Bees algorithm for generalized assignment problem

Özbakır, Lale; Baykasoğlu, Adil; Tapkan, Pınar

doi:10.1016/j.amc.2009.11.018

Cited by 149 publications

(53 citation statements)

References 23 publications

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“…Davidovic et al 33 tried to find a solution to the problem of establishing an independent static schedule by making use of ABC. Özbakır et al 34 studied the performance of ABC's Advanced Assignment Problems where; beside small and medium sized problems, large sized problems were also measured and comparison with the existing studies in the literature was made. The study by Kashani et al 35 proposed the use of ABC for the assignment scheduling problem in the distribution system and they showed that the model is better than the Genetic Algorithm based models.…”

Section: Literature Studies On Artificial Bee Colony Algorithmmentioning

confidence: 99%

Applicability of artificial bee colony algorithm for nurse scheduling problems

Buyukozkan¹,

Sarucan²

2014

IJCIS

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This paper describes the first Artificial Bee Colony (ABC) Algorithm approach applied to nurse scheduling evaluated under different working environments. For this purpose, the model has been applied on a real hospital where data taken from different departments of the hospital were used and the schedules from the model were compared with the existing schedules. The results obtained indicated that the proposed model exhibits success in solving the nurse scheduling problems in hospitals.

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Section: Literature Studies On Artificial Bee Colony Algorithmmentioning

confidence: 99%

Applicability of artificial bee colony algorithm for nurse scheduling problems

Buyukozkan¹,

Sarucan²

2014

IJCIS

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“…The population based search algorithm of Bee was first proposed by Pham and his colleague [13]. The Bee is inspired by the natural foraging behaviour of honey bees to successfully find the optimal solution with various engineering problems [14]- [16]. The Bee requires a number of parameters to be set, namely: number of scout bees (x 1 ), number of elite bees (x 2 ), number of patches selected out of x 1 visited points (x 3 ), number of bees recruited for regions visited by "elite bees" (x 4 ) and number of bees recruited for the other (x 3 -x 2 ) selected patches (x 5 ).…”

Section: Two Phase Approximation Algorithm (2-pam)mentioning

confidence: 99%

Two Phase Approximation Method Based on Bat Algorithm on Multi-objective Aggregate Production Planning

Luangpaiboon¹

2017

IJMO

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Abstract-This paper proposes the two phase approximation method (2-PAM) based primarily on the concept of dynamic programming and metaheuristic. The multi-objective aggregate production planning (APP) model is solved by a pair of mathematical approaches. The desirability function approach was implemented to compromise all objectives of the aggregate production planning decision problem. The metaheuristic of bat and bee algorithms are first applied to provide the initial solutions in each stage. The dynamic programming approach is followed to combine all solutions. It was found that the results obtained from the proposed method based on bat algorithm can provide more effective and informative APP and more chance to achieve the optimal disaggregate plan when compared to the other variant of 2-PAM with bee algorithm.Index Terms-Aggregate production planning, desirability function, two phase approximation algorithm, bat algorithm, bee algorithm, dynamic programming. I. INTRODUCTIONConventional optimisation algorithms search for a single optimum based on a weighted sum of all objectives. If these objectives simultaneously get better or worse, mathematical approaches such as linear, non-linear and dynamic programming can effectively can guarantee global optimum. However, in real world problems if the objectives are conflicting and there is an increase in the number of decision variables, then there is not a single optimum and the number of evaluations of the recursive functions would exponentially increase. In order to find satisfactory solutions for these hard optimisation problems, meta-heuristics have been introduced to overcome the deficiencies of conventional algorithms. Meta-heuristics are widely used to solve complex problems in industry and services, in various areas and in this case it is applied to the engineering problem. Aggregate production planning (APP) is concerned with the determination of production, inventory, and work force levels. It has been used to plan the future process capacity to meet fluctuating or uncertain sales demand requirements over a medium-time horizon ranging from six months to one year. Conventionally, the scarce resources of the company are assumed to be constant during the planning horizon of interest and the planning eff ort is oriented toward the best utilisation of those resources, given the external demand requirements. The APP Manuscript received June 25, 2017; revised December 12, 2017. This work was supported in part by Thammasat University.P. Luangpaiboon is with Industrial Statistics and Operational Research Unit (ISO-RU), Department of Industrial Engineering, Faculty of Engineering, Thammasat University, 12120, Thailand (e-mail: lpongch@engr.tu.ac.th). considers various operation strategies over appropriate scarce resources. They consist of hiring, overtime, layoffs, backorders, subcontracting and inventory level. An aim is to simultaneously optimise all customer requirements via multiple objectives over a fixed planning horizon. The data based on the APP model from Th...

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“…In [9], the original BA has been modified in order to solve GAP. The first modification is that using EC-length parameter instead of ngh parameter.…”

Section: Ba Algorithmsmentioning

confidence: 99%

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

George¹,

Raimond²

2013

IJCA

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Till now many optimization algorithms are available for solving complex problems. The algorithm that has created an impact is the numerical function optimization. Some of the existing optimization algorithms are classified into evolutionary computing and metaheuristic methods. A comprehensive survey on numerical function optimization is presented below with a deeper view into different aspects of the algorithms.

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Bees algorithm for generalized assignment problem

Cited by 149 publications

References 23 publications

Applicability of artificial bee colony algorithm for nurse scheduling problems

Applicability of artificial bee colony algorithm for nurse scheduling problems

Two Phase Approximation Method Based on Bat Algorithm on Multi-objective Aggregate Production Planning

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

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