Browsing by Subject "Mixed-integer programming"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Analysis, design and implementation of models for housestaff scheduling at outpatient clinics and improving patient flow at a family health clinic(2015-05) Shu, Zhichao, Ph. D.; Bard, Jonathan F.; Morrice, Douglas J. (Douglas John), 1962-; Khajavirad, Aida; Dimitrov, Ned; Leykum, LuciClinical experiences during the three years of residencies occur in inpatient and outpatient settings on generalist and specialist clinical services. Housestaff rotate through different clinical experiences monthly, with their primary care clinic time overlaid longitudinally on these other clinical services. The primary goals of this research are to construct housestaff schedules and improve efficiencies for residency programs. In the first phase of the research, we developed two models for constructing monthly clinic schedules for housestaff training in Internal Medicine. In our first model, the objective is to both maximize clinic utilization and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program in which several of the hard constraints are temporarily allowed to be violated to avoid infeasibility. A three-phase methodology is then proposed to find solutions. The second model solves a similar problem with the objective of maximizing the number of interns and residents that are assigned clinic duty each month during their training in Internal Medicine. A complexity analysis is provided that demonstrates that the basic problem can be modeled as a pure network and the full problem can be modeled as a network with gains. In the second phase of the research, the goal was to redesign the monthly templates that comprise the annual block rotations to obtain better housestaff schedules. To implement this model, we investigate two different programs: Family Medicine and Internal Medicine. The problems were formulated as mixed-integer programs but proved too difficult to solve exactly. As an alternative, several heuristics were developed that yielded good feasible solutions. For the last part of the research, we focused on improving patient flow at a family health clinic. The objective was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals.Item Assembly and test operations with multipass requirement in semiconductor manufacturing(2014-05) Gao, Zhufeng; Bard, Jonathan, F.In semiconductor manufacturing, wafers are grouped into lots and sent to a separate facility for assembly and test (AT) before being shipped to the customer. Up to a dozen operations are required during AT. The facility in which these operations are performed is a reentrant flow shop consisting of several dozen to several hundred machines and up to a thousand specialized tools. Each lot follows a specific route through the facility, perhaps returning to the same machine multiple times. Each step in the route is referred to as a "pass." Lots in work in process (WIP) that have more than a single step remaining in their route are referred to as multi-pass lots. The multi-pass scheduling problem is to determine machine setups, lot assignments and lot sequences to achieve optimal output, as measured by four objectives related to key device shortages, throughput, machine utilization, and makespan, prioritized in this order. The two primary goals of this research are to develop a new formulation for the multipass problem and to design a variety of solution algorithms that can be used for both planning and real-time control. To begin, the basic AT model considering only single-pass scheduling and the previously developed greedy randomized adaptive search procedure (GRASP) along with its extensions are introduced. Then two alternative schemes are proposed to solve the multipass scheduling problem. In the final phase of this research, an efficient procedure is presented for prioritizing machine changeovers in an AT facility on a periodic basis that provides real-time support. In daily planning, target machine-tooling combinations are derived based on work in process, due dates, and backlogs. As machines finish their current lots, they need to be reconfigured to match their targets. The proposed algorithm is designed to run in real time.Item A mixed integer convex programming approach to constrained attitude guidance(2015-12) Eren, Utku; Açıkmeşe, Behçet; Akella, Maruthi R.This brief report introduces a new algorithm for attitude motion planning, Constrained Attitude Guidance (CAG) problem, in the presence of angular rate constraints and conic exclusion regions (pointing constraints). The CAG problem is solved by considering only the quaternion kinematics in the formulation and using constraints on quaternions and its time derivatives to indirectly apply bounds on the angular rates and accelerations. The CAG formulation makes use of Mixed Integer Convex Programming (MICP) in order to impose, approximately, the unity constraint on the quaternion magnitude, where the approximation accuracy can be set to a desired accuracy. The solution complexity of the MICP formulation increases exponentially with the number of binary variables that are used to impose the unit norm constraint on the quaternion. Since this number is independent of the number of exclusion pointing constraints, the solution approach has favorable complexity in terms of the number of pointing constraints. The report also provides a numerical example that incorporates both angular rate and pointing constraints.Item The therapist scheduling problem for patients with fixed appointment times(2011-12) Wang, Huan, master of science in engineering; Bard, Jonathan F.; Jarrah, Ahmad I.This report presents a series of models that can be used to find weekly schedules for therapists who provide ongoing treatment to patients scattered around a geographical region. In all cases, the patients’ appointment times and visit days are known prior to the beginning of the planning horizon. Variations in the model include single vs. multiple home bases, homogeneous vs. heterogeneous therapists, lunch break requirements, and a nonlinear cost structure for mileage reimbursement and overtime. The single home base and homogeneous therapist cases proved to be easy to solve and so were not investigated. This left two cases of interest: the first includes only lunch breaks while the second adds overtime and mileage reimbursement. In all, 40 randomly generated data sets were solved that consisted of either 15 or 20 therapists and between roughly 300 and 540 visits over five days. For each instance, we were able to obtain the minimum cost of providing home healthcare services for both models using CPLEX 12.2. The results showed that CPU time increases more rapidly than total cost as the total number of visits grows. In general, data sets with therapists who have different starting and ending locations are more difficult to solve than those whose therapists have the same home base.