Solving fuzzy multiple objective generalized assignment problems directly via bees algorithm and fuzzy ranking

In this paper, a direct solution approach for solving fuzzy multiple objective generalized assignment problems is proposed. In the problem, the coefficients and right hand side values of the constraints and the objective function coefficients are defined as fuzzy numbers. The addressed problem also has a multiple objective structure where the goals are determined so as to minimize the total cost and the imbalance between the workload of the agents. The direct solution approach utilizes the fuzzy ranking methods to rank the objective function values and to determine the feasibility of the constraints within a metaheuristic search algorithm, known as bees algorithm. Different fuzzy ranking methods, namely signed distance, integral value and area based approach are used in bees algorithm. For the computational study, the effects of these fuzzy ranking methods on the quality of the solutions are also analyzed. (C) 2012 Elsevier Ltd. All rights reserved

Análise da eficiência na gestão do risco de fornecedores: aplicação de um modelo Data Envelopment Analysis

Atualmente a tendência dos negócios leva a cadeias de abastecimento complexas e dinâmicas, que consequentemente levanta questões relativas ao aumento do risco de fornecimento em torno dessas mesmas cadeias; pelo que existe cada vez mais uma necessidade dos gestores identificarem e gerirem o risco de um modo mais diversificado. Associado às cadeias de abastecimento estão os fornecedores. A maioria dos riscos relativos a estes, está incluída no contexto de risco de fornecimento, resultando assim numa problemática de seleção e avaliação de fornecedores. Riscos como baixa qualidade, atrasos na entrega, falha ou interrupção de fornecimento, são exemplos de fatores de risco associados. Neste contexto, um dos maiores desafios para as organizações atualmente é trabalharem com os melhores fornecedores do mercado, procurando garantir a estabilidade em termos de fornecimento, com as melhores condições possíveis, quer a nível de preço, qualidade, entre outros, exigindo cada vez mais relações comerciais eficientes com os fornecedores. Assim, este trabalho tem como objetivo o desenvolvimento de um modelo baseado no método Data Envelopment Analysis (DEA), que permite às organizações avaliar e melhorar a eficiência das suas relações comerciais na gestão de risco de fornecimento nas suas cadeias de abastecimento. Como tal, o modelo proposto é divido em dois casos, que diferem pela origem da obtenção dos seus valores. Ou seja, num dos casos é aplicada uma avaliação externa à organização, e no outro é utilizada uma avaliação interna, o que permitirá discutir a sua utilização. Segundo o modelo proposto verificou-se que a eficiência média foi de 93% no caso I e 94% no caso II. Concluindo-se ainda ambos os casos necessitam de melhorias nos fornecimentos ao nível de: Qualidade, Logística e Tecnologia, ou seja, melhorar a qualidade dos serviços prestados, diminuir os seus prazos de execução dos serviços/fornecimento de material e aumento do conhecimento tecnológico

Expert system for ship lock control based on computational intelligence

U disertaciji je dato jedno rešenje automatskog operativnog upravljanja dvosmernom brodskom prevodnicom sa jednom komorom. Kreiran je ekspertski sistem zasnovan na rasplinutoj (fuzzy) logici. Upravljački sistem je testiran na modelu brodske prevodnice koji je kreiran na osnovu statističkih podataka o gustini saobraćaja na hidrosistemu DTD (Dunav-Tisa-Dunav), na osnovu tehničke dokumentacije brodske prevodnice i na osnovu razgovora sa operaterima. Sistem je zatim optimizovan globalnim algoritmima optimizacije. Dobijeno rešenje se pokazalo značajno bolje u poređenju sa standardnim algoritmima odluke.This thesis presents a solution to automatic control of a two-way one-channel ship lock. Expert system based on fuzzy logic is designed. This control system is tested on model of ship lock created using statistical data of transportation density on DTD (Danube-Tisa-Danube) channel, using technical documentation of ship lock and interview with operators. The system is further optimized with global optimization techniques. Given solution proved to be significantly better than standard decision algorithms

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems