• Title/Summary/Keyword: Multiple Objective

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A Fuzzy-Goal Programming Approach For Bilevel Linear Multiple Objective Decision Making Problem

  • Arora, S.R.;Gupta, Ritu
    • Management Science and Financial Engineering
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    • v.13 no.2
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    • pp.1-27
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    • 2007
  • This paper presents a fuzzy-goal programming(FGP) approach for Bi-Level Linear Multiple Objective Decision Making(BLL-MODM) problem in a large hierarchical decision making and planning organization. The proposed approach combines the attractive features of both fuzzy set theory and goal programming(GP) for MODM problem. The GP problem has been developed by fixing the weights and aspiration levels for generating pareto-optimal(satisfactory) solution at each level for BLL-MODM problem. The higher level decision maker(HLDM) provides the preferred values of decision vector under his control and bounds of his objective function to direct the lower level decision maker(LLDM) to search for his solution in the right direction. Illustrative numerical example is provided to demonstrate the proposed approach.

Multiple Objective Scheduling of Flexible Manufacturing Systems Using Petri Nets (페트리네트를 이용한 유연생산시스템의 다중목표 스케쥴링)

  • Yim, Seong-Jin;Lee, Doo-Yong
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.21 no.5
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    • pp.769-779
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    • 1997
  • This paper presents an approach to multiple objective scheduling of flexible manufacturing systems(FMS). The approach is an extension of the scheduling method that formulates scheduling problems using Petrinets, and applies heuristic search to find optimal or near-optimal schedules with a single objective. New evaluation functions are developed to optimize simultaneously the makespan and the total operating cost. A scheduling example is used to demonstrate the effectiveness of the proposed approach.

Genetic algorithms for balancing multiple variables in design practice

  • Kim, Bomin;Lee, Youngjin
    • Advances in Computational Design
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    • v.2 no.3
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    • pp.241-256
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    • 2017
  • This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

Acquisition and Refinement of State Dependent FMS Scheduling Knowledge Using Neural Network and Inductive Learning (인공신경망과 귀납학습을 이용한 상태 의존적 유연생산시스템 스케쥴링 지식의 획득과 정제)

  • 김창욱;민형식;이영해
    • Journal of Intelligence and Information Systems
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    • v.2 no.2
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    • pp.69-83
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    • 1996
  • The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

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Simultaneous Optimization of Multiple Quality Characteristics in Laser Beam Cutting Using Taguchi Method

  • Dubey, Avanish Kumar;Yadava, Vinod
    • International Journal of Precision Engineering and Manufacturing
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    • v.8 no.4
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    • pp.10-15
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    • 2007
  • Taguchi methods have been used for a long time to improve the product quality and process performance of a manufacturing system, Few researchers have applied this methodology in laser beam cutting (LBC) of sheet metals and found the considerable improvement in cut qualities. In all experimental investigations of LBC so far, the objective was to optimize the single quality characteristic at a time. In this paper the simultaneous optimization of multiple quality characteristics such as Kerf width and material removal rate (MRR) during pulsed Nd:YAG LBC of thin sheet of magnetic material (high Silicon-steel) has been presented using Taguchi's quality loss function. The results show the considerable improvement in multiple S/N ratio as compared to initial cutting condition. Also, the comparison of results from single and multi-objective optimization have been presented and it was found that the loss in quality is always possible shifting from single quality to multiple quality optimization.

A QoS-Guaranteed Cell Selection Strategy for Heterogeneous Cellular Systems

  • Guo, Qiang;Xu, Xianghua;Zhu, Jie;Zhang, Haibin
    • ETRI Journal
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    • v.28 no.1
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    • pp.77-83
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    • 2006
  • In order to improve the accuracy of cell selection in heterogeneous cellular systems, this paper proposes a fuzzy multiple-objective decision-based cell selection (FMDCS) strategy. Since heterogeneous cellular systems have different access technologies and multiple traffic classes, the strategy adopts cell type, data rate, coverage, transmission delay, and call arrival rate as evaluation indices, and uses different weight vectors according to the traffic classes of the mobile host. Then, a fuzzy multiple-objective decision algorithm is applied to select the optimal cell from all candidates. This paper also gives an instance analysis and simulation. The instance analysis shows FMDCS makes different selections for different traffic classes. Simulation results of the after-handoff quality-of-service (QoS) show the selected cell can provide MH optimal service.

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The Mathematical Relationship Between the Region of Efficient Objective Value and the Region of Weight in Multiple Objective Linear Programming (다목적 선형계획 문제의 유효 목적함수 영역과 가중치 수리적 관계에 관한 연구)

  • 소영섭
    • Journal of the Korean Operations Research and Management Science Society
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    • v.19 no.2
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    • pp.119-128
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    • 1994
  • There are three important regions im Multiple Objective Linear Programming (MOLP). One is the region of efficient solutions, another is the region of weight to be used for finding efficient solutions, the third is the region of efficient (nondominated) objective values. In this paper, first, we find the condition of extreme point in the region of efficient objective values. Second, we find that the sum of the dimension of the weight region and the dimension of efficient objective values region is constant. Using the above, it is shown that we find the shape and dimension of weight region corresponding to the given region or efficient objective values and vice versa.

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A Constrained Multi-objective Computation Offloading Algorithm in the Mobile Cloud Computing Environment

  • Liu, Li;Du, Yuanyuan;Fan, Qi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.9
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    • pp.4329-4348
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    • 2019
  • Mobile cloud computing (MCC) can offload heavy computation from mobile devices onto nearby cloudlets or remote cloud to improve the performance as well as to save energy for these devices. Therefore, it is essential to consider how to achieve efficient computation offloading with constraints for multiple users. However, there are few works that aim at multi-objective problem for multiple users. Most existing works concentrate on only single objective optimization or aim to obtain a tradeoff solution for multiple objectives by simply setting weight values. In this paper, a multi-objective optimization model is built to minimize the average energy consumption, time and cost while satisfying the constraint of bandwidth. Furthermore, an improved multi-objective optimization algorithm called D-NSGA-II-ELS is presented to get Pareto solutions with better convergence and diversity. Compared to other existing works, the simulation results show that the proposed algorithm can achieve better performance in terms of energy consumption, time and cost while satisfying the constraint of the bandwidth.

STABILITY OF EQUIVALENT PROGRAMMING PROBLEMS OF THE MULTIPLE OBJECTIVE LINEAR STOCHASTIC PROGRAMMING PROBLEMS

  • Cho, Gyeong-Mi
    • Journal of the Korean Mathematical Society
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    • v.35 no.2
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    • pp.259-268
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    • 1998
  • In this paper the stochastic multiple objective programming problems where the right-hand-side of the constraints is stochastic are considered. We define the equivalent scalar-valued problem and study the stability of the equivalent scalar-valued problem with respect to the weight parameters and probability mesures under reasonable assumptions.

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