Emel Kizilkaya Aydogan

Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment

In todays organizations, performance measurement comes more to the foreground with the advancement in the high technology. So as to manage this power, which is an important element of the organizations, it is needed to have a performance measurement system. Increased level of competition in the business environment and higher customer requirements forced industry to establish a new philosophy to measure its performance beyond the existing financial and non-financial based performance indicators. In this paper, a conceptual performance measurement framework that takes into account company-level factors is presented for a real world application problem. In order to use the conceptual framework for measuring performance, a methodology that takes into account both quantitative and qualitative factors and the interrelations between them should be utilized. For this reason, an integrated approach of analytic hierarchy process (AHP) improved by rough sets theory (Rough-AHP) and fuzzy TOPSIS method is proposed to obtain final ranking.

hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.

A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.

A Method Based on SMAA-Topsis for Stochastic Multi-Criteria Decision Making and a Real-World Application

Stochastic multi-criteria acceptability analysis (SMAA-2) and the technique for order preference by similarity to ideal solution (TOPSIS) are methods for evaluating alternatives with multiple criteria. SMAA is a method that is used for solving multi-criteria decision-making problems with uncertain, inaccurate information, and does not require preference information from the decision makers. The TOPSIS method is based on the principle of determining a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution. This paper proposes a new method, SMAA-TOPSIS, by combining the SMAA and TOPSIS methods. The SMAA-TOPSIS method was executed for two problems: drug benefit-risk analysis and machine gun selection. This paper found that TOPSIS could be used with uncertain and arbitrarily distributed values for weights and criteria measurements by using a combination of SMAA and TOPSIS. Also, we obtained clearer and consistent SMAA outputs.

Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.

A new intuitional algorithm for solving heterogeneous fixed fleet routing problems: Passenger pickup algorithm

Fixed-fleet heterogeneous vehicle routing is a type of vehicle routing problem that aims to provide service to a specific customer group with minimum cost, with a limited number of vehicles with different capacities. In this study, a new intuitional algorithm, which can divide the demands at the stops for fixed heterogeneous vehicle routing, is developed and tested on tests samples. The algorithm is compared to the BATA Algorithm available in the literature in relation to the number of vehicles, fixed cost, variable cost and total cost.

Balancing two-sided U-type assembly lines using modified particle swarm optimization algorithm

In this paper, a new two-sided U-type assembly line balancing (TUALB) procedure and a new algorithm based on the particle swarm optimization algorithm to solve the TUALB problem are proposed. The proposed approach minimizes the number of stations for a given cycle time as the primary objective and it minimizes the number of positions as a secondary objective. The proposed approach is illustrated with an example problem. In order to evaluate the efficiency of the proposed algorithm, the test problems available in the literature are used. The experimental results show that the proposed approach performs well.

Chemical agent detector placement methodology

Abstract Terrorism is a threat to global peace in our world today and in the future this terrorist threat is surely going to be important. Global terrorist groups have highly sophisticated weapon systems with them to terrorize the humanity. Asymmetric warfare which is accepted especially by rough states against free nations is also a threat to humanity. It includes terrorism, weapons of mass destruction, electronic and information war. In this concept, nuclear, biological and chemical (NBC) weapons are the most preferred ones in asymmetric warfare strategy because of their special characteristics. According to this strategy; cities, urban territories, critical military and civilian facilities are the main targets of terrorist attacks. In many developing countries, military zones which were located out of cities at first, happened to be surrounded by urban territories. This situation made the military zones more vulnerable to attacks by terrorists hiding amongst civilians. Another reason for military zones to be a possible target for attacks is the great terror on civilians which will be probably caused by such an attack. In this kind of sensational attacks, chemical agents and chemical weapons will be probably used because of their characteristics. This analytical research is done because key military zones mentioned above must be defensed against these kinds of attacks. While formulating the problem, to find the best locations for the point and the line of sight detectors which are used to identify chemical hazard and warn friendly forces about approaching danger, two models are structured and proposed on the basis of The Maximal Covering and The Maximal Expected Covering Models. In the proposed models, the optimum locations for detectors and alarms are found and the best matching between these detectors and alarms is given. In this research, for a threat point to be covered by a detector, that point must be in the detection range and an alarm must be linked to that detector. Additionally, for the line of the sight detection systems, a threat point must be in the line of sight of detectors. According to the method of this research, chemical defence systems are tried to be optimally set in key military zones during peace time well before attacks. With the proposed models, a solution and an analysis are done in a selected military zone as an example and that zone is tried to be defensed against a possible chemical terrorist attack.

Mining classification rules with Reduced MEPAR-miner Algorithm

Abstract In this study, a new classification technique based on rough set theory and MEPAR-miner algorithm for association rule mining is introduced. Proposed method is called as ‘Reduced MEPAR-miner Algorithm’. In the method being improved rough sets are used in the preprocessing stage in order to reduce the dimensionality of the feature space and improved MEPAR-miner algorithms are then used to extract the classification rules. Besides, a new and an effective default class structure is also defined in this proposed method. Integrating rough set theory and improved MEPAR-miner algorithm, an effective rule mining structure is acquired. The effectiveness of our approach is tested on eight publicly available binary and n -ary classification data sets. Comprehensive experiments are performed to demonstrate that Reduced MEPAR-miner Algorithm can discover effective classification rules which are as good as (or better) the other classification algorithms. These promising results show that the rough set approach is a useful tool for preprocessing of data for improved MEPAR-miner algorithm.

A combined approach for fuzzy multi-objective multiple knapsack problems for defence project selection

In this study, a model representing military requirements as scenarios and capabilities is offered. Pair-wise comparisons of scenarios are made according to occurrence probabilities by using the Analytical Hierarchy Process (AHP). The weights calculated from AHP are used as the starting weights in a Quality Function Deployment (QFD) matrix. QFD is used to transfer war fighter requirements into the benefit values of projects. Two levels of QFD matrices are used to evaluate new capability areas versus capabilities and capabilities versus projects. The benefit values of the projects are used in a multi-objective problem (multi-objective multiple knapsack problem) that considers the project benefit, implementation risks and environmental impact as multiple objectives. Implementation risk and environmental impact values are also calculated using the same combined AHP and QFD methodology. Finally, the results of the fuzzy multi-objective goal programming suggest a list of projects that offers optimal benefit when carried out within multiple budgets.