Local search for the surgery admission planning problem

We present a model for the surgery admission planning problem, and a meta-heuristic algorithm for solving it. The problem involves assigning operating rooms and dates to a set of elective surgeries, as well as scheduling the surgeries of each day and room. Simultaneously, a schedule is created for each surgeon to avoid double bookings. The presented algorithm uses simple Relocate and Two-Exchange neighbourhoods, governed by an iterated local search framework. The problem's search space associated with these move operators is analysed for three typical fitness surfaces, representing different compromises between patient waiting time, surgeon overtime, and waiting time for children in the morning on the day of surgery. The analysis shows that for the same problem instances, the different objectives give fitness surfaces with quite different characteristics. We present computational results for a set of benchmarks that are based on the admission planning problem in a chosen Norwegian hospital

Allocation of surgeries to operating rooms by goal programing

High usage rate in a surgical suite is extremely important in meeting the increasing demand for health care services and reducing costs to improve quality of care. In this paper a goal programming model which can produce schedules that best serve the needs of the hospital, i.e., by minimizing idle time and overtime, and increasing satisfaction of surgeons, patients, and staff, is described. The approach involves sorting the requests for a particular day on the basis of block restrictions, room utilization, surgeon preferences and intensive care capabilities. The model is tested using the data obtained during field studies at Dokuz Eylul University Hospital. The model is also tested for alternative achievement functions to examine the model's ability to satisfy abstract goals

A multicriteria sorting procedure for financial classification problems: The case of business failure risk assessment

This paper presents a new multicriteria sorting procedure in financial classification problems, based on the methodological framework of PRO-METHEE method. The proposed procedure, called as PROMSORT, is applied to the business failure risk problem and compared to PROMETHEE TRI and ELECTRE TRI. The proposed methodology also identifies the differences in performances across risk groups, and assists in monitoring the firms' financial performances. The results showed that the proposed procedure can be considered as an effective alternative to existing methods in financial classification problems

Strategic tactical and operational production-distribution models: a review

The concept of supply chain management is gaining so much importance that the firms can compete in today's global economy. This paper provides a detailed literature survey of previous research on supply chain management literature at strategic, tactical, operational levels and reverse logistics, but we limited our research only to the models developed for production and distribution problem. We scrutinise the previous reviews in order to distinguish our research from the others. In the light of these previous reviews, we have developed our classification scheme. The models reviewed in this research have been classified in terms of the solution methodology used. These are: optimisation-based models, metaheuristic-based models, Information Technology (IT)-driven models and hybrid models. The objective is to develop a framework for the existing literature to reveal major trends in the literature and to explore research opportunities in this area

Implicit optimal tour scheduling with flexible break assignments

In service organizations, customer demand varies over the course of an operating day and across the days of an operating week. These organizations must assign employees to weekly tour schedules in an attempt to satisfy the fluctuating customer demand. The tour-scheduling problem has been traditionally formulated by the set-covering approach. However, this approach requires a separate integer variable for each variation in the tour schedule due to different work days and different shift and break start times on these days, and this explicit representation results in an enormously large number of decision variables. In this paper, an implicit integer-programming model is presented, which combines daily shift assignments and alternate day-on patterns and also schedules break periods for shifts within the specified break windows. The implicit form of the model requires very few integer variables compared to the set-covering approach. (C) 2002 Elsevier Science Ltd. All rights reserved

An implicit goal programming model for the tour scheduling problem considering the employee work preferences

Many organizations face employee scheduling problems under conditions of variable demand for service over the course of an operating day and across a planning horizon. These organizations are concerned with the tour scheduling problem that involves assigning shifts and break times to the work days of employees and allocating days off to individual work schedules. Nowadays, organizations try to adopt various scheduling flexibility alternatives to meet the fluctuating service demand. On the other hand, they have also realized that providing employee productivity and satisfaction is as much important as meeting the service demand. Up to date, tour scheduling solution approaches have neglected considering employee preferences and tried to develop work schedules for employees in a subsequent step

Comparison of different variable and value order strategies for the optimum solution of a single machine scheduling problem with sequence-dependent setups

The job sequencing problem for a single machine with sequence-dependent setups is solved using the constraint programming (CP) and mixed-integer programming (MIP) approaches. For the CP search, ten different variable and value ordering heuristics are tested using both the CP model and/or the combined model of the MIP and CP formulations. Some of these heuristics exploit problem specific data like setup cost and due date. Others rely on hybrid strategies that use the linear programming (LP) solver within the CP search or direct the search using the initial feasible solution obtained from the MIP model. A comparative analysis of the search heuristics and the CP and MIP solvers has been given with respect to the solution times. The research results indicate that the CP solver finds the optimum solution in a very short time compared to the MIP solver as the number of jobs to be scheduled increases