scholarly journals Application of TOPSIS and AHP in the Multi-Objective Decision-Making Problems

2018 ◽  
Vol 228 ◽  
pp. 05002 ◽  
Author(s):  
Yihan Wang
Keyword(s):  
Decision Making ◽  
Theoretical Analysis ◽  
Optimization Problems ◽  
Basic Principles ◽  
Multi Objective ◽  
New Methods

The problems of multi-objective decision making are analysed and studied. In order to solve its optimization problems, the basic principles and application steps of TOPSIS and AHP are introduced in this paper. Then some practical examples are given to show how to apply these two new methods in multi-objective decision making problems. Finally, the advantage and feasibility of the TOPSIS and AHP methods are demonstrated by theoretical analysis.

scholarly journals Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Wenxiao Li ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Jianxin Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Decision Makers ◽  
Superior Performance ◽  
Test Problems ◽  
Mathematical Function ◽  
Hyperbolic Tangent ◽  
Nsga Ii ◽  
Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

scholarly journals A new method for decision making in multi-objective optimization problems

2012 ◽  
Vol 32 (2) ◽  
pp. 331-369 ◽  
Author(s):  
Oscar Brito Augusto ◽  
Fouad Bennis ◽  
Stephane Caro
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
New Method ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Statistical adjustment of the parameters of multi-objective optimization problems with design expert method

2020 ◽  
Vol 9 (1) ◽  
pp. 6
Author(s):  
Atefeh Hasan-Zadeh
Keyword(s):  
Decision Making ◽  
Experimental Design ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Design Expert ◽  
Multi Objective ◽  
Statistical Adjustment ◽  
Engineering And Technology ◽  
Conflicting Goals

Optimization methods in which one single criterion is considered cannot provide a comprehensive solution to various decision- making algorithms because they cannot consider the interchange of conflicting goals that sometimes conflict with one another. Multi-objective opti-mization is a suitable solution to this obstacle. Given the importance of multi-objective optimization problems in engineering and technology, adjusting the parameters of these types of problems will, in addition to the decision-making accuracy, facilitate the analysis of the results and makes it more applicable. For this purpose, multi-objective optimization using experimental design methods has been developed which can solve these problems by considering different objectives simultaneously. Mathematical modelling for the setting of the parameters of the considered problem with all the statistical details related to their prediction and optimization have been studied. 

Many-objective flexible job shop scheduling using NSGA-III combined with multi-attribute decision making

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840110 ◽  
Author(s):  
Chun Wang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Decision Making ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Reference Points ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Attribute Decision Making

This paper considers many-objective flexible job shop scheduling problem (MaOFJSP) in which the number of optimization problems is larger than three. An integrated multi-objective optimization method is proposed which contains both optimization and decision making. The non-dominated sorting genetic algorithm III (NSGA-III) is utilized to find a trade-off solution set by simultaneously optimizing six objectives including makespan, workload balance, mean of earliness and tardiness, cost, quality, and energy consumption. Then, an integrated multi-attribute decision-making method is introduced to select one solution that fits into the decision maker’s preference. NSGA-III is compared with three multi-objective evolutionary algorithms (MOEAs)-based scheduling methods, and the simulation results show that NSGA-III performs better in generating the Pareto solutions. In addition, the impacts of using different reference points and decoding methods are investigated.

scholarly journals A Simple Proposal for Including Designer Preferences in Multi-Objective Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 991
Author(s):  
Xavier Blasco ◽  
Gilberto Reynoso-Meza ◽  
Enrique A. Sánchez-Pérez ◽  
Juan Vicente Sánchez-Pérez ◽  
Natalia Jonard-Pérez
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Underlying Structure ◽  
Multi Objective Optimization ◽  
Number Of Solutions ◽  
Multi Objective ◽  
Objective Space ◽  
Definition Of

Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we call a preference basis that shows the preferred optimization directions in the objective space. Associated to this preference basis a new basis in the objective space—dominance basis—is computed. With this new basis the meaning of dominance is reinterpreted to include the designer’s preferences. In this paper, we show the effect of changing the geometric properties of the underlying structure of the Euclidean objective space by including preferences. This way of incorporating preferences is very simple and can be used in two ways: by redefining the optimization problem and/or in the decision-making phase. The approach can be used with any multi-objective optimization algorithm. An advantage of including preferences in the optimization process is that the solutions obtained are focused on the region of interest to the designer and the number of solutions is reduced, which facilitates the interpretation and analysis of the results. The article shows an example of the use of the preference basis and its associated dominance basis in the reformulation of the optimization problem, as well as in the decision-making phase.

A Framework for Time-Cost-Quality Optimization in Project Management Problems Using an Exploratory Grid Concept in the Multi-Objective Simulated-Annealing

2021 ◽  
pp. 1-26
Author(s):  
Alzira Mota ◽  
Paulo Ávila ◽  
Ricardo Albuquerque ◽  
Lino Costa ◽  
João Bastos
Keyword(s):  
Decision Making ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Decision Makers ◽  
Time Cost ◽  
Trade Off ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Criteria Weights ◽  
Optimal Set

Time, cost, and quality are the three indispensable factors for the realization and success of a project. In this context, we propose a framework composed of a multi-objective approach and multi-criteria decision-making methods (MCDM) to solve time-cost-quality trade-off optimization problems. A multi-objective Simulated Annealing (MOSA) algorithm is used to compute an approximation to the Pareto optimal set. The concept of the exploratory grid is introduced in the MOSA to improve its performance. MCDM are used to assist the decision-making process. The Shannon entropy and AHP methods assign weights to criteria. The first methodology is for the inexperienced decision-makers, and the second concedes a personal and flexible weighting of the criteria weights, based on the project manager’s assessment. The TOPSIS and VIKOR methods are considered to rank the solutions. Although they have the same purpose, the rankings achieved are different. A tool is implemented to solve a time-cost-quality trade-off problem on a project activities network. The computational experiments are analyzed and the results with the exploratory grid in Simulated Annealing (SA) are promising. Despite the framework aims to solve multi-objective trade-off optimization problems, supporting the decisions of the project manager, the methodologies used can also be applied in other areas.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

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