Lai Soon Lee

A Genetic Algorithm with Fuzzy Crossover Operator and Probability

The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions.

A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems

The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

A GENETIC ALGORITHM BASED ON SEXUAL SELECTION FOR THE MULTIDIMENSIONAL 0/1 KNAPSACK PROBLEMS

In this study, a new technique is presented for choosing mate chromosomes during sexual selection in a genetic algorithm. The population is divided into groups of males and females. During the sexual selection, the female chromosome is selected by the tournament selection while the male chromosome is selected based on the hamming distance from the selected female chromosome, fitness value or active genes. Computational experiments are conducted on the proposed technique and the results are compared with some selection mechanisms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

Centralized Resource Allocation for Connecting Radial and Nonradial Models

This paper examines an alternative approach to the centralized resource allocation model that indicates that all the units are under the control of an entity of the centralized decision maker. The proposed approach is a technique for connecting the two basic Radial CRA-BCC and Nonradial CRA-SBM models in an integrated structure called connected CRA-SBM. In the proposed model, exchanging the two parameter amounts can change the location of the analysis between the CRA-BCC and the CRA-SBM models and deal with the weaknesses inherent in such models. By remedying all the weaknesses in one model, the entire units are simply projected on the frontier line and one can obtain suitable benchmarks for each of them. In the offered model, all of the inputs and outputs, respectively, decrease and increase simultaneously. Lastly, numerical examples emphasize the significance of the offered method.

Directional slack-based measure for the inverse data envelopment analysis.

A novel technique has been introduced in this research which lends its basis to the Directional Slack-Based Measure for the inverse Data Envelopment Analysis. In practice, the current research endeavors to elucidate the inverse directional slack-based measure model within a new production possibility set. On one occasion, there is a modification imposed on the output (input) quantities of an efficient decision making unit. In detail, the efficient decision making unit in this method was omitted from the present production possibility set but substituted by the considered efficient decision making unit while its input and output quantities were subsequently modified. The efficiency score of the entire DMUs will be retained in this approach. Also, there would be an improvement in the efficiency score. The proposed approach was investigated in this study with reference to a resource allocation problem. It is possible to simultaneously consider any upsurges (declines) of certain outputs associated with the efficient decision making unit. The significance of the represented model is accentuated by presenting numerical examples.

Modified TAROT for cross-selling personal financial products

The Top Application characteristics Remainder Offer characteristics Tree (TAROT) was first introduced in 2007. This is a modified Classification and Regression Trees (CART) used to help decide which question(s) to ask potential applicants to customise an offer of a personal financial product so that it would have a high probability of take up. In this piece of work the authors are presenting, they have further modified the TAROT to cross TAROT, using its properties and modeling steps to deal with the issue of cross-selling. Since the bank already has ready customers, it would be ideal to cross-sell the financial products seeing that one can ask one (or more) further question(s) based on the initial offer to identify and customise another financial product to offer.

An improved GRMOD heuristic for container loading problem

The Container Loading Problem (CLP) is a study of loading a subset of goods or parcels of different sizes into a three-dimensional rectangular container of fixed dimensions such that the volume of packed boxes is maximized. In this paper, an improved version of the modified George and Robinson heuristic (iGRMOD) is developed to solve the CLP. Comparison computational results on benchmark data set from the literature will be presented. The performances of the iGRMOD are superior than the GRMOD and other heuristics reported in the literature.

ROUTING VEHICLES WITH ANTS

Routing vehicles involve the design of an optimal set of routes for a fleet of vehicles to serve a number of customers with known demands. This research develops an Ant Colony Optimization for the vehicle routing with one central depot and identical vehicles. The procedure simulates the behavior of real ants that always find the shortest path between their nest and a food source through a form of communication, pheromone trail. Finally, preliminary results on the learning of the algorithm testing on benchmark data set will be presented in this paper.

LOADING CONTAINERS WITH ANTS

Loading containers is like loading a subset of given three-dimensional rectangular boxes of different sizes into a three-dimensional rectangular container of fixed dimensions in order to achieve optimal space utilization. In this paper, Ant Colony Optimization (ACO) with its probabilistic decision rule is used to construct towers of boxes and to arrange them into the container. Some initial computational results on benchmark data set will be presented.

EFFICIENCY ANALYSIS OF CASPIAN CATTLE FEEDLOT FARMS BY DATA ENVELOPMENT ANALYSIS

Data envelopment analysis (DEA) is an excellent approach for evaluating the performance of decision making units (DMUs) that use multiple inputs to produce multiple outputs. This research utilizes the DEA to analyze the operating efficiency of Caspian cattle feedlot farms in Iran. The inputs utilized by the farms are number of calve, number of labors, total metabolizable energy intake, total crude protein intake and total cost of hygiene-treatment of calve; and the output considered is total live weigh gain of calve. By using two DEA models, the efficiency score for each farm is calculated and the efficient farms are ranked.