Showing papers in "Naval Research Logistics in 2021"

Subsidy design in a vessel speed reduction incentive program under government policies

Abstract: As a green port and shipping‐related policy, the vessel speed reduction incentive program (VSRIP) involves using a subsidy to induce ships to reduce their speed in a port area so that the emissions can be reduced at the port. However, this program may attract new ships to visit the port because of the subsidy; in this case, the port's profit will grow due to more ship visits, but its total emissions may also increase, which is counter to the original intention of the subsidy. The government could then intervene by providing part of the subsidy for the VSRIP or by collecting air emission taxes for the increased emission at the port. This paper studies how to design suitable subsidies for ships participating in a VSRIP. Two bilevel subsidy design models are formulated based on a Stackelberg game to maximize the port's profit (related to the profits from original and new ships, the subsidy provided by the port, and air emission taxes) and to minimize the government's cost (related to the damage cost of air emissions, the subsidy provided by the government, and air emission taxes). We determine which policy (including a sharing subsidy policy, no government intervention, and an air emission tax policy) should be implemented by the government in different cases and how much subsidy should be provided by the port under each government policy. We find that these decisions are affected by several practical factors, such as the damage cost of air emissions per ton of fuel and the subsidy sensitivities of original and new ships. We also outline several meaningful insights based on the analysis of these practical factors.

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Abstract: We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.

A Stochastic Programming Approach for Chemotherapy Appointment Scheduling

Abstract: Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability and number of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19

Abstract: Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic When testing resources are scarce, an important managerial decision is who to test This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms) Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

Abstract: The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.

Responsive make-to-order supply chain network design

Abstract: In this paper we address the network design of a responsive supply chain consisting of make-to-order (make-to-assemble) facilities facing stochastic demand from customers residing at nodes of a network. Each facility has a finite (processing) capacity and thus the stochasticity of demand may lead to congestion delays at the facilities. The objective is to determine the number, locations and capacities of the facilities so as to minimize the total network cost. We consider three problems. In the first, we minimize the total network cost which includes delivery and capacity costs while maintaining an acceptable response time to customers. In the second, a penalty is charged on the number of units that are delivered later than the targeted response time. In the third the penalty charged depends also on the number of days that the delivery is late. In both problems 2 and 3 the penalty cost is a function of network’s response time.

The outsourcing conundrum: Misappropriation of intellectual property in supply chains

Abstract: In an outsourcing arrangement, the principal must weigh the potential savings in manufacturing cost against the risk of intellectual property (IP) misappropriation by suppliers. While formal legal measures for protecting IP exist in many countries, they are by no means perfect. We consider a market‐based approach where the supplier can sell the acquired IP to a third party imitator or can become an imitator if the imitation‐cost is not large. We identify economic‐equilibrium scenarios that discourage excessive misappropriation of IP. We find that IP sell/purchase transactions can happen only if the market potential has a moderate value. It can also motivate the supplier to become imitator. A large market, while attractive to the imitator, causes the principal not to outsource. In a small market, on the other hand, the imitator is unable to compete especially with a high‐quality imitation. Interestingly, we also find that the combined sales of the principal and imitator decreases, if the imitator increases IP purchase. We establish that the principal can benefit from partial outsourcing implying that a proportion of the components are outsourced, if the market potential is large.

Steady-state analysis of load balancing with Coxian-2 distributed service times

Abstract: This paper studies load balancing for many-server ($N$ servers) systems assuming Coxian-$2$ service time and finite buffer with size $b-1$ (i.e. a server can have at most one job in service and $b-1$ jobs in queue). We focus on steady-state performance of load balancing policies in the heavy traffic regime such that the load of system is $\lambda = 1 - N^{-\alpha}$ for $0<\alpha<0.5.$ We identify a set of policies that achieve asymptotic zero waiting. The set of policies include several classical policies such as join-the-shortest-queue (JSQ), join-the-idle-queue (JIQ), idle-one-first (I1F) and power-of-$d$-choices (Po$d$) with $d=O(N^\alpha\log N)$. The proof of the main result is based on Stein's method and state space collapse. A key technical contribution of this paper is the iterative state space collapse approach that leads to a simple generator approximation when applying Stein's method.

Interpretable biomanufacturing process risk and sensitivity analyses for quality-by-design and stability control

Abstract: While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.