Erdal Emel

A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems

Vehicle routing problems have been extensively analyzed within the last few decades, since they play a central role in optimization of distribution networks. This paper presents a new Hybrid Electromagnetism-like Algorithm for solving capacitated vehicle routing problems. Electromagnetism-like Algorithm is a population-based algorithm based on attraction-repulsion mechanisms between charged particles. A modified objective function value calculation approach, utilizing the Random-Key Procedure makes it possible for Electromagnetism-like Algorithm to solve known combinatorial optimization problems like capacitated vehicle routing problems. Here, the solutions obtained such are improved by a relatively new local search method, Iterated Swap Procedure, and tested on several benchmarking problems. The computational results show that the proposed Hybrid Electromagnetism-like Algorithm gives promising results within acceptable computational times when compared to other novel meta-heuristics.

An adaptive artificial bee colony algorithm for global optimization

Artificial bee colony algorithm (ABC) is a recently introduced swarm based meta-heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to determine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a memory length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants.

Simulation based decision-making for hospital pharmacy management

Managing healthcare delivery systems plays an important role for healthcare providers in order to have high quality service performances. Inpatient pharmacy delivery systems are one of those that have a key role in hospital¿s service quality. Simulation is the best tool to analyze the hospital pharmacy operations due to their inherent complexity. In this article, a simulation model is developed based on data collected from a hospital in Turkey to analyze its pharmacy delivery system. In comparison to the baseline system, two different scenarios with varying factors are investigated, seeking to minimize drug delivery time to patients. The results presented here indicate the possibility for improved system performance.

A modified artificial bee colony algorithm for p-center problems.

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient.

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.

An enhanced artificial bee colony algorithm with solution acceptance rule and probabilistic multisearch

The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

A Clinical Decision Support System for Femoral Peripheral Arterial Disease Treatment

One of the major challenges of providing reliable healthcare services is to diagnose and treat diseases in an accurate and timely manner. Recently, many researchers have successfully used artificial neural networks as a diagnostic assessment tool. In this study, the validation of such an assessment tool has been developed for treatment of the femoral peripheral arterial disease using a radial basis function neural network (RBFNN). A data set for training the RBFNN has been prepared by analyzing records of patients who had been treated by the thoracic and cardiovascular surgery clinic of a university hospital. The data set includes 186 patient records having 16 characteristic features associated with a binary treatment decision, namely, being a medical or a surgical one. K-means clustering algorithm has been used to determine the parameters of radial basis functions and the number of hidden nodes of the RBFNN is determined experimentally. For performance evaluation, the proposed RBFNN was compared to three different multilayer perceptron models having Pareto optimal hidden layer combinations using various performance indicators. Results of comparison indicate that the RBFNN can be used as an effective assessment tool for femoral peripheral arterial disease treatment.

Efficient constraint handling in electromagnetism-like algorithm for traveling salesman problem with time windows.

The traveling salesman problem with time windows (TSPTW) is a variant of the traveling salesman problem in which each customer should be visited within a given time window. In this paper, we propose an electromagnetism-like algorithm (EMA) that uses a new constraint handling technique to minimize the travel cost in TSPTW problems. The EMA utilizes the attraction-repulsion mechanism between charged particles in a multidimensional space for global optimization. This paper investigates the problem-specific constraint handling capability of the EMA framework using a new variable bounding strategy, in which real-coded particles boundary constraints associated with the corresponding time windows of customers, is introduced and combined with the penalty approach to eliminate infeasibilities regarding time window violations. The performance of the proposed algorithm and the effectiveness of the constraint handling technique have been studied extensively, comparing it to that of state-of-the-art metaheuristics using several sets of benchmark problems reported in the literature. The results of the numerical experiments show that the EMA generates feasible and near-optimal results within shorter computational times compared to the test algorithms.

A Fiber-Optic Bending Sensor for the Vibration Monitoring of CNC Face-Milling Machine

Vibration measurement of CNC milling is one of the used techniques for prediction of tool wear. Monitoring of tool wear is very important since a worn tool will affect machine and workpiece either. We developed a fiber-optic sensor for spindle vibration of CNC face-milling machine. The sensor is based on monitoring loss of light from the fiber core. For this sensor a laser light transmitter circuit, a sense plane construction, and a light receiver circuit are designed. Designed fiber-optic sensor is tested on Taksan TMC 650V face-milling machine. Obtained signals from this sensor is investigated in time domain and frequency domain and showed that it is valuable to tool ware monitoring.

An Expert System for Diagnosing Faults on CNC Machine Tool Breakdowns

Abstract In this study, an expert system (ES) has been developed for diagnosing faults causing breakdowns in computer numerical controlled (CNC) machine tools.The knowledge-base (KB) for ES has been established using structural, conceptual and heuristic knowledge. Generic design information for mechanical, electrical and hydraulic structural elements of CNC machine tools, together with expert maintenance personnels knowledge constitutes the knowledge-base. The expert system is implemented in Prolog with an interactive menu driven user interface.