A Property Preserving Method for Extending a Single-Objective Problem Instance to Multiple Objectives with Specific Correlations

2016 ◽  
pp. 18-33 ◽  
Author(s):  
Ruby L. V. Moritz ◽  
Enrico Reich ◽  
Matthias Bernt ◽  
Martin Middendorf
Keyword(s):  
Multiple Objectives ◽  
Problem Instance ◽  
Single Objective

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

Composite Dispatching Rule Design for Photolithography Area Scheduling in Wafer Manufacturing System with Multiple Objectives

2012 ◽  
Vol 252 ◽  
pp. 418-421
Author(s):  
Sen Wang ◽  
Peng Zhang ◽  
Wei Qin ◽  
Jie Zhang
Keyword(s):  
Linear Combination ◽  
Performance Measures ◽  
Significant Role ◽  
Manufacturing System ◽  
Multiple Objectives ◽  
Wafer Fabrication ◽  
Scheduling Problem ◽  
Dispatching Rule ◽  
Wafer Manufacturing ◽  
Single Objective

This paper investigates the photolithography area scheduling problem in wafer fabrication system, with the objective of simultaneously optimizing multiple performance measures. It has been known that photolithography area is usually the bottleneck work center of a wafer fab, and the scheduling problem in this area plays a significant role in improving the performance of the fab. Most studies of this problem have been focusing on only single objective. Therefore an approach for composite dispatching rule design is proposed to tackle this problem with multiple objectives, and the composite dispatching rule is a linear combination of several elementary dispatching rules with relative weights. Results demonstrate that the composite dispatching rule achieved a great improvement on every objective, compared to the performance of single elementary dispatching rule.

scholarly journals A Survey of Multi-Objective Sequential Decision-Making

2013 ◽  
Vol 48 ◽  
pp. 67-113 ◽  
Author(s):  
D. M. Roijers ◽  
P. Vamplew ◽  
S. Whiteson ◽  
R. Dazeley
Keyword(s):  
Decision Making ◽  
Pareto Front ◽  
Optimal Solution ◽  
Multiple Objectives ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Multi Objective ◽  
Scalarization Function ◽  
Future Work ◽  
Single Objective

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.

Evolutionary Multi-Objective Optimization for Assignment Problems

2011 ◽  
pp. 364-387
Author(s):  
Mark P. Kleeman ◽  
Gary B. Lamont
Keyword(s):  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Assignment Problems ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Single Objective

Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single objective. This chapter focuses on assignment problems that have multiple objectives that need to be satisfied. In particular, this chapter looks at how multi-objective evolutionary algorithms have been used to solve some of these problems. Additionally, this chapter examines many of the operators that have been utilized to solve assignment problems and discusses some of the advantages and disadvantages of using specific operators.

scholarly journals Sen's Multi Objective Optimization (MOO) Technique-An Extension

2020 ◽  
Vol 06 (09) ◽  
pp. 77-79
Author(s):  
Chandra Sen
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

Linear programming has been very popular for achieving (maximizing or minimizing) a single objective with certain constraints. However, when objectives are more than one, linear programming becomes inefficient. Sen's multi-objective optimization (MOO) technique [1] is efficient in achieving multiple objectives simultaneously. Few modifications in Sen's MOO technique are proposed for improving its applicability for solving multi-objective optimization problems.

Multiobjective Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 47 ◽  
Author(s):  
Matthias Ehrgott
Keyword(s):  
Decision Making ◽  
Combinatorial Optimization ◽  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Combinatorial Optimization Problems ◽  
Multiobjective Optimization Problems ◽  
Single Objective

Using some real world examples I illustrate the important role of multiobjective optimization in decision making and its interface with preference handling. I explain what optimization in the presence of multiple objectives means and discuss some of the most common methods of solving multiobjective optimization problems using transformations to single objective optimisation problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article, I refer to the real world examples introduced at the beginning.

scholarly journals Evolutionary numerical simulation approach for design optimization of gas turbine blade cooling channels

2014 ◽  
Vol 5 ◽  
pp. A22 ◽  
Author(s):  
Narasimha R. Nagaiah ◽  
Christopher D. Geiger
Keyword(s):  
Gas Turbine ◽  
Turbine Blade ◽  
Cooling System ◽  
Weight Vector ◽  
Multiple Objectives ◽  
Gas Turbine Blade ◽  
Turbine Blade Cooling ◽  
Blade Cooling ◽  
Traditional Approaches ◽  
Single Objective

Gas turbine blade cooling system design is a multidisciplinary, iterative and often tedious task involving complex relationships among multiple design objectives. Typical blade design requires a broad range of expertise in the materials, structural, heat transfer, and cost optimization disciplines. The multiple objectives involved are often conflicting and must be solved simultaneously with equal importance. The traditional approaches researchers scalarize the multiple objectives into a single objective using a weight vector, thus transforming the original multiple objective problem into a single objective problem. This research addresses the shortcomings of existing traditional approaches of the optimization of blade cooling configuration design.

Sign in / Sign up

Export Citation Format

Share Document