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.

Optimization of Plane Slots Number for Container Yard Based on Two-Stage Stochastic Programming

2013 ◽  
Vol 807-809 ◽  
pp. 2845-2848
Author(s):  
Zi Jian Guo ◽  
Xu Hui Yu ◽  
Wen Yuan Wang ◽  
Guo Lei Tang
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Random Variable ◽  
Optimization Method ◽  
Low Carbon ◽  
Two Stage ◽  
The Sustainable Development ◽  
Proposed Model ◽  
Container Ports ◽  
Stochastic Programming Model

Appropriate plane slots number of the container yard is of great importance to the sustainable development of low-carbon container ports. Considering the container type, handling process, cost and benefit of unit yard space, etc., a two-stage stochastic programming model with the goal of maximizing the yard profit was established by choosing the maximum daily storage number of containers as a random variable. The maximum yard profit and the minimum penalty function are respectively chosen as the goal in the first and second stage. The example results show that the value obtained by the two-stage stochastic programming is smaller than that by the specification. The proposed model provides an optimization method for the determination of plane slots number through effectively lessening the influence of uncertainties and saving resource cost.

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.

scholarly journals Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yousaf Shad Muhammad ◽  
Saima Khan ◽  
Ijaz Hussain ◽  
Alaa Mohamd Shoukry ◽  
Sadaf Shamsuddin ◽  
...  
Keyword(s):  
Multiobjective Programming ◽  
Programming Model ◽  
Sample Selection ◽  
Minimum Cost ◽  
Fixed Cost ◽  
Optimal Sample ◽  
Proposed Model ◽  
Compromise Allocation ◽  
Goal Programming Model ◽  
The Cost

In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.

scholarly journals Lexicographic Goal Programming Model for Bank’s Performance Management

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ali AlArjani ◽  
Teg Alam
Keyword(s):  
Saudi Arabia ◽  
Performance Management ◽  
Goal Programming ◽  
Financial Management ◽  
Programming Model ◽  
Financial Statements ◽  
Operating Income ◽  
Proposed Model ◽  
Goal Programming Model ◽  
Lexicographic Goal Programming

Any bank’s financial management is essential to preparing the assets and liabilities for multiple goals. In this paper, we develop an optimal bank model for the financial management department in the Kingdom of Saudi Arabia. The lexicographic goal programming model was used to formulate the banks’ performance management. In this study, the six goals of one of the leading banks in Saudi Arabia, namely, maximize asset, minimize liability, maximize equity, maximize operating income, maximize net income, and maximizing total goal achievements in the financial statement, were studied. To illustrate the model, we have focused on Al Rajhi Bank’s financial statements as a case study. The data was obtained from the banks’ financial statements. The outcomes of the study exhibited that all goals were accomplished. This proposed model is dynamic because it will help examine the banks’ financial strengths located in the kingdom. As a result, the proposed model can guide banking firms in making decisions and developing strategies to deal with numerous monetary circumstances.

scholarly journals Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Lu Gan ◽  
Jiuping Xu
Keyword(s):  
Seismic Risk ◽  
Construction Projects ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Fuzzy Variable ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Transportation Systems ◽  
Fuzzy Random

This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach.

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 Binary goal programming model for optimizing tire selection using branch and bound algorithm

2021 ◽  
Vol 12 ◽  
pp. 8
Author(s):  
Shady Aly
Keyword(s):  
Decision Making ◽  
Branch And Bound ◽  
Goal Programming ◽  
Programming Model ◽  
Operating Conditions ◽  
Branch And Bound Algorithm ◽  
Adoption Decision ◽  
Proposed Model ◽  
Goal Programming Model ◽  
Design Selection

The problem of assessment and adoption of automotive tyre design specifications has not been addressed sufficiently in literature. This is in spite of its significance as a crucial component relevant to design and safety of the automobile. In this paper, a multi-objective optimization model of the tyre design trademark adoption decision is proposed. Multi-attribute or multi-criterion decision making techniques are heuristics providing good solution, but do not guarantee optimum solution. Up to date, there is no optimal yielding method for selection of vehicle tyre manufacturer or trademark based on prespecified design targets. The proposed model is formulated as a binary goal programming model for optimizing tyre trademark design selection decision by adopting an optimal tyre design trademark that best achieve design targets. The model is solved by the branch and bound algorithm. One advantage of the proposed model is flexibility to incorporate multiple design targets, tolerance limits and different constraints. The proposed model can support efficient and effective decision making concerning the adoption of tyre trademark design for new automobile or to re-adopt new design for new road vehicle operating conditions.

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