Multi-objective stochastic programming to solve manpower allocation problem

Ekhtiari, Mostafa; Ghoseiri, Keivan

doi:10.1007/s00170-012-4159-3

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…In this section, a hybrid approach is proposed to solve the above problem, inspiring from chance-constrained programming to deal with stochastic constraints and objectives, and possibilistic programming to handle fuzzy objectives and constraints. According to Abdelaziz et al (2007) and Ekhtiari and Ghoseiri (2013), to transform this stochastic objective into an equivalent deterministic inequality, an ideal value is required for the first objective. To this aim, ideal value of the first objective is determined by letting j ¼ 1 and x i1 ¼ 1.…”

Section: Formulation As a Multi-objective Programming Problemmentioning

confidence: 99%

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A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

2020

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Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is considered in a multi-criteria and uncertain environment. The proposed method in this paper inspired by the notion of heterogeneous decision-making problems in which decision-makers deal with different types of data. To this aim, a mathematical modeling-based approach is proposed considering different types of uncertainty in classification information. Demand information is considered to be stochastic due to its time-varying nature and cost information is considered to be fuzzy due to its cognitive ambiguity. A hybrid algorithm based on chance-constrained and possibilistic programming is proposed to solve the problems. Considering the stochastic nature of demand information, solving the proposed model using the hybrid algorithm, the classification of items to three classes of extremely important, class A, moderately important, class B, and relatively unimportant, class C, items are determined along with a minimum inventory level required to deal with the stochasticity of demands information. The proposed approach is applied to a case study of classifying 51 inventory items. The obtained results assigned 22%, 39%, and 39% of the items to A, B, and C classes, respectively. Keywords Multi-criteria inventory classification Á Chance-constrained programming Á Possibilistic programming Communicated by V. Loia.

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Section: Formulation As a Multi-objective Programming Problemmentioning

confidence: 99%

“…According to Abdelaziz et al ( 2007 ) and Ekhtiari and Ghoseiri ( 2013 ), to transform this stochastic objective into an equivalent deterministic inequality, an ideal value is required for the first objective. To this aim, ideal value of the first objective is determined by letting and .…”

Section: Solving Approachmentioning

confidence: 99%

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

2020

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“…In addition they presented a method for constructing statistical lower bounds for the optimal value of the considered problem and discussed how one should tune the underlying parameters. Ekhtiari et al [8] used multi objective stochastic programming to solve manpower allocation problem. They presented a novel combination of the chance constrained programming and the global criterion model for that which is called chance constrained global criterion.…”

Section: Literature Of Past Workmentioning

confidence: 99%

A New Model for Probabilistic Multi-Period Multi-Objective Project Selection Problem

Khalili‐Damghani¹,

Poortarigh²,

Pakgohar³

2018

dtetr

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The project selection problem is considered as one of the most imperative decisions for investor organizations. Due to non-deterministic nature of some criteria in the real world projects in this paper, a new model for project selection problem is proposed in which some parameters are assumed probabilistic. This model is formulated as a non-linear, multi-objective, multi-period, zero-one programming model. Then the epsilon constraint method and an algorithm are applied to check the Pareto front and to find optimal solutions. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Since the proposed model is more compatible with real world problems, the results are more tangible and trustable compared with deterministic cases. Implications of the proposed approach are discussed and suggestions for further work are outlined.Keywords: Project selection, multi objective programming, chance constraint, epsilon constraint method. INTRODUCTIONDecision making about selection or rejection of a project is considerable from both theoretical and practical aspects and it depends on satisfaction of financial and nonfinancial constraints of the problem. In the real world project selection problems there are normally more than one objective function. This makes the solution algorithm more complicated and time consuming. The purpose of this paper is decision making about selection of N projects in T periods of time such that the profit gets maximum and total cost of equipment, human resources and used raw materials become minimum. In real world problems many of parameters are unlikely to be deterministic and ignoring the stochastic model can lead to unreliable results. In this paper a new framework is proposed for modeling a project selection problem relying on the concept of risk and by applying a linear approximation on probabilistic constraints. The mentioned model is called "Mean-Risk" model which the main idea of it is maximizing the expected profit of selecting a project such that risk curve of this selection always be below of the confidence curve. Here without loss of generality and just for simplification, the confidence is assumed a linear function. Since some parameters are probabilistic, the constraints include these parameters are also probabilistic constraints. Chance constrained programming was developed as a means of describing constraints in the form of probability levels of attainment. Consideration of chance constraints allows the decision maker to consider objectives in terms of their attainment probability. This approach changes constraints with stochastic parameters to constraints with a confidence level as the threshold of decision maker, using variance-covariance matrix. If α is a predetermined confidence level desired by the decision maker, the implication is that a constraint will have a probability of satisfaction of α. After transforming the problem from probabilistic mode to deterministic mode, the resulting model i...

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“…The CCGC (min-max) model was proposed by Ekhtiari and Ghoseiri [24]. They introduced their deterministic equivalent programming model for solving multi-objective stochastic problems such as Program (1).…”

Section: The Ccgc (Min-max) Modelmentioning

confidence: 99%

“…It is conducted with the objective to improve the vendors' selection and evaluation process simultaneously at three aspects, that is, multiple criteria, random factors, and achievement of efficient solutions. For this purpose, the problem of vendor selection is introduced in the form of a mixed integer multiobjective stochastic problem as converted by CCGC (minmax) [24] into a mixed integer nonlinear single objective deterministic problem. The converted problem is nonlinear and its solution on the larger scale could be more timeconsuming; therefore, in order to solve it, not only Lingo software; but also ABC, PSO, and ICA have been used and their performance is compared and evaluated from the view of solutions quality and the speed of reaching solutions.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

Ekhtiari

Poursafary

2013

ISRN Artificial Intelligence

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It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem.

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Multi-objective stochastic programming to solve manpower allocation problem

Cited by 8 publications

References 41 publications

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

A New Model for Probabilistic Multi-Period Multi-Objective Project Selection Problem

Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm

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