Daiki Min

Scheduling elective surgery under uncertainty and downstream capacity constraints

The objective of this study is to generate an optimal surgery schedule of elective surgery patients with uncertain surgery operations, which includes uncertainty in surgery durations and the availability of downstream resources such as surgical intensive care unit (SICU) over multi-periods. The stochastic optimization is adapted and the sample average approximation (SAA) method is proposed for obtaining an optimal surgery schedule with respect to minimizing the total cost of patient costs and overtime costs. A computational experiment is presented to evaluate the performance of the proposed method.

An elective surgery scheduling problem considering patient priority

This paper addresses a scheduling problem where patients with different priorities are scheduled for elective surgery in a surgical facility, which has a limited capacity. When the capacity is available, patients with a higher priority are selected from the waiting list and put on the schedule. At the beginning of each period, a decision of the number of patients to be scheduled is made based on the trade-offs between the cost for overtime work and the cost for surgery postponement. A stochastic dynamic programming model is formulated to address this problem. A structural analysis of the proposed model is conducted to understand the properties of an optimal schedule policy. Based on the structural analysis, bounds on feasible actions are incorporated into a value iteration algorithm, and a brief computation experiment shows the improvement in computational efficiency. Numerical examples show that the consideration of patient priority results in significant differences in surgery schedules from the schedule that ignores the patient priority.

A simulation study of appointment scheduling in outpatient clinics: Open access and overbooking

Patient appointment scheduling (AS) in outpatient clinics is a widely studied subject and plays an important role in facilitating the efficient use of clinical resources and patients’ timely access to quality care. This paper considers two AS systems: open access (OA) and overbooking (OB). Clinics make strategic decisions on selecting an AS system and then make tactical decisions on the efficient or optimal use of the system based on the selection. This study proposes some guidelines for the strategic choice of an AS system. For this purpose, we conduct a discrete-event simulation to compare the two AS systems under various environments. We employ four performance measures for the comparison: overtime work, the proportion of unmet demand, in-clinic waiting times, and the use of appointment time slots. For the analysis, we devise an integrated measure representing a linear combination of the four measures. We divide the analysis into two phases. In the first phase, well-performed OA and OB policies are separately identified, and in the second phase, the two scheduling systems with the identified policies are compared. We find overbooking is more robust to various clinic environments and performs better than open access in general. Along with that result, we additionally suggest some rules for determining best open access and overbooking policies.

Fuzzy Logic-Based Approach to Detecting a Passive RFID Tag in an Outpatient Clinic

This study is motivated by the observations on the data collected by radio frequency identification (RFID) readers in a pilot study, which was used to investigate the feasibility of implementing an RFID-based monitoring system in an outpatient eye clinic. The raw RFID data collected from RFID readers contain noise and missing reads, which prevent us from determining the tag location. In this paper, fuzzy logic-based algorithms are proposed to interpret the raw RFID data to extract accurate information. The proposed algorithms determine the location of an RFID tag by evaluating its possibility of presence and absence. To evaluate the performance of the proposed algorithms, numerical experiments are conducted using the data observed in the outpatient eye clinic. Experiments results showed that the proposed algorithms outperform existing static smoothing method in terms of minimizing both false positives and false negatives. Furthermore, the proposed algorithms are applied to a set of simulated data to show the robustness of the proposed algorithms at various levels of RFID reader reliability.

MANAGING A PATIENT WAITING LIST WITH TIME-DEPENDENT PRIORITY AND ADVERSE EVENTS

This paper addresses the problem of managing a waiting list for elective surgery to decide the number of patients selected from the waiting list and to schedule them in accordance with the operating room capacity in the next period. The waiting list prioritizes patients not only by their initial urgency level but also by their waiting time. Selecting elective surgery patients requires a balance between the waiting time for urgent patients and that for less urgent patients. The problem is formulated as an infinite horizon Markov Decision Process. Further, the study proposes a scheduling procedure based on structural properties of an optimal policy by taking a sampling-based finite horizon approximation approach. Finally, we examine the performance of the policy under various conditions.

Carbon Reduction Investments under Direct Shipment Strategy

Recently much research efforts have focused on how to manage carbon emissions in logistics operations. This paper formulates a model to determine an optimal shipment size with aims to minimize the total cost consisting not only of inventory and transportation costs but also cost for carbon emissions. Unlike the literature assuming carbon emission factors as a given condition, we consider the emission factors as decision variables. It is allowed to make an investment in improving carbon emission factors. The optimal investment decision is shown to be of a threshold type with respect to unit investment costs. Moreover, the findings in this work provide insights on the various elements of the investment decision and their impacts.

Supply Chain Coordination Under the Cap-and-trade Emissions Regulation

This paper considers a supply chain consisting of a manufacturer under the cap-and-trade emissions regulation and a permit supplier. We study joint production quantity and investment in reducing permit production cost decisions for centralized and decentralized supply chains. We formulate two supply chain contracts with aims to coordinate the decentralized supply chain; wholesale price contract and cost-sharing contract. Under the cost-sharing contract, the manufacturer shares a part of the investment in reducing permit production cost and then is allowed to purchase emission permit at a lower price. We analytically find that the proposed cost-sharing contract with reasonable parameters can coordinate the supply chain whereas the wholesale price contract is not desirable to achieve the system-wide profit. Numerical example is followed to support the analysis.

The Analysis of Carbon Emission Costs under Milk Run Logistics Strategy

This paper develops an analytic model for minimizing the cost o f distributing items by truck from one supplier to many customers under Milk run logistics strategy. The model der ives formulas for not only inventory and transportation costs but also costs associated with carbon emission trading sc heme. In addition, monetary investment for reducing carbon emissions is considered. We analyze how to determine opt imal shipment size and carbon emission reduction investment. The purpose of this work is to evaluate the effects of carbon emission trading scheme on the Milk run logistics strategy in terms of how much to reduce carbon emissi ons and/or inventory and transportation costs. We analytically show that it is possible to reduce carbon emission s while reducing inventory and transportation costs by introducing cap-and-trade carbon emission trading scheme under certain conditions.

Staffing a Service System with Appointment-Based Customer Arrivals

Appointment systems are widely used to facilitate customers’ access to services in many industries such as healthcare. A number of studies have taken a queueing approach to analyse service systems and facilitate managerial decisions on staffing requirements by assuming independent and stationary customer arrivals. This paper is motivated by the observation that the queueing-based method shows relatively poor performance when customers arrive according to their appointment times. Because customer arrivals are dependent on their appointment times, this study, unlike queueing-based methods, conducts a detailed analysis of appointment-based customer arrivals instead of making steady-state assumptions. We develop a new model that captures the characteristics of appointment-based customer arrivals and computes the probability of transient system states. Through the use of this model, which relaxes stationary and independent assumptions, we propose a heuristic algorithm that determines staffing requirements with aims to minimizing staff-hours while satisfying a target service level. The simulation results show that the proposed method outperforms the queueing-based method.

Heuristic procedures for a stochastic batch service problem

This paper considers a multi-class batch service problem that involves a class-dependent waiting cost and a service cost in determining customer batch sizes. Unlike a fixed service cost used widely in standard models, the service cost considered in this work is incurred only if the total service time is over the capacity. We formulate this problem as an infinite horizon Markov decision process, and exploit its structural properties to establish theoretical results, including bounds on the optimal action space. We use the results to improve the value iteration procedure. Furthermore, we design heuristic algorithms for large problems. The numerical experiments demonstrate that the class-dependent waiting cost has a considerable influence on the optimal customer batch size. Finally, we evaluate the efficiency of the proposed value iteration procedure and the quality of the heuristic solutions.