- Project Networks and Reallocation Externalities,
with Harish Krishnan and Juan Serpa
Major revision at Management Science
- Selected to MSOM Supply Chain SIG conference (2022)
- Selected to 1st ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'21)
AbstractA project involves several “participants” - including agencies, contractors, and subcontractors - all working concurrently on multiple projects and allocating resources among them. This interdependency creates a network of otherwise unrelated projects. By constructing the largest project network ever mapped, we track the timelines of 2.6 million infrastructure projects involving 140,000 participants. We show that a seemingly localized disruption, affecting only one project site, eventually causes delays and penalties across unrelated projects. This is because self-interest drives participants to opportunistically reallocate resources into disrupted projects, at the expense of other projects, triggering a domino effect of further reallocations in the network. Thus, the costs of on-site disruptions end up being evenly shared by multiple participants within the network, rather than being fully absorbed by the affected project. Performance-based contracts, which reward contractors for timeliness, exacerbate these externalities by encouraging self-interested resource reallocation.
- Selected to MSOM Supply Chain SIG conference (2022)
- The Effect of Expedited Payments on Project Delays:
Evidence from QuickPay Reform,
with Volodymyr Babich, Harish Krishnan, and Jie Ning
AbstractContractors are usually not paid immediately after an invoice is submitted. Such payment delays affect the value of projects to contractors, their financing costs, decisions about project sequencing and acceptance, as well as competition among contractors. We study how these consequences of project payment delays translate into projects delays. We develop theories about the link between the two delays and test the corresponding predictions using the U.S. government's project data, taking advantage of an exogenous shock from the QuickPay reform that expedited payments to certain federal contractors. We find that, surprisingly, faster payments led to greater project delays. Compared with the pre-reform delay rate per quarter, there is a 26% increase, on average, in delay rate for projects subject to the reform. Both the likelihood of a project delay and the expectation of the delay magnitude conditional on it being positive are significantly higher for the affected projects after the reform. We present evidence indicating that project delays can be explained by liquidity constraints of the contractor, and by competition among contractors. We identify other factors that moderate the effect of QuickPay. For example, the delays are not as severe if a treated project is in the same portfolio with projects that do not receive faster payments, or if it is at an early stage in its life. We show that the delay is not caused by contractors taking on more projects in response to the reform.
- Managing Reputation Risk in Supply Chains: The Role of Risk-sharing under Limited Liability,
with Harish Krishnan
Management Science, 67(8): 4845-4862, 2021
AbstractWhen a supplier fails to comply with social and environmental standards, the buyer’s reputation suffers. Reputation costs can typically be very high for the buyer whereas the supplier’s liability is often limited. Conventional procurement strategies like dual sourcing mitigate the buyer’s operational risk, but often do so at the expense of increasing its reputation risk and sourcing costs. In this paper, we propose a risk-sharing contract for managing the buyer’s reputation concerns. We find that by sharing some of the supplier’s operational loss, the buyer may (in some conditions) decrease its reputational risk but this has to be balanced against an increase in the operational risk. Risk-sharing also reduces sourcing costs because the buyer takes on some of the worst-case loss of a wealth-constrained supplier. These results suggest that risk-sharing can be superior, as a procurement strategy, to conventional approaches like dual sourcing or penalty contracts. This is true when reputation and sourcing costs are a significant concern, and operational costs are not that high. Under some conditions, the buyer may choose risk-sharing even if it increases reputation risk in order to reduce procurement costs.
- Solving Semi-open Queuing Networks with Time-varying Arrivals: An Application in Container Terminal Landside Operations,
with Govind Kumawat, Debjit Roy, and René de Koster
European Journal of Operational Research, 267(3): 855-876, 2018
AbstractSemi-open queuing networks (SOQNs) are widely applied to measure the performance of manufacturing, logistics, communications, restaurant, and health care systems. Many of these systems observe variability in the customer arrival rate. Therefore, solution methods, which are developed for SOQNs with time-homogeneous arrival rate, are insufficient to evaluate the performance of systems which observe time-varying arrivals. This paper presents an efficient solution approach for SOQNs with time-varying arrivals. We use a Markov-modulated Poisson Process to characterize variability in the arrival rate and develop a matrix-geometric method (MGM)-based approach to solve the network. The solution method is validated through extensive numerical experiments. Further, we develop a stochastic model of the landside operations at an automated container terminal with time-varying truck arrivals and evaluate using the MGM-based approach. Results show that commonly used time-homogeneous approximation of time-varying truck arrivals is inaccurate (error is more than 15% in expected waiting time and expected number of trucks waiting outside the terminal) for performance evaluation of the landside operations. The application results are insightful in resource planning, demand leveling, and regulating the number of trucks permitted inside the terminal.
- A Cooperative Quay Crane-based Stochastic Model to Estimate Vessel Handling Time,
with Debjit Roy and René de Koster
Flexible Services and Manufacturing Journal, 29: 97-124, 2017
AbstractHaving a good estimate of a vessel’s handling time is essential for planning and scheduling container terminal resources, such as berth positions, quay cranes (QCs) and transport vehicles. However, estimating the expected vessel handling time is not straightforward , because it depends on vessel characteristics, resource allocation decisions, and uncertainties in terminal processes. To estimate the expected vessel handling time, we propose a two-level stochastic model. The higher level model consists of a continuous-time Markov chain (CTMC) that captures the effect of QC assignment and scheduling on vessel handling time . The lower level model is a multi-class closed queuing network that models the dynamic interactions among the terminal resources and provides an estimate of the transition rate input parameters to the higher level CTMC model. We estimate the expected vessel handling times for several container load and unload profiles and discuss the effect of terminal layout parameters and crane service time variabilities on vessel handling times. From numerical experiments, we find that by having QCs cooperate, the vessel handling times are reduced by up to 15 %. The vessel handling time is strongly dependent on the variation in the QC service time and on the vehicle travel path topology.
- Modeling Emergency Evacuation with Time and Resource Constraints: A Case Study from Gujarat,
with Debjit Roy
Socio-Economic Planning Sciences, 51: 23-33, 2015
AbstractThis study develops an off-site emergency response plan for a nuclear power plant in Gujarat, India subject to time constraints with resource limitations and risk of radiation exposure to victims. We formulate an optimization model to capture the effect of delay in evacuation, limited resource availability, and costs associated with resource allocation. A single chain closed queuing network model with class switching is used to model traffic congestion during evacuation. The throughput measures from the queuing network are used as inputs in the optimization model. Further, two resource allocation strategies are suggested and genetic algorithm is used for optimizing resource utilization and evacuation risk. The results indicate that pooling resources among a cluster of affected areas is most suitable for evacuation. Numerical experiments are conducted to analyze the time trade-offs and the effect of service time variability on the expected evacuation time. The proposed model can serve as an important resource planning and allocation tool for emergency evacuation.
- A Two-level Stochastic Model to Estimate Vessel Throughput Time,
with Debjit Roy and René de Koster
Progress in Material Handling Research: 2014, Material Handling Institute, Charlotte, NC.
Work in Progress
- Financial penalties and responsible operations: Theory and evidence from the mining
with Anna Sáez de Tejada Cuenca
- Supply network formation under risk,
with Kashish Arora and Sripad Devalkar
- Are Chief Sustainability Officers guardians of ESG violations? An empirical evaluation,
with Finn Peterson and Rachna Shah