A soft computing-based approach to optimise queuing-inventory control problem

Alaghebandha, Mohammad; Hajipour, Vahid

doi:10.1080/00207721.2013.809614

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…Two of the most widely used multi-objective algorithms are NSGA-II and MOPSO. ICA, a novel algorithm that employs an evolutionary approach, was introduced by Atashpaz-Gargari and Lucas [33] in 2007 and has been used since then for solving many engineering problems [34]. The multi-objective version of this evolutionary algorithm, MOICA, which was introduced in 2013 by Enayatifar et al, utilizes the functionalities of ICA in the multi-objective area [19].…”

Section: Handling Multiple Objectivesmentioning

confidence: 99%

Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks

Basiri,

Masehian,

Taghiyareh

et al. 2024

Preprint

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Team performance and the composition of the members determine the success of a project. The multi-objective expert team formation problem seeks to identify a collaborative and affordable team of experts such that the hiring costs are minimized and mutual communication is maximized given a network of experts and an essential set of skills. A new swarm-based metaheuristic, called BRADO, has been utilized to address this NP-hard problem. The BRADO algorithm emulates the emigration phenomenon of intellectual elites of society. This problem has been solved by considering two different approaches, the Sigma, and the Multiplication methods, for integrating the affordability and collaboration objective functions. The problem has been solved for projects of varying scales by the proposed BRADO, Multi-objective PSO, NSGA-II, and Multi-objective ICA. Our experiments show that BRADO provides more efficient solutions to the multi-objective expert team formation problem than the other algorithms, especially for projects with more required skills.

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Section: Handling Multiple Objectivesmentioning

confidence: 99%

Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks

Basiri,

Masehian,

Taghiyareh

et al. 2024

Preprint

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“…Zhao et al [10] studied a multi-item continuous review inventory model with regarding resources' limitation and stochastic demands. Alaghebandha and Hajipour [11] presented a mathematical model for a multi-product continuous review inventory model and considered warehouse spaces, service level, number of shortages and cost of expected shortage limitations and finally used an ICA algorithm to solve the proposed model. Saracoglu et al [12] formulated a multi-product inventory models as an integer linear programming which considers some constraints such as capacity constraint and budget limitation and finally developed a genetic algorithm to solve their problem.…”

Section: Introductionmentioning

confidence: 99%

A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

Fattahi

Hajipour

Nobari

2015

Applied Soft Computing

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“…In this research, a chromosome represents different number of products ordered in different periods as presented by Alaghebandha and Hajipour (2015). A vector Alinezhad, Mahmoudi, andHajipour: Industrial Engineering &Management Systems Vol 15, No 4, December 2016, pp.354-363, © 2016 KIIE 358 is added to the chromosome structure to generate the prices.…”

Section: Initializationmentioning

confidence: 99%

“…They discussed two inventory policy of (s, S) and (s, Q) and obtained the joint distribution of the number of customers and the number of items in the inventory as the product of their marginal under the assumption that customers do not join when inventory level is zero. Alaghebandha and Hajipour (2015) proposed a multi-product continuous review inventory control problem within batch arrival queuing approach (M Qr /M/1) to find the optimal quantities of maximum inventory. They proposed an efficient imperialist competitive algorithm (ICA) to solve the model.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Soft Computing-Based Approaches to Optimize Inventory-Queuing-Pricing Problem under Fuzzy Considerations

Alinezhad¹,

Mahmoudi²,

Hajipour³

2016

Industrial Engineering and Management Systems

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Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.

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A soft computing-based approach to optimise queuing-inventory control problem

Cited by 9 publications

References 32 publications

Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks

Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks

A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

Multi-Objective Soft Computing-Based Approaches to Optimize Inventory-Queuing-Pricing Problem under Fuzzy Considerations

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