A Goal Programming Approach to Multichoice Multiobjective Stochastic Transportation Problems with Extreme Value Distribution

Qahtani, Hadeel Al; El-Hefnawy, Ali; El-Ashram, Maha; Fayomi, Aisha

doi:10.1155/2019/9714137

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…The paper [18] introduced a goal programming approach to multichoice multiobjective stochastic transportation problems with extreme value distribution.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

A novel approach for solving decision-making problems with stochastic linear-fractional models

Laith

Al-Salih

Habeeb

2021

EEJET

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Stochastic chance-constrained optimization has a wide range of real-world applications. In some real-world applications, the decision-maker has to formulate the problem as a fractional model where some or all of the coefficients are random variables with joint probability distribution. Therefore, these types of problems can deal with bi-objective problems and reflect system efficiency. In this paper, we present a novel approach to formulate and solve stochastic chance-constrained linear fractional programming models. This approach is an extension of the deterministic fractional model. The proposed approach, for solving these types of stochastic decision-making problems with the fractional objective function, is constructed using the following two-step procedure. In the first stage, we transform the stochastic linear fractional model into two stochastic linear models using the goal programming approach, where the first goal represents the numerator and the second goal represents the denominator for the stochastic fractional model. The resulting stochastic goal programming problem is formulated. The second stage implies solving stochastic goal programming problem, by replacing the stochastic parameters of the model with their expectations. The resulting deterministic goal programming problem is built and solved using Win QSB solver. Then, using the optimal value for the first and second goals, the optimal solution for the fractional model is obtained. An example is presented to illustrate our approach, where we assume the stochastic parameters have a uniform distribution. Hence, the proposed approach for solving the stochastic linear fractional model is efficient and easy to implement. The advantage of the proposed approach is the ability to use it for formulating and solving any decision-making problems with the stochastic linear fractional model based on transforming the stochastic linear model to a deterministic linear model, by replacing the stochastic parameters with their corresponding expectations and transforming the deterministic linear fractional model to a deterministic linear model using the goal programming approach

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“…The paper [18] introduced a goal programming approach to multichoice multiobjective stochastic transportation problems with extreme value distribution.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

A novel approach for solving decision-making problems with stochastic linear-fractional models

Laith

Al-Salih

Habeeb

2021

EEJET

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“…The latter considers the preferences of the decision-makers before the optimization and any solution found is within the Pareto front. Due to a reduction in solution time, the latter is generally preferred over the former [22,25].…”

Section: Multi-objective Optimization and Goal Programmingmentioning

confidence: 99%

“…Goal-programming is a multi-objective optimization approach that falls under the methods with a priori articulation of preferences, and thus inherits its merits. Goal-programming is more beneficial for such problems that have conflicting objective functions [25]. All the parameters of objective functions are assigned a goal or a target value to be accomplished.…”

Section: Multi-objective Optimization and Goal Programmingmentioning

confidence: 99%

Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids

Hussain

Kim

2020

Sustainability

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Renewable-based off-grid microgrids are considered as a potential solution for providing electricity to rural and remote communities in an environment-friendly manner. In such systems, energy storage is commonly utilized to cope with the intermittent nature of renewable energy sources. However, frequent usage may result in the fast degradation of energy storage elements. Therefore, a goal-programming-based multi-objective optimization problem has been developed in this study, which considers both the energy storage system (battery and electric vehicle) degradation and the curtailment of loads and renewables. Initially, goals are set for each of the parameters and the objective of the developed model is to minimize the deviations from those set goals. Degradation of battery and electric vehicles is quantified using deep discharging, overcharging, and cycling frequency during the operation horizon. The developed model is solved using two of the well-known approaches used for solving multi-optimization problems, the weighted-sum approach and the priority approach. Five cases are simulated for each of the methods by varying weight/priority of different objectives. Besides this, the impact of weight and priority values selected by policymakers is also analyzed. Simulation results have shown the superiority of the weighted-sum method over the priority method in solving the formulated problem.

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“…In this paper, he analysed the multi-choice stochastic TP where the objective function cost coefficients and demand parameters for these constraints are multi-choice parameters. Al Qahtani et al [20] studied a multi-choice multi-objective transportation problem in which at least one of the goals has multiple aspiration levels, and supply and demand parameters are random variables that have not been predetermined.…”

Section: Introductionmentioning

confidence: 99%

Multi-choice Linear Programming in Fuzzy Random Hybrid Uncertainty Environment and Their Application in Multi-commodity Transportation Problem

Nasseri

Bavandi

2020

Fuzzy Information and Engineering

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In this paper, due to increasing competition in the business world, which makes decision makers dealing with multiple options/ information for optimal decisions on a single task, we will look at multi-choice programming in hybrid fuzzy random environment. Alternative choices multi-choice parameters are considered as fuzzy random variables. By using polynomials interpolation for each multichoice parameter, the model is transformed into a fuzzy random programming problem. Then, to convert this model to its deterministic form, we use the concept of the mean value of fuzzy random variables. Finally, to validate the proposed mathematical operations, we solve a multi-commodity transportation problem with fuzzy random multi-choice parameters.

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A Goal Programming Approach to Multichoice Multiobjective Stochastic Transportation Problems with Extreme Value Distribution

Cited by 16 publications

References 19 publications

A novel approach for solving decision-making problems with stochastic linear-fractional models

A novel approach for solving decision-making problems with stochastic linear-fractional models

Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids

Multi-choice Linear Programming in Fuzzy Random Hybrid Uncertainty Environment and Their Application in Multi-commodity Transportation Problem

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