China. [ABSTRACT FROM AUTHOR]

We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community. [ABSTRACT FROM AUTHOR]

We examine the operations, fairness, and social implications of fair trade certified products. We consider the market for fair trade certified products, which may serve as a substitute to a regular product. A fair trade organization chooses standards for the certification, namely, the (unit) premium and minimum fraction of fair trade raw materials, to maximize the total premium transferred to farmers. We analyze the operations of various stakeholders in the fair trade value chain, as well as the role and social welfare of fair trade organizations with different philosophies. We characterize the firms' entry decision in the fair trade market as well as the resulting market equilibrium. Furthermore, we examine the impact of mainstreaming, i.e., whether to allow large‐scale plantations (often owned by large corporates) to be eligible for fair trade certification or not, which is one of the most discussed topics in the fair trade movement. We also identify the welfare allocation among various stakeholders in the value chain under different certification policies, and thereby address the fairness issue of the fair trade market as well as its resulting social welfare implications. Finally, we obtain a number of policy insights and suggestions to support this nascent marketplace. [ABSTRACT FROM AUTHOR]

Recent research has pointed out that the optimal strategies to mitigate supply disruptions and demand uncertainty are often mirror images of each other. In particular, risk diversification is favorable under the threat of disruptions and risk pooling is favorable under demand uncertainty. This article studies how dynamic sourcing in supply chain design provides partial benefits of both strategies. Optimization models are formulated for supply chain network design with dynamic sourcing under the risk of temporally dependent and temporally independent disruptions of facilities. Using computational experiments, it is shown that supply chain networks that allow small to moderate degrees of dynamic sourcing can be very robust against both disruptions and demand uncertainty. Insights are attained on the optimal degree of dynamic sourcing under different conditions. [ABSTRACT FROM PUBLISHER]

The article presents a study on the optimization of the location of two-echelon spare parts inventory system along with service considerations. A mixed integer nonlinear programming model (MINLP) was formulated to be used in designing service parts network that will ensure short response times. It reveals that using a response time requirement is less costly in the achievement of a target response time than through penalty costs.