Serap Ulusam Seçkiner

A simulated annealing approach to the solution of job rotation scheduling problems

This paper presents a new solution to the job rotation scheduling problem where the objective is to minimize the workload for each worker. Our motivation for this study comes from dangerous jobs that have some exposures. Job rotation is one method that is sometimes used to reduce exposure to strenuous jobs. Especially, this method can be applied to the service organizations that customer demand varies over the course of an operating day and across the days of an operating week. However, developing effective rotation schedules can be complex in even moderate sized service systems. Integer programming and a simulated annealing algorithm were used to construct the schedules. The efficiency of simulated annealing in solving combinatorial optimization problems is very well known. However, it has recently not been applied to job rotation scheduling problem based on the review of the available literature. In this research paper, the developed models are presented and results for test problems are reported.

Ant colony optimization for the job rotation scheduling problem

This paper seeks to explore the effectiveness of ant colony optimization for solving the job rotation scheduling problem. The problem of scheduling in job rotation is considered with the objective of minimizing the workload of jobs. The performances of the proposed two ant-colony algorithms are evaluated based on integer programming and simulated annealing solutions. Results show that the ant-colony algorithm implementation is able to compete effectively with the best known solution to the problem.

Gear Fault Location Detection for Split Torque Gearbox Using AE Sensors

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven helicopter object exchange models (OEMs) to develop products using STG. However, the unique structure of the STG creates a problem on how to locate the gear faults in an STG. As of today, only limited research on STG fault detection using vibration and acoustic emission (AE) sensors has been conducted. In this paper, an effective gear fault location detection methodology using AE sensors for STG is presented. The methodology uses wavelet transform to process AE sensor signals at different locations to determine the arrival time of the AE bursts. By analyzing the arrival time of the AE bursts, the gear fault location can be determined. The parameters of the wavelets are optimized by using an ant colony optimization algorithm. Real seeded gear fault experimental tests on a notional STG are conducted. AE signals at different locations of the gearbox with both healthy and damaged output driving gears are collected simultaneously to determine the location of the damaged gear. Experimental results have shown the effectiveness of the presented methodology.

An integer programming model for hierarchical workforce scheduling problem

In this paper, an integer programming model for the hierarchical workforce problem under the compressed workweeks is developed. The model is based on the integer programming formulation developed by Billionnet [A. Billionnet, Integer programming to schedule a hierarchical workforce with variable demands, European Journal of Operational Research 114 (1999) 105–114] for the hierarchical workforce problem. In our model, workers can be assigned to alternative shifts in a day during the course of a week, whereas all workers are assigned to one shift type in Billionnet’s model. The main idea of this paper is to use compressed workweeks in order to save worker costs. This case is also suitable for the practice. The proposed model is illustrated on the Billionnet’s example problem and the obtained results are compared with the Billionnet’s model results. � 2006 Elsevier B.V. All rights reserved.

Ant colony optimization for continuous functions by using novel pheromone updating

This paper presents an ant colony optimization (ACO) algorithm for continuous functions based on novel pheromone updating. At the end of the each iteration in the proposed algorithm, pheromone is updated according to percentiles which determine the number of ants to track the best candidate solution. It is performed by means of solution archive and information provided by previous solutions. Performance of the proposed algorithm is tested on ten benchmark problems found in the literature and compared with performances of previous methods. The results show that ACO which is based on novel pheromone updating scheme (ACO-NPU) handles different types of continuous functions very well and can be a robust alternative approach to other stochastic search algorithms.

An exploration of the literature on the use of 'swarm intelligence-based techniques' for public service problems

The importance of studying public service systems and finding robust solutions to the problems encountered in public service management has increased considerably over the past decade. One of the main objectives is to find acceptable solutions to Public Service Problems (PSPs) within an affordable period of time. However, many PSPs remain difficult to solve within a reasonable time due to their complexity and dynamic nature. This requires solving PSPs with techniques which provide efficient algorithmic solutions. There has been increasing attention in the literature to solving PSPs through the use of Swarm Intelligence-Based Techniques (SIBTs) like ant colony optimisation, particle swarm optimisation, Bee(s) Algorithm (BA), etc. This paper presents a review of Swarm Intelligence (SI) applications in public services (including PSPs in specific application areas), as well as the models and SI algorithms that have been reported in the literature. [Received 30 January 2008; Revised 4 December 2008; Revised 17 March 2009; Accepted 23 March 2009]

The Use of Artificial Neural Networks in Decision Support in Vesicoureteral Reflux Treatment

Aim: To develop a prediction model based on artificial neural networks (ANN) for the treatment selection in vesicoureteral reflux (VUR). Methods: A total of 96 children with VUR (145 ureteric units (UU)) were treated at our institution during 2004–2006. An ANN based on quick propagation architecture was created with the commercially available software package. The patients’ age and sex, the cause and grade of VUR, the affected ureter, the type of treatment (conservative, subureteric injection, or open surgery), existence of renal scar on DMSA, follow-up times and the number of injections were used as variables. These data were also transferred to a statistical software package and regression analysis was done. Results: In all, 105 UU showed no reflux, 5 UU showed improvements in reflux grade (considered only in the conservative management group), and the remaining 35 UU showed persistence. In the training group (n = 99), ANN showed 98.5% sensitivity, 92.5% specificity, 97% positive predictive value, and 96% negative predictive value in predicting treatment outcome. Conclusions: We have demonstrated that ANN can accurately predict the resolution of VUR, and thus could be useful in daily clinical practice. This approach would allow urologists to aid in the decision-making process of VUR treatment.

Appropriate Use of Artificial Neural Networks

It is obvious that the validation and test set have been used as mentioned in the methods section of our article [3] . Surely, similar performances in validation and test groups have also been obtained. The performance of our model highly depends on a well-trained set which provides us with a reliable decision. The input/output training data are fundamental in neural network technology, because they convey the necessary information to ‘discover’ the optimal operating point [4] . Under these circumstances, the proposed model can handle the decision for VUR treatment, and it can also be a robust alternative approach to other statistical data analysis techniques. Dear Sir, We read the comments of Guner regarding our article [1] . Actually, the goal of our study was to show the usability of artificial neural networks in daily clinical practice of vesicoureteral reflux (VUR) treatment. An obvious advantage of a data-driven approach like artificial neural networks is that controllable variables (e.g., type of treatment, follow-up times, the number of injections) and non-controllable variables (age, sex, existence of renal scar on DMSA, the cause and grade of VUR) can be easily and timely updated with new patient data [2] . Thus, more accurate control of the decision support in VUR treatment is guaranteed. Received: May 10, 2010 Accepted: May 23, 2010 Published online: July 15, 2010

A NEURAL NETWORK-BASED SYSTEM FOR PREDICTION OF COMPUTER USER COMFORT

This article proposes a neural network-based system for prediction of computer user comfort with respect to the existing settings of the workstation. In this context, anthropometric measures and the existing measures of a computer workstation were related to back-support comfort, distance comfort, keyboard comfort, monitor comfort, and seat comfort using two distinct modeling approaches—multiple linear regression and artificial neural network. The purpose of this article was to compare and contrast the resulting models. The data from 144 computer workstations were used and a total of 12 different data types such as shoulder to elbow, eye to buttock, pan height, monitor height, or distance from the chair were recorded. While multiple linear regression could not be used to adequately predict the computer workstation comfort, the neural network was deemed superior. This approach allows ergonomists to aid in the decision-making process of computer environment design and the prediction of the health risk in an occupational environment.

A Novel Approach for the Nurse Scheduling Problem

This study considers nurse scheduling problem in seven-days-three-week operations under an arrangement called 4-day workweek. A computer-assisted scheduling program has been proposed to schedule nurses with multiple shifts. The program has been developed in Delphi 7.0 and includes a scheduling module which schedules nurses for three weeks on the basis of the weekday and weekend shift nursing requirements. The program is significantly beneficial for scheduling of large numbers of nurses and providing optimal three weekly schedules in a reasonable time and provides a faster response, a reduced cost as compared to human experts and a number of practical features.