Chung-Piaw Teo

Problem definition: In the aftermath of disasters, governments must make urgent decisions about how to deploy limited resources for recovery, such as restoring roads or siting emergency facilities. However, these actions often need to be taken before the amount and timing of external funding (e.g., federal disaster relief) are known. This mismatch between the need for immediacy and the delay in budget realization poses a fundamental challenge: How can agencies prioritize recovery actions when budgets are uncertain and decisions, once made, are irreversible? Methodology/results: We develop a practical planning framework that produces a fixed priority list of recovery actions, allowing agencies to act immediately and continue execution as funding arrives over time. The framework identifies early actions that perform well across a range of possible funding paths and preserve the value of later investments. The model is cast as a multiscenario mixed-integer linear program with monotonicity constraints, enforcing consistency in prioritization across all scenarios. To compute such a list efficiently, whereas the natural linear program relaxation of this formulation is weak, we introduce a pegging-based heuristic: For each scenario, we solve the optimal 0-1 allocation, fix it, and relax the remaining scenarios into a linear program. Aggregating across all scenarios yields a robust and interpretable prioritization list. Our analysis provides performance guarantees for committing to a single priority list instead of waiting for full budget information. We derive explicit bounds on the expected performance loss of any prioritization strategy relative to a full-information hindsight benchmark. These results show that, under modest assumptions, the loss from committing to a single priority list is provably small. Furthermore, our pegging-based heuristic yields approximation guarantees under mild conditions and performs remarkably well in empirical evaluations. Managerial implications: This framework offers disaster response planners a rigorous and practical tool for making irreversible decisions under budget uncertainty. The main insight is that the best early action is not always the one that gives the largest immediate gain, but the one that positions the system best when additional funding becomes available. Through experiments on synthetic data and a real-world road network in Manhattan, we demonstrate that the proposed prioritization strategy consistently outperforms conventional heuristics and closely approximates the performance of an ideal benchmark with full budget information. The results highlight the potential of our approach to support timely, resilient, and high-quality disaster recovery planning under uncertain funding conditions. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72422006] and the Hong Kong Research Grants Council Theme-based Research Scheme [Grant T32-615/24-R]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2024.1574 .

The Locker Alliance Network (LAN), a cutting-edge initiative under Singapore’s Smart Nation vision, aims to revolutionize parcel pickup processes. This government-led project seeks to implement two major changes in Singapore’s urban logistics: (i) establishing an open-access facility for all logistic service providers (LSPs) and (ii) enhancing the last mile delivery efficiency of each LSP, thereby reducing its operational footprint. This research investigates the impact of locker network design on the delivery efficiency of individual LSPs and explores strategies for the government to construct an inclusive network for all LSPs. Our study explores two operational modes: the two-trip mode, in which home and locker deliveries are managed separately, and the mixed-trip mode, which consolidates both into one route. We extend the Beardwood–Halton–Hammersley theorem to estimate the delivery trip length to remaining home locations, assuming consumers can choose between locker pickup or home delivery. This enables us to explicitly formulate the LAN design problem as a nonlinear optimization model. Furthermore, our models for both operational modes reveal that optimal locker network design tends to expand in a nested manner relative to the market share of an LSP. This finding suggests that governments could design locker networks based on the profiles of the largest LSP, ensuring that the expansion of these networks aligns with the existing logistics infrastructure. Leveraging data from LAN’s pilot program in Singapore, our research underscores the pivotal role of network design in last mile delivery efficiency. A poorly designed locker network can inadvertently reduce productivity by increasing the length of delivery trips, thus negating the potential benefits of the system. In contrast, an optimized network can mitigate this negative impact on delivery efficiency. Beyond network design, low customer adoption emerges as another critical bottleneck. Our findings show that, as more customers shift to locker pickup, especially in mixed-trip modes, delivery efficiency improves. Therefore, network design and a higher adoption rate of locker pickup are key to LAN’s success. The government’s role in driving deeper customer engagement, alongside efforts to optimize network design and delivery operations, is vital to unlocking the full potential of this smart nation initiative. This paper was accepted by David Simchi-Levi, operations management. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72422006], the Hong Kong Research Grants Council [Grants 16210720, 16500423], the 2019 Academic Research Fund Tier 3, the Ministry of Education, Singapore [Award MOE-2019-T3-1-010], and the Natural Science Foundation of Chongqing, China [Grant CSTB2022NSCQ-MSX1667]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04567 .

A Distribution-Free Playbook for Dual Sourcing In the paper “Dual Sourcing Made Easy: Distributionally Robust Optimization of an Inventory System Under Independent Demand,” the authors study a common dual-sourcing dilemma: a low-cost supplier with a long lead time versus a faster, more expensive backup. Rather than committing to a parametric demand model, they propose a distributionally robust formulation that relies only on the demand mean and variance, while leveraging independence across periods. This yields a simple, closed-form way to set a tailored base–surge (TBS) policy: regular orders cover the predictable component of demand, and emergency orders buffer the remaining uncertainty, with inventory targets reflecting lead-time differences. The resulting policy is lightweight to calibrate, easy to deploy, and straightforward to stress test. In a field application using data from a multinational food manufacturer, the robust TBS settings improve service performance and reduce stockouts relative to the firm’s current rule and standard benchmarks, without sacrificing cost efficiency under volatility.

In the post-pandemic era, multi-sourcing is rapidly becoming the preferred approach for companies to drive optimized cost, quality and turnaround times. However, multi-sourcing systems present a myriad of challenges in designing an effective planning model. In this article, we propose a robust capacity planning model with multiple supply sources, and demonstrate how the capacity plan can be efficiently solved via a parsimonious mean–variance approach. We apply the max-min criterion to design a distributionally robust multi-source capacity plan. We use a new approach to deriving a set of optimality conditions on the robust capacity plan. These conditions enable us to derive the worst distribution, the optimal robust capacity vector, and the worst-case expected profit in closed form. The new approach bypasses numerous tedious intermediate procedures in the traditional distributionally robust optimization literature and allows us to derive the optimal solution based on exogenous cost parameters. This closed-form solution appears to be hitherto unknown and has important ramifications for the multi-sourcing capacity planning problem. Our findings reveal that, despite the complexity of multi-sourcing, the worst-case demand scenario can be reduced to just n + 1 distinct outcomes, structured by two key sequences derived from supplier characteristics. The optimal capacity plan has a clear structure–allocations align with the midpoints between these demand scenarios. Surprisingly, we also find that most of the robustness benefits of a full supplier portfolio can be achieved by engaging just the two best-matched sources. This provides a practical and cost-effective road map for robust capacity planning in multi-sourcing environments.

Design and Pricing of a Single Bundle Product bundling is a widely used selling strategy among multiproduct firms, yet designing and pricing bundles optimally remain a complex challenge. In “Partition and Prosper: Design and Pricing of a Single Bundle,” Sun, Li, and Teo show that the selection and pricing of a single bundle from a range of products under multivariate normal valuations is polynomial-time solvable, provided that the associated covariance matrix can be decomposed into a positive diagonal matrix minus a positive semidefinite matrix of (small) fixed rank. Interestingly, they also show that if the individual product prices are predetermined, the optimization problem becomes NP hard even if customer valuations are independent. The authors use a Bayesian optimization algorithm combined with a novel conic integer programming reformulation to solve the problem under general valuation distributions and demonstrate the superior numerical performance of the algorithm via extensive simulations.

Problem definition: Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver-order matching process. Methodology/results: We formulate real-time matching algorithms that take into account uncertain food processing times to strategically “delay” the assignment of drivers to orders. This intentional delay is designed to create a “thicker” marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k-level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi-convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. Managerial implications: Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications. Funding: This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Award ref: MOE-2019-T3-1-010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0354 .

Problem definition: The job of any marketplace is to facilitate the matching of supply with demand in real time. Success is often measured using various metrics. The challenge is to design matching algorithms to balance the tradeoffs among multiple objectives in a stochastic environment, to arrive at a “compromise” solution, which minimizes say the [ ]-norm–based distance function between the attained performance metrics and the target performances. Methodology/results: We observe that the sample average approximation formulation of this multiobjective stochastic optimization problem can be solved by an online algorithm that uses only gradient information from “historical” (i.e., past) sample information and not on the current state of the system. The online algorithm relies on a set of weight functions, which are updated adaptively over time, based on real-time tracking of the gaps in attained performance and the performance target. This allows us to recast the online algorithm as a randomized algorithm to solve the original stochastic problem. When the predetermined performance targets are attainable, our randomized policy achieves the targets with a near-optimal performance guarantee (measured by regret, or deviation away from the optimal performance). When the targets are not attainable, our policy generates a compromise solution to the multiobjective stochastic optimization problem, even when the efficient frontier for this stochastic optimization problem cannot be explicitly characterized a priori. We implement our model to address a challenge faced by a ride-sourcing platform that matches passengers and drivers in real time. Four performance metrics—platform revenue, driver service score, pick-up distance, and number of matched pairs—are simultaneously considered in the design of ride-matching algorithm, without prespecifying the weight on each performance metric. This mechanism has been extensively tested using synthetic and real data. Managerial implications: We show that, under appropriate conditions, all parties in the ride-sourcing ecosystem, from drivers, passengers, to the platform, can be better off under our compromise matching policy compared with other popular policies currently in use. In particular, the platform can obtain higher revenue and ensure better drivers (with higher service scores) are assigned more orders, and passengers are more likely to be matched to better drivers (albeit with a slight increase in the waiting time) compared with existing policies that focus on pick-up distance minimization. The ability to balance the conflicting goals in multiple objectives in a stochastic operating environment has the potential to contribute to the long-term sustainable growth of ride-sourcing platforms. Funding: This work was supported by the Singapore Ministry of Education AcRF Tier 3 [Grant MOE-2019-T3-1-010], the Hong Kong University of Science and Technology [Grant R9827], the Singapore Management University [Lee Kong Chian Fellowship], and the Singapore Ministry of Education AcRF Tier 2 [Grant T2EP20121-0035]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0247 .

Problem definition: In many resource scheduling problems for services with scheduled starting and completion times (e.g., airport gate assignment), a common approach is to maintain appropriate buffer between successive services assigned to a common resource. With a large buffer, the chances of a “crossing” (i.e., a flight arriving later than the succeeding one at the gate) will be significantly reduced. This approach is often preferred over more sophisticated stochastic mixed-integer programming methods that track the arrival of all the flights to infer the number of “conflicts” (i.e., a flight arriving at a time when the assigned gate becomes unavailable). We provide a theoretical explanation, from the perspective of robust optimization for the good performance of the buffering approach in minimizing not only the number of crossings but also the number of conflicts in the operations. Methodology/results: We show that the buffering method inherently minimizes the worst-case number of “conflicts” under both robust and distributionally robust optimization models using down-monotone uncertainty sets. Interestingly, under down-monotone properties, the worst-case number of crossings is identical to the worst-case number of conflicts. Using this equivalence, we demonstrate how feature information from flight and historical delay information can be used to enhance the effectiveness of the buffering method. Managerial implications: The paper provides the first theoretical justification on the use of buffering method to control for the number of conflicts in resource assignment problem. Funding: This work was supported by the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010] and the Research Grants Council of Hong Kong SAR, China [Grant PolyU 152240/17E]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0572 .

Problem definition: “Long tail” products with intermittent demand often tie up valuable warehouse space and capital investment for many companies. Furthermore, the paucity of demand data poses additional challenges for model estimation and performance evaluation. Traditional inventory solutions are not designed for products with intermittent demand. In this paper, we propose a new framework to optimize the choice of “replenishment timing” and “replenishment quantity” for managing the inventory metrics of long tail products, when evaluated over a finite horizon. Methodology/results: Our analysis is motivated by a recent interesting observation that the gambler’s fallacy phenomenon actually holds in a finite number of coin tosses. We use this phenomenon to analyze the inventory problem for intermittent demand to demonstrate that classical inventory models using KPIs such as fill rate, average cost per cycle, or average cost per unit, etc., must necessarily “bias” the underlying demand distribution to account for the finite horizon effect. We provide the exact closed-form expressions of the biased distribution to account for this effect in performance evaluation. The results show that the choice of replenishment timing and replenishment quantity is essential to superior performance on several key inventory metrics. Managerial implications: For long tail products, the belief that it is less likely for another demand to arrive shortly after a preceding one (the gambler’s fallacy), turns out to be true when performance is tabulated over a finite horizon, even if demands across time are independent. So it pays to delay the replenishment of depleted stocks to save on holding cost and warehouse space. Managers can optimize the replenishment timing, besides choosing the replenishment quantity, to optimize the performance metrics of several classes of inventory problems. This is especially useful for companies managing a large number of long tail products. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 72192832] and the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1217 .

Problem definition: Online platforms that provide on-demand services are often threatened by the phenomenon of leakage, where customer-provider pairs may decide to transact “off-platform” to avoid paying commissions to the platform. This paper investigates properties of services that make them vulnerable or resistant to leakage. Academic/practical relevance: In practice, much attention has been given to platform leakage, with platforms experimenting with multiple approaches to alleviate leakage and maintain their customer and provider bases. Yet, there is a current dearth of studies in the operations literature that systematically analyze the key factors behind platform leakage. Our work fills this gap and answers practical questions regarding the sustainability of platform. Methodology: We develop two game-theoretical models that capture service providers’ and customers’ decisions whether to conduct transactions on or off the platform. In the first (“perfect information”) model, we assume that customers are equipped with information to select their desired providers on the platform, whereas in the second (“imperfect information”) model, we assume customers are randomly matched with available providers by the platform. Results: For profit maximizing platforms, we show that leakage occurs if and only if the value of the counterparty risk from off-platform transactions exceeds a threshold. Across both models, platforms tend to be more immunized against leakage as provider pool sizes increase, customer valuations for service increase, their waiting costs decrease, or variability in service times are reduced. Finally, by comparing the degree of leakage between both settings, we find that neither model dominates the other across all parameter combinations. Managerial implications: Our results provide guidance to existing platform managers or entrepreneurs who are considering “platforming” their services. Namely, based on a few key features of the operating environment, managers can assess the severity of the threat of platform leakage for their specific business context. Our results also suggest how redesigning the waiting process, reducing service time variability, upskilling providers can reduce the threat of leakage. They also suggest the conditions under which revealing provider quality information to customers can help to curb leakage. Funding: J. Goh’s work was supported by a National University of Singapore Start-Up [Grant R-314-000-110-133] and a 2021 Humanities and Social Sciences Fellowship from the National University of Singapore. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1179 .