scholarly journals An Interactive Polyhedral Approach for Multi-objective Combinatorial Optimization with Incomplete Preference Information

2019 ◽  
pp. 221-235
Author(s):  
Nawal Benabbou ◽  
Thibaut Lust
Keyword(s):  
Combinatorial Optimization ◽  
Preference Information ◽  
Multi Objective ◽  
Incomplete Preference ◽  
Polyhedral Approach

scholarly journals Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

2019 ◽  
Vol 11 (9) ◽  
pp. 2619 ◽  
Author(s):  
Wei He ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Jili Kong
Keyword(s):  
Service Management ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Multiple Objectives ◽  
Second Phase ◽  
Time Cost ◽  
Two Phase ◽  
Preference Information ◽  
Multi Objective ◽  
Service Selection And Scheduling

Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.

An Interactive Signed Distance Approach for Multiple Criteria Group Decision-Making Based on Simple Additive Weighting Method with Incomplete Preference Information Defined by Interval Type-2 Fuzzy Sets

2014 ◽  
Vol 13 (05) ◽  
pp. 979-1012 ◽  
Author(s):  
Ting-Yu Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Information ◽  
Incomplete Preference ◽  
Signed Distance ◽  
Decision Making Problem ◽  
Simple Additive Weighting ◽  
Interval Type

Interval type-2 fuzzy sets (T2FSs) with interval membership grades are suitable for dealing with imprecision or uncertainties in many real-world problems. In the Interval type-2 fuzzy context, the aim of this paper is to develop an interactive signed distance-based simple additive weighting (SAW) method for solving multiple criteria group decision-making problems with linguistic ratings and incomplete preference information. This paper first formulates a group decision-making problem with uncertain linguistic variables and their transformation to interval type-2 trapezoidal fuzzy numbers. Concerning the relative importance of multiple decision-makers and group consensus of fuzzy opinions, a procedure using hybrid averages is then employed to construct a collective decision matrix. By an appropriate extension of the classical SAW approach, this paper utilizes the concept of signed distances and establishes an integrated programming model to manage multi-criteria group decisions under the incomplete and inconsistent preference structure. Further, an interactive procedure is established for group decision making. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a collaborative decision-making problem of patient-centered care (PCC).

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

2012 ◽  
pp. 1-16
Author(s):  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung Bae Cho
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Discrete Values ◽  
Continuous Domain

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

Core-guided method for constraint-based multi-objective combinatorial optimization

2020 ◽  
Author(s):  
Naiyu Tian ◽  
Dantong Ouyang ◽  
Yiyuan Wang ◽  
Yimou Hou ◽  
Liming Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Multi Objective

QMAEA: A quantum multi-agent evolutionary algorithm for multi-objective combinatorial optimization

SIMULATION ◽  
2013 ◽  
Vol 90 (2) ◽  
pp. 182-204 ◽  
Author(s):  
F Tao ◽  
Y J Laili ◽  
L Zhang ◽  
Z H Zhang ◽  
AY C Nee
Keyword(s):  
Combinatorial Optimization ◽  
Evolutionary Algorithm ◽  
Multi Objective ◽  
Multi Agent
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