A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem
碩士 === 義守大學 === 資訊管理學系 === 102 === The objectives of inventory management are to maintain high level of service quality by using least cost and to reduce the possibility of shortage in order to satisfy the requirements of customers at the meantime. The goals of this thesis are to design, model, simu...
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ndltd-TW-102ISU053960322015-10-14T00:23:51Z http://ndltd.ncl.edu.tw/handle/17203374824753393846 A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem 超啟發式多目標最佳化演算法於多準則存貨控制之研究 Tzu-ling Chen 陳姿伶 碩士 義守大學 資訊管理學系 102 The objectives of inventory management are to maintain high level of service quality by using least cost and to reduce the possibility of shortage in order to satisfy the requirements of customers at the meantime. The goals of this thesis are to design, model, simulate, implement, and verify efficient meta-heuristic multi-objective algorithms for the multiple decision making problems. Multiple criteria decision making research has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. In Multi-objective optimization (MOO), solutions/alternatives are implicitly defined by a set of constraints that bound a feasible region, and objective functions are then optimized in this region (continuous problems) or large set of alternatives and their performances according to the multiple evaluation criteria is given explicitly (discrete combinatorial problems). Multiple criteria decision making (MCDM) addresses mainly discrete problems with not very large (combinatorial) sets of alternatives. Both MOO and MCDM involve evaluation of solutions/alternatives based on multiple criteria/objectives and search for trade-off solution(s) (e.g. using Pareto dominance). In some context, the benefits of utilizing MCDM include that conflicting design objectives are taken into account simultaneously leading to an overall insight of the problems which would deliver a significant and competitive advantage to the engineering design community. In some sense, the benefits of MOO include that the conflicting objectives are taken into account simultaneously, via practically implementing and testing Pareto-optimal solutions. It is very important that before the actual decision about the final solution takes place the decision maker should gain a good understanding about the trade-offs between the solution alternatives. Then the final decision can be firmly taken. Therefore, MOO approaches for creating Pareto-optimal solutions are considered vital to MCDM community. Implementing the MCDM task for solving optimization problem is considered as a very important and in the same time complicated approach for researchers to pursue. The aim of this thesis is to exploit synergistic meta-heuristics for specific topics and mathematical models in the field of multiple criteria decision analysis and multi-objective optimization and to algorithmic problems raised by the use of such models. Meta-heuristic algorithms are often nature-inspired, and they are becoming very powerful in solving optimization problems. To model the classic tradeoff between local search and global exploration, we investigate synergistic strategies for meta-heuristic multi-objective optimization learning, with an emphasis on the balance between intensification and diversification. To this aim, we propose a behavioral diversity preservation mechanism and multi-objective algorithm. This thesis intends to design a multi-objective bat algorithm, from analysis to their applications. We try to analyze the main components of this algorithm and how and why it work efficiently. The created algorithm is applied to multiple criteria inventory control problems. Jiann-Horng Lin 林建宏 2014 學位論文 ; thesis 72 zh-TW |
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碩士 === 義守大學 === 資訊管理學系 === 102 === The objectives of inventory management are to maintain high level of service quality by using least cost and to reduce the possibility of shortage in order to satisfy the requirements of customers at the meantime. The goals of this thesis are to design, model, simulate, implement, and verify efficient meta-heuristic multi-objective algorithms for the multiple decision making problems. Multiple criteria decision making research has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. In Multi-objective optimization (MOO), solutions/alternatives are implicitly defined by a set of constraints that bound a feasible region, and objective functions are then optimized in this region (continuous problems) or large set of alternatives and their performances according to the multiple evaluation criteria is given explicitly (discrete combinatorial problems). Multiple criteria decision making (MCDM) addresses mainly discrete problems with not very large (combinatorial) sets of alternatives. Both MOO and MCDM involve evaluation of solutions/alternatives based on multiple criteria/objectives and search for trade-off solution(s) (e.g. using Pareto dominance). In some context, the benefits of utilizing MCDM include that conflicting design objectives are taken into account simultaneously leading to an overall insight of the problems which would deliver a significant and competitive advantage to the engineering design community. In some sense, the benefits of MOO include that the conflicting objectives are taken into account simultaneously, via practically implementing and testing Pareto-optimal solutions. It is very important that before the actual decision about the final solution takes place the decision maker should gain a good understanding about the trade-offs between the solution alternatives. Then the final decision can be firmly taken. Therefore, MOO approaches for creating Pareto-optimal solutions are considered vital to MCDM community. Implementing the MCDM task for solving optimization problem is considered as a very important and in the same time complicated approach for researchers to pursue. The aim of this thesis is to exploit synergistic meta-heuristics for specific topics and mathematical models in the field of multiple criteria decision analysis and multi-objective optimization and to algorithmic problems raised by the use of such models. Meta-heuristic algorithms are often nature-inspired, and they are becoming very powerful in solving optimization problems. To model the classic tradeoff between local search and global exploration, we investigate synergistic strategies for meta-heuristic multi-objective optimization learning, with an emphasis on the balance between intensification and diversification. To this aim, we propose a behavioral diversity preservation mechanism and multi-objective algorithm. This thesis intends to design a multi-objective bat algorithm, from analysis to their applications. We try to analyze the main components of this algorithm and how and why it work efficiently. The created algorithm is applied to multiple criteria inventory control problems.
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author2 |
Jiann-Horng Lin |
author_facet |
Jiann-Horng Lin Tzu-ling Chen 陳姿伶 |
author |
Tzu-ling Chen 陳姿伶 |
spellingShingle |
Tzu-ling Chen 陳姿伶 A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
author_sort |
Tzu-ling Chen |
title |
A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
title_short |
A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
title_full |
A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
title_fullStr |
A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
title_full_unstemmed |
A Study of Multi-objective Meta-heuristic Algorithm for Multiple Criteria Inventory Control Problem |
title_sort |
study of multi-objective meta-heuristic algorithm for multiple criteria inventory control problem |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/17203374824753393846 |
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