An Interval-Index-Oriented Intelligent Optimization Method and its Industrial Application

2011 ◽  
Vol 219-220 ◽  
pp. 546-550
Author(s):  
Ming Shan Cai ◽  
Ling Shuang Kong
Keyword(s):  
Optimal Control ◽  
Industrial Application ◽  
Programming Model ◽  
Optimization Method ◽  
Quality Indices ◽  
Reasoning Strategy ◽  
Goal Programming Model ◽  
Interval Goal Programming ◽  
Expert Reasoning

Based on the strong coupling and interval requirement of multiple quality indices, the interval-index-oriented optimization method is proposed to effectively realize the optimal control of alumina blending process. Firstly, the lexicographic interval goal programming model is built to describe the process requirements for quality indices. Then, based on the characteristics of the programming model, a kind of classificatory knowledge base is constructed by using the empirical knowledge accumulated in long-term production and the expert reasoning strategy is proposed to realize the optimal control of quality indexes with interval constraints. The results of industrial application shows that the proposed method can realize the optimal control of quality indices. It provides a good optimization mode for other blending processes of nonferrous metal production.

Multicriteria Optimization Method for Network-Level Bridge Management

1996 ◽  
Vol 1561 (1) ◽  
pp. 37-43 ◽  
Author(s):  
V. Ravirala ◽  
D. A. Grivas ◽  
A. Madan ◽  
B. C. Schultz
Keyword(s):  
New York ◽  
Multicriteria Optimization ◽  
Capital Investment ◽  
Programming Model ◽  
Treatment Options ◽  
New York State ◽  
Optimization Method ◽  
Medium Size ◽  
Bridge Management ◽  
Goal Programming Model

A multicriteria optimization method for analyzing important capital investment decisions involved in managing bridge infrastructure is presented. The condition assessment and decision variables of the method can be adapted to analyze a population of small and medium-size bridges or a population of spans of a large bridge. Condition ratings of various bridge structural elements are used to assess the condition needs of four major components. Subsequent use of this information leads to characterization of bridge condition by defining bridge states. State increment models are used to identify suitable treatment options for each state and predict the variable time over which state increments (or transitions) occur. These state increment models are incorporated into an optimization method that has three major steps: (a) identification of objective functions representing the multiple decision criteria, (b) assessment of the importance of each objective in achieving the numerical goals targeted by decision makers, and (c) formulation of a goal programming model. The goal program determines an optimal multi-year bridge program that minimizes the weighted sum of deviations from goals. Important results from the analysis of capital program scenarios for more than 800 small and medium-size bridges managed by the New York State Thruway Authority are presented. It is concluded that the multicriteria optimization method provides a useful tool to analyze multiple goal-oriented scenarios for a bridge capital program and establish a relationship between average network condition rating and total expenditure.

scholarly journals Two-Stage Multiobjective Optimization for Emergency Supplies Allocation Problem under Integrated Uncertainty

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xuejie Bai
Keyword(s):  
Programming Model ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Optimization Method ◽  
Allocation Problem ◽  
Two Stage ◽  
Whole Process ◽  
Proposed Model ◽  
Goal Programming Model ◽  
Computational Difficulty

This paper proposes a new two-stage optimization method for emergency supplies allocation problem with multisupplier, multiaffected area, multirelief, and multivehicle. The triplet of supply, demand, and the availability of path is unknown prior to the extraordinary event and is descriptive with fuzzy random variable. Considering the fairness, timeliness, and economical efficiency, a multiobjective expected value model is built for facility location, vehicle routing, and supply allocation decisions. The goals of proposed model aim to minimize the proportion of demand nonsatisfied and response time of emergency reliefs and the total cost of the whole process. When the demand and the availability of path are discrete, the expected values in the objective functions are converted into their equivalent forms. When the supply amount is continuous, the equilibrium chance in the constraint is transformed to its equivalent one. To overcome the computational difficulty caused by multiple objectives, a goal programming model is formulated to obtain a compromise solution. Finally, an example is presented to illustrate the validity of the proposed model and the effectiveness of the solution method.

scholarly journals Estimating the farm-level economic costs of spring cropping to manage Alopecurus myosuroides (black-grass) in UK agriculture

2019 ◽  
Vol 157 (04) ◽  
pp. 318-332
Author(s):  
K. Ahodo ◽  
D. Oglethorpe ◽  
H. L. Hicks ◽  
R. P. Freckleton
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Farm Size ◽  
Economic Modelling ◽  
Short Term ◽  
Trade Off ◽  
Farm Level ◽  
Alopecurus Myosuroides ◽  
Goal Programming Model

AbstractCrop rotation is a non-chemical strategy adopted by farmers to manage weeds. However, not all crops in a rotation are equally profitable. Thus, there is potentially a trade-off between the costs and benefits of this strategy. The objective of the current study is to quantify this trade-off for the rotational control of an important weed (Alopecurus myosuroides). Data from 745 farms were used to parameterize a farm-level mixed-integer goal-programming model of the economics of spring cropping for weed control in UK agriculture. On average, the short-term loss of profit from spring cropping is greater than the benefits in terms of reduced herbicide usage and yield increases. These costs are greater when weed densities are low, so that spring cropping is an expensive strategy in the early stages of an infestation. However, there is a great deal of farm-to-farm variation: factors such as soil type and farm size are important and the current study highlights that economic modelling at the farm level is important in enabling farmers to make informed decisions. In general, however, if spring cropping is to be a successful strategy then the benefits to farmers will be in terms of long-term reductions in weed densities, but this will be at the expense of short-term profitability.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

scholarly journals A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

2021 ◽  
Author(s):  
Zahra Homayouni ◽  
Mir Saman Pishvaee ◽  
Hamed Jahani ◽  
Dmitry Ivanov
Keyword(s):  
Supply Chains ◽  
Large Scale ◽  
Programming Model ◽  
Supply Chain Design ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Economic Uncertainty ◽  
Cap And Trade ◽  
Sustainable Logistics ◽  
Goal Programming Model

AbstractAdoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

Allocation of teaching personnel: A goal programming model

1983 ◽  
Vol 17 (4) ◽  
pp. 211-216 ◽  
Author(s):  
Sheila M. Lawrence ◽  
Kenneth D. Lawrence ◽  
Gary R. Reeves
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Goal Programming Model ◽  
Teaching Personnel
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