Irem Ozkarahan

An integrated multicriteria decision-making methodology for outsourcing management

In this study, an outsourcer evaluation and management system is developed for a textile company by use of fuzzy goal programming (FGP). At first phase of the methodology, evaluation criteria for the outsourcers and the objectives of the company are determined. The existing outsourcers of the company are evaluated by PROMETHEE. At the second phase, the FGP model developed selects the most appropriate outsourcers suitable to be strategic partners with the company and simultaneously allocates the quantities to be ordered to them. In the end, the results achieved are compared with the current situation of the company. The results indicated that scientific methodology for outsourcer management system is very beneficial for the company. Scope and purpose The purchasing activities of a company constitute a very important part in the overall operation of the company. The quality and the delivery capabilities of any manufacturing firm depend heavily on the performance of its suppliers. When companies outsource a significant part of their business and become more dependent on outsourcers, the quality and delivery performance of the company depends totally on its outsourcers. The consequences of poor decision making become more severe. It is therefore too important for an outsourcing type manufacturer to evaluate, manage and select their suppliers. This research proposes an outsourcer evaluation and management system on scientific basis. The procedure developed is made up of two phases. The first one is the evaluation of outsourcers in terms of the decision makers objectives. This phase is followed by FGP modeling phase which provides the decision maker the list of suitable outsourcers.

A fuzzy multi-objective covering-based vehicle location model for emergency services

Abstract Timeliness is one of the most important objectives that reflect the quality of emergency services such as ambulance and firefighting systems. To provide timeliness, system administrators may increase the number of service vehicles available. Unfortunately, increasing the number of vehicles is generally impossible due to capital constraints. In such a case, the efficient deployment of emergency service vehicles becomes a crucial issue. In this paper, a multi-objective covering-based emergency vehicle location model is proposed. The objectives considered in the model are maximization of the population covered by one vehicle, maximization of the population with backup coverage and increasing the service level by minimizing the total travel distance from locations at a distance bigger than a prespecified distance standard for all zones. Model applications with different solution approaches such as lexicographic linear programming and fuzzy goal programming (FGP) are provided through numerical illustrations to demonstrate the applicability of the model. Numerical results indicate that the model generates satisfactory solutions at an acceptable achievement level of desired goals. Scope and purpose This paper considers the emergency service vehicles location problem. A multi-objective maximal covering location model is proposed in this paper. The model addresses the issue of determining the best base locations for a limited number of vehicles so that the service level objectives are optimized. Three of the important surrogates that reflect the quality of emergency service systems are considered as objectives in the model: maximization of the population covered by one vehicle, maximization of the population with backup coverage and minimization of the total travel distance from locations at a distance bigger than a prespecified distance standard for all zones. The proposed model allows the incorporation of decision makers imprecise aspiration levels for the goals by means of FGP approach. Thus, the solution efficiency inherent in the FGP approaches is also included into the model. To demonstrate the applicability of the model, numerical examples are provided using different solution approaches.

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 models ability to satisfy abstract goals.

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 todays 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 problems. 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.

Goal Programming Model Subsystem of a Flexible Nurse Scheduling Support System

Abstract A model for nurse scheduling via the goal programming versions of the traditional “set covering” model is discussed in detail. The demand profile and nurses preferences are fed into an expert like capability designed to formulate a goal programming representation of the problem. Solutions to the goal programmed model are made available for various goal priority options. Each solution can be disaggregated into specific assignments for specific units and nurses.

Allocation of surgical procedures to operating rooms

Reduction of health care costs is of paramount importance in our time. This paper is a part of the research which proposes an expert hospital decision support system for resource scheduling. The proposed system combines mathematical programming, knowledge base, and database technologies, and what is more, its friendly interface is suitable for any novice user. Operating rooms in hospitals represent big investments and must be utilized efficiently. In this paper, first a mathematical model similar to job shop scheduling models is developed. The model loads surgical cases to operating rooms by maximizing room utilization and minimizing overtime in a multiple operating room setting. Then a prototype expert system which replaces the expertise of the operations research analyst for the model, drives the modelbase, database, and manages the user dialog is developed. Finally, an overview of the sequencing procedures for operations within an operating room is also presented.

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.This paper presents a goal programming model that implicitly represents scheduling flexibility and also incorporates information about the preferred working patterns of employees. After solving the proposed model, a work schedule will be generated for each employee without requiring a further step for the assignment of shifts, break times, and work days to employees. The model is capable of handling multiple scheduling objectives, and it can produce optimal solutions in very short computing times.

A scheduling model for hospital residents

When medical students finish their school they must go through a horrendous apprenticeship known as hospital residency to be able to practice medicine. During residency, they work at least 16-hr a day, 5-days a week, with 2 or 3 nights on-call. These can turn into 36-hr shifts. This means that many patients are being treated by exhausted novices, who are therefore much more likely to make mistakes. It was one such mistake, leading to the death of a New York woman, which led to serious attempts to reforming working hours of residents. In this paper, we developed a decision model which attempts to schedule residents based on the requirements of the residency program as well as the desires of residents as to days-off, weekends, on-call nights, etc.

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 PROMETHEE 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.

Lagrangian-Based Solution Approaches for a Resource-Constrained Parallel Machine Scheduling Problem with Machine Eligibility Restrictions

This study is motivated by a real world scheduling problem in an injection molding department of an electrical appliance company. In this paper, a resource-constrained parallel machine scheduling problem with machine eligibility restrictions is investigated. For the problem, an integer linear program is developed with the objective of minimizing total flow time. Based on this model, a Lagrangian-based solution approach with a subgradient optimization procedure has been proposed. Additionally, a problem-specific heuristic algorithm is developed to obtain near-optimal solutions. Through randomly generated instances of the problem, it is demonstrated that the proposed algorithms generate not only very tight lower bounds but also efficient results with a small optimality gap.