Adil Baykasoğlu

Prediction of compressive and tensile strength of limestone via genetic programming

Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.

A simulated annealing algorithm for dynamic layout problem

Abstract Increased level of volatility in todays manufacturing world demanded new approaches for modelling and solving many of its well-known problems like the facility layout problem. Over a decade ago Rosenblatt published a key paper on modelling and solving dynamic version of the facility layout problems. Since then, various other researchers proposed new and improved models and algorithms to solve the problem. Balakrishnan and Cheng have recently published a comprehensive review of the literature about this subject. The problem was defined as a complex combinatorial optimisation problem. The efficiency of SA in solving combinatorial optimisation problems is very well known. However, it has recently not been applied to DLP based on the review of the available literature. In this research paper a SA-based procedure for DLP is developed and results for test problems are reported. Scope and purpose One of the characteristic of todays manufacturing environments is volatility. Under a volatile environment (or dynamic manufacturing environment) demand is not stable. To operate efficiently under such environments facilities must be adaptive to changing demand conditions. This requires solution of the dynamic layout problem (DLP). DLP is a complex combinatorial optimisation problem for which optimal solutions can be found for small size problems. This research paper makes use of a SA algorithm to solve the DLP. Simulated annealing (SA) is a well-established stochastic neighbourhood search technique. It has a potential to solve complex combinatorial optimisation problems. The paper presents in detail how to apply SA to solve DLP and an extensive computational study. The computational study shows that SA is quite effective in solving dynamic layout problems.

Bees algorithm for generalized assignment problem

Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.

PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH

With their high potential, high motivation, great problem-solving ability and flexibility, project teams are important work structures for the business life. The success of these teams is highly dependent upon the people involved in the project team. This makes the project team selection an important factor for project success. The project team selection can be defined as selecting the right team members, which will together perform a particular project/task within a given deadline. In this article, an analytical model for the project team selection problem is proposed by considering several human and nonhuman factors. Because of the imprecise nature of the problem, fuzzy concepts like triangular fuzzy numbers and linguistic variables are used. The proposed model is a fuzzy multiple objective optimization model with fuzzy objectives and crisp constraints. The skill suitability of each team candidate is reflected to the model by suitability values. These values are obtained by using the fuzzy ratings method. The suitability values of the candidates and the size of the each project team are modeled as fuzzy objectives. The proposed algorithm takes into account the time and the budget limitations of each project and interpersonal relations between the team candidates. These issues are modeled as hard-crisp constraints. The proposed model uses fuzzy objectives and crisp constraints to select the most suitable team members to form the best possible team for a given project. A simulated annealing algorithm is developed to solve the proposed fuzzy optimization model. Software based on C + + computer programming language is also developed to experiment on the proposed model in forming project teams.

A TABOO SEARCH BASED APPROACH TO FIND THE PARETO OPTIMAL SET IN MULTIPLE OBJECTIVE OPTIMIZATION

Abstract Taboo search is a heuristic optimization technique which works with a neighbourhood of solutions to optimize a given objective function. It is generally applied to single objective optimization problems. Taboo search has the potential for solving multiple objective optimization (MOO) problems, because it works with more than one solution at a time, and this gives it the opportunity to evaluate multiple objective functions simultaneously. In this paper, a taboo search based algorithm is developed to find Pareto optimal solutions in multiple objective optimization problems. The developed algorithm has been tested with a number of problems and compared with other techniques. Results obtained from this work have proved that a taboo search based algorithm can find Pareto optimal solutions in MOO effectively.

Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks

This paper presents a new hybrid algorithm, which executes ant colony optimization in combination with genetic algorithm (ACO-GA), for type I mixed-model assembly line balancing problem (MMALBP-I) with some particular features of real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. The proposed ACO-GA algorithm aims at enhancing the performance of ant colony optimization by incorporating genetic algorithm as a local search strategy for MMALBP-I with setups. In the proposed hybrid algorithm ACO is conducted to provide diversification, while GA is conducted to provide intensification. The proposed algorithm is tested on 20 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with pure ACO pure GA and hGA in terms of solution quality and computational times. Computational results indicate that the proposed ACO-GA algorithm has superior performance.

A survey on the methods and tools of concurrent new product development and agile manufacturing

Companies in either manufacturing or servicing have to be restructured or re-organized in order to overcome with challenges of the 21st century in which customers are not only satisfied but also delighted. In this competitive environment, organizations should use a flexible, adaptive and responsive paradigm that can be entitled by a unique term: agile manufacturing (AM). An AM system is able to develop a variety of product at low cost and in a short time period. For this, it has some of useful enabling technologies and physical tools. Among these, concurrent engineering (CE) is a systematic approach to the integrated, concurrent design of product and their related processes, including manufacture and support. It is then a useful and beneficial approach to reduce the development time and manufacturing cost, while simultaneously improving the quality of a product in order to better respond to the customer expectations. The aim of this study is to underline the synergistic impact of new product development (NPD) and CE, (which can be called CNPD), and to survey their methods and tools in association with the AM.

Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems

Teaching-learning based optimization (TLBO) algorithm has been recently proposed in the literature as a novel population oriented meta-heuristic algorithm. It has been tested on some unconstrained and constrained non-linear programming problems, including some design optimization problems with considerable success. The main purpose of this paper is to analyze the performance of TLBO algorithm on combinatorial optimization problems first time in the literature. We also provided a detailed literature review about TLBOs applications. The performance of the TLBO algorithm is tested on some combinatorial optimization problems, namely flow shop (FSSP) and job shop scheduling problems (JSSP). It is a well-known fact that scheduling problems are amongst the most complicated combinatorial optimization problems. Therefore, performance of TLBO algorithm on these problems can give an idea about its possible performance for solving other combinatorial optimization problems. We also provided a comprehensive comparative study along with statistical analyses in order to present effectiveness of TLBO algorithm on solving scheduling problems. Experimental results show that the TLBO algorithm has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems.

Training neural networks with harmony search algorithms for classification problems

Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy.

An improved firefly algorithm for solving dynamic multidimensional knapsack problems

There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.