Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives

2006 ◽  
Vol 175 (1) ◽  
pp. 566-592 ◽  
Author(s):  
J. Sudhir Ryan Daniel ◽  
Chandrasekharan Rajendran
Keyword(s):  
Supply Chain ◽  
Multiple Objectives ◽  
Base Stock ◽  
Heuristic Approaches ◽  
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.

Computing base-stock levels for a two-stage supply chain with uncertain supply

Omega ◽  
2019 ◽  
Vol 89 ◽  
pp. 92-109
Author(s):  
Donald P. Warsing ◽  
Worawut Wangwatcharakul ◽  
Russell E. King
Keyword(s):  
Supply Chain ◽  
Base Stock ◽  
Two Stage ◽  
Uncertain Supply

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.

Meta-heuristic approaches for fixed-charge solid transportation problem in two-stage supply chain network

2017 ◽  
Vol 20 (1) ◽  
pp. 447-471 ◽  
Author(s):  
Mojtaba Akbari ◽  
Saber Molla-Alizadeh-Zavardehi ◽  
Sadegh Niroomand
Keyword(s):  
Supply Chain ◽  
Transportation Problem ◽  
Fixed Charge ◽  
Supply Chain Network ◽  
Solid Transportation Problem ◽  
Two Stage ◽  
Heuristic Approaches
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