Chance Constrained Mixed Integer Program: Bilinear and Linear Formulations, and Benders Decomposition

In this paper, we study chance constrained mixed integer program with consideration of recourse decisions and their incurred cost, developed on a finite discrete scenario set. Through studying a non-traditional bilinear mixed integer formulation, we derive its linear counterparts and show that they could be stronger than existing linear formulations. We also develop a variant of Jensen's inequality that extends the one for stochastic program. To solve this challenging problem, we present a variant of Benders decomposition method in bilinear form, which actually provides an easy-to-use algorithm framework for further improvements, along with a few enhancement strategies based on structural properties or Jensen's inequality. Computational study shows that the presented Benders decomposition method, jointly with appropriate enhancement techniques, outperforms a commercial solver by an order of magnitude on solving chance constrained program or detecting its infeasibility

IMPROVING HEALTHCARE DELIVERY: LIVER HEALTH UPDATING AND SURGICAL PATIENT ROUTING

Growing healthcare expenditures in the United States require improved healthcare delivery practices. Organ allocation has been one of the most controversial subjects in healthcare due to the scarcity of donated human organs and various ethical concerns. The design of efficient surgical suites management systems and rural healthcare delivery are long-standing efforts to improve the quality of care. In this dissertation, we consider practical models in both domains with the goal of improving the quality of their services. In the United States, the liver allocation system prioritizes among patients on the waiting list based on the patients' geographical locations and their medical urgency. The prioritization policy within a given geographic area is based on the most recently reported health status of the patients, although blood type compatibility and waiting time on the list are used to break ties. Accordingly, the system imposes a health-status updating scheme, which requires patients to update their health status within a timeframe that depends on their last reported health. However, the patients' ability to update their health status at any time point within this timeframe induces information asymmetry in the system. We study the problem of mitigating this information asymmetry in the liver allocation system. Specifically, we consider a joint patient and societal perspective to determine a set of Pareto-optimal updating schemes that minimize information asymmetry and data-processing burden. This approach combines three methodologies: multi-objective optimization, stochastic programming and Markov decision processes (MDPs). Using the structural properties of our proposed modeling approach, an efficient decomposition algorithm is presented to identify the exact efficient frontier of the Pareto-optimal updating schemes within any given degree of accuracy. Many medical centers offer transportation to eligible patients. However, patients' transportation considerations are often ignored in the scheduling of medical appointments. In this dissertation, we propose an integrated approach that simultaneously considers routing and scheduling decisions of a set of elective outpatient surgery requests in the available operating rooms (ORs) of a hospital. The objective is to minimize the total service cost that incorporates transportation and hospital waiting times for all patients. Focusing on the need of specialty or low-volume hospitals, we propose a computationally tractable model formulated as a set partitioning based problem. We present a branch-and-price algorithm to solve this problem, and discuss several algorithmic strategies to enhance the efficiency of the solution method. An extensive computational test using clinical data demonstrates the efficiency of our proposed solution method. This also shows the value of integrating routing and scheduling decisions, indicating that the healthcare providers can substantially improve the quality of their services under this unified framework

Models, algorithms and performance analysis for adaptive operating room scheduling

The complex optimisation problems arising in the scheduling of operating rooms have received considerable attention in recent scientific literature because of their impact on costs, revenues and patient health. For an important part, the complexity stems from the stochastic nature of the problem. In practice, this stochastic nature often leads to schedule adaptations on the day of schedule execution. While operating room performance is thus importantly affected by such adaptations, decision-making on adaptations is hardly addressed in scientific literature. Building on previous literature on adaptive scheduling, we develop adaptive operating room scheduling models and problems, and analyse the performance of corresponding adaptive scheduling policies. As previously proposed (fully) adaptive scheduling models and policies are infeasible in operating room scheduling practice, we extend adaptive scheduling theory by introducing the novel concept of committing. Moreover, the core of the proposed adaptive policies with committing is formed by a new, exact, pseudo-polynomial algorithm to solve a general class of stochastic knapsack problems. Using these theoretica

Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital

An approximate dynamic programming approach to the admission control of elective patients

In this paper, we propose an approximate dynamic programming (ADP) algorithm to solve a Markov decision process (MDP) formulation for the admission control of elective patients. To manage the elective patients from multiple specialties equitably and efficiently, we establish a waiting list and assign each patient a time-dependent dynamic priority score. Then, taking the random arrivals of patients into account, sequential decisions are made on a weekly basis. At the end of each week, we select the patients to be treated in the following week from the waiting list. By minimizing the cost function of the MDP over an infinite horizon, we seek to achieve the best trade-off between the patients' waiting times and the over-utilization of surgical resources. Considering the curses of dimensionality resulting from the large scale of realistically sized problems, we first analyze the structural properties of the MDP and propose an algorithm that facilitates the search for best actions. We then develop a novel reinforcement-learning-based ADP algorithm as the solution technique. Experimental results reveal that the proposed algorithms consume much less computation time in comparison with that required by conventional dynamic programming methods. Additionally, the algorithms are shown to be capable of computing high-quality near-optimal policies for realistically sized problems

Methods and Formal Models for Healthcare Systems Management

A healthcare system is an organization of people, institutions, and resources that deliver healthcare services to meet the health needs of target populations. The size of the systems, the huge number of agents involved and their different expectations make the management of healthcare systems a tough task which could be alleviated through the use of technology. In this thesis, new methods and formal models for healthcare system management are presented. Particularly, the thesis is divided in two main parts: the first one has to do with the modeling and analysis in hospitals by the use of clinical pathways while the second one deals with the planning and scheduling of patients in the operation rooms.Regarding the modeling and analysis of healthcare systems, depending on different visions and expectations, the system can be treated from different perspectives called facets. In chapter 2, the formal definition and characterization of two facets are given: (1) facet of resource management and (2) handshake between clinical pathways facet. They are obtained by applying to Stochastic Well-formed Nets (colored Petri Nets) modeling the healthcare system a set of relaxations, abstraction and modifications. In the first facet the subclass of S4PR is obtained which is a characteristic model of the resource allocation systems while in the second facet Deterministically Synchronized Sequential Process (DSSP) are considered. Both nets (S4PR and DSSP) are formal subclasses of Petri Nets where net level techniques can be applied.In chapters 3 and 4, we will focus on the liveness of the DSSP systems resulting from the facet of communication between clinical pathways. These kinds of nets are composed by agents (modeling clinical pathways) cooperating in a distributed way by the asynchronous messaging passing through the buffers (modeling the communication channels). In particular two approaches have been proposed.The idea behind the first approach is to advance the buffer consumption to the first conflict transition in the agents. Considering healthcare systems modeled by a DSSP, this means that before a patient starts a clinical pathway, all required information must be available. Unfortunately, this pre-assignment method only works in some particular DSSP structures which are characterized. A more general approach (than buffer pre-assignment) for liveness enforcing in non-live DSSP is given in Chapter. 4. The approach is formalized on two levels: execution and control. The execution level uses the original DSSP structure while for the control level we compute a new net system called the control PN. This net system is obtained from the original DSSP and has a predefined type of structure. The control PN will evolve synchronously with the non-live DSSP ensuring that the deadlock states will not be reached. The states (marking) of the control PN will enable or disable some transitions in the original DSSP, while some transitions in the control PN should fire synchronously with some transitions of the original DSSP.The second part of the thesis deals with surgery scheduling of patients in a hospital department. The Operating Rooms (ORs) are one of the most expensive material resources in hospitals, being the bottleneck of surgical services. Moreover, the aging population together with the improvement in surgical techniques are producing an increase in the demand for surgeries. So, the optimal use of the ORs time is crucial inhealthcare service management. We focus on the planning and scheduling of patients in Spanish hospital departments considering its organizational structure particularities as well as the concerns and specifications of their doctors.In chapter 5, the scheduling of elective patients under ORs block booking is considered. The first criterion is to optimize the use of the OR, the second criterion is to prevent that the total available time in a block will be exceeded and the third criterion is to respect the preference order of the patient in the waiting list. Three different mathematical programming models for the scheduling of elective patients are proposed. These are combinatorial problems with high computational complexity, so three different heuristic solution methods are proposed and compared. The results show that a Mixed Integer Linear Programming (MILP) problem solved by Receding Horizon Strategy (RHS)obtains better scheduling in lowest time.Doctors using the MILP problem must fix an appropriate occupation rate for optimizing the use of the ORs but without exceeding the available time. This has two main problems: i) inexperienced doctors could find difficult to fix an appropriate occupation rate, and ii) the uncertain in the surgery durations (large standard deviation) could results in scheduling with an over/under utilization. In order to overcome these problems, a New Mixed-Integer Quadratic Constrained Programming (N-MIQCP) model is proposed. Considering some probabilistic concepts, quadratic constraints are included in N-MIQCP model to prevent the scheduling of blocks with a high risk of exceeding the available time. Two heuristic methods for solving the N-MIQCP problem are proposed and compared with other chance-constrained approaches in bibliography. The results conclude that the best schedulings are achieved using our Specific Heuristic Algorithm (SHA) due to similar occupation rates than using other approaches are obtained but our SHA respects much more the order of the patients in the waiting list.In chapter 6, a three steps approach is proposed for the combined scheduling of elective and urgent patients. In the first step, the elective patients are scheduled for a target Elective Surgery Time (EST) in the ORs, trying to respect the order of the patients on the waiting list. In the second one, the urgent patients are scheduled in the remaining time ensuring that an urgent patient does not wait more than 48 hours. Finally, in the third step, the surgeries assigned to each OR (elective and urgent) are sequenced in such a way that the maximum time that an emergency patient should wait is minimized. Considering realistic data, different policies of time reserved in the ORs for elective and urgent patients are evaluated. The results show that all ORs must be used to perform elective and urgent surgeries instead of reserving some ORs exclusively for one type of patient.Finally, in chapter 7 a software solution for surgery service management is given. A Decision Support System for elective surgery scheduling and a software tool called CIPLAN are proposed. The DSS use as core the SHA for the scheduling of elective patients, but it has other features related to the management of a surgery department. A software tool called CIPLAN which is based on the DSS is explained. The software tool has a friendly interface which has been developed in collaboration with doctors in the “Lozano Blesa” Hospital in Zaragoza. A real case study comparing the scheduling using the manual method with the scheduling obtained by using CIPLAN is discussed. The results show that 128.000 euros per year could be saved using CIPLAN in the mentioned hospital. Moreover, the use of the tool allows doctors to reduce the time spent in scheduling to use it medical tasks.<br /

Optimization of Healthcare Delivery System under Uncertainty: Schedule Elective Surgery in an Ambulatory Surgical Center and Schedule Appointment in an Outpatient Clinic

This work investigates two types of scheduling problems in the healthcare industry. One is the elective surgery scheduling problem in an ambulatory center, and the other is the appointment scheduling problem in an outpatient clinic. The ambulatory surgical center is usually equipped with an intake area, several operating rooms (ORs), and a recovery area. The set of surgeries to be scheduled are known in advance. Besides the surgery itself, the sequence-dependent setup time and the surgery recovery are also considered when making the scheduling decision. The scheduling decisions depend on the availability of the ORs, surgeons, and the recovery beds. The objective is to minimize the total cost by making decision in three aspects, number of ORs to open, surgery assignment to ORs, and surgery sequence in each OR. The problem is solved in two steps. In the first step, we propose a constraint programming model and a mixed integer programming model to solve a deterministic version of the problem. In the second step, we consider the variability of the surgery and recovery durations when making scheduling decisions and build a two stage stochastic programming model and solve it by an L-shaped algorithm. The stochastic nature of the outpatient clinic appointment scheduling system, caused by demands, patient arrivals, and service duration, makes it difficult to develop an optimal schedule policy. Once an appointment request is received, decision makers determine whether to accept the appointment and put it into a slot or reject it. Patients may cancel their scheduled appointment or simply not show up. The no-show and cancellation probability of the patients are modeled as the functions of the indirect waiting time of the patients. The performance measure is to maximize the expected net rewards, i.e., the revenue of seeing patients minus the cost of patients\u27 indirect and direct waiting as well as the physician\u27s overtime. We build a Markov Decision Process model and proposed a backward induction algorithm to obtain the optimal policy. The optimal policy is tested on random instances and compared with other heuristic policies. The backward induction algorithm and the heuristic methods are programmed in Matlab