Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Multicriteria pathfinding in uncertain simulated environments

Dr. James Keller, Dissertation Supervisor.Includes vita.Field of study: Electrical and computer engineering."May 2018."Multicriteria decision-making problems arise in all aspects of daily life and form the basis upon which high-level models of thought and behavior are built. These problems present various alternatives to a decision-maker, who must evaluate the trade-offs between each one and choose a course of action. In a sequential decision-making problem, each choice can influence which alternatives are available for subsequent actions, requiring the decision-maker to plan ahead in order to satisfy a set of objectives. These problems become more difficult, but more realistic, when information is restricted, either through partial observability or by approximate representations. Pathfinding in partially observable environments is one significant context in which a decision-making agent must develop a plan of action that satisfies multiple criteria. In general, the partially observable multiobjective pathfinding problem requires an agent to navigate to certain goal locations in an environment with various attributes that may be partially hidden, while minimizing a set of objective functions. To solve these types of problems, we create agent models based on the concept of a mental map that represents the agent's most recent spatial knowledge of the environment, using fuzzy numbers to represent uncertainty. We develop a simulation framework that facilitates the creation and deployment of a wide variety of environment types, problem definitions, and agent models. This computational mental map (CMM) framework is shown to be suitable for studying various types of sequential multicriteria decision-making problems, such as the shortest path problem, the traveling salesman problem, and the traveling purchaser problem in multiobjective and partially observable configurations.Includes bibliographical references (pages 294-301)

Co-operative Labour-Management Relations in Australia

Report Presented to International Evidence: Worker-Management Institutions and Economic Performance Conference, U.S. Commission on the Future of Worker-Management Relations.Report_Hartnett_Australia.pdf: 1377 downloads, before Oct. 1, 2020

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Accelerating ant colony optimization by using local search

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics, computer science and real life problem solving or decision making application. Many different optimization techniques have been developed for solving such functional problem. In order to solving various problem computer Science introduce evolutionary optimization algorithm and their hybrid. In recent years, test functions are using to validate new optimization algorithms and to compare the performance with other existing algorithm. There are many Single Object Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular optimization technique for solving hard combination mathematical optimization problem. In this paper, we run ACO upon five benchmark function and modified the parameter of ACO in order to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested upon some benchmark function under both static and dynamic to evaluate performances. We choose wide range of benchmark function and compare results with existing DE and its hybrid DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin

Ranking strategic objectives in a strategy map based on logarithmic fuzzy preference programming and similarity method

This paper aims to rank strategic objectives in a strategy map to improve the efficiency of strategy implementation. Objectives are ranked based on strategic destinations using the combination of Logarithmic Fuzzy Preference Programming (LFPP) and similarity method. In the first step, the weight of strategic destinations is obtained using LFPP technique; then objectives are ranked by similarity method. Similarity method uses the concept of alternative gradient and magnitude for effectively solving the general multi-criteria analysis problem. Finally, objectives are ranked in an actual strategy map. As a practical and efficient tool, the proposed approach can assist managers and decision-makers in drawing more efficient output from strategy maps.publishe