Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

Berrichi, Ali; Yalaoui, Farouk; Amodeo, Lionel; Mezghiche, Mohamed

doi:10.1016/j.cor.2009.11.017

Cited by 147 publications

(70 citation statements)

References 24 publications

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“…The ants choose the nodes during their trip according to a probabilistic function. The probabilistic function is given by heuristic information of the problems, the pheromone information, and specific parameters such as alpha and beta [19,20]. This algorithm was used successfully to solve various difficult combinatorial problems in optimization [21].…”

Section: Ant Colony Optimization Metaheuristicmentioning

confidence: 99%

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Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

et al. 2015

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Original scientific paper This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions. Keywords: Ant Colony Optimization; Hyper-Cube; scheduling; Software Project Management Programiranje računarskog projekta primjenom Hyper-Cube algoritma za optimizaciju kolonije mravaIzvorni znanstveni članak U radu se daje prijedlog dizajna paradigme algoritma za optimizaciju kolonije mrava primjenom Hyper-Cube sustava za rješenje problema programiranja računarskog projekta (Software Project Scheduling Problem). Taj se NP-hard problem sastoji od davanja zaduženja zaposlenicima u svrhu smanjenja trajanja projekta i njegovih ukupnih troškova. To zaduženje mora zadovoljiti ograničenja problema i pitanje prvenstva među zadacima. Pristup prikazan ovdje koristi Hyper-Cube sustav za uspostavljanje eksplicitno multidimenzionalnog prostora za kontrolu ponašanja mravi. Time nam se omogućava autonomno vođenje istraživanja u cilju pronalaženja ohrabrujućih rješenja.

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Section: Ant Colony Optimization Metaheuristicmentioning

confidence: 99%

“…The ants choose the paths in the graph according to a probability function. The probabilistic function used by ants is based on the heuristic information from the problem and the memoristic information [19,20].…”

Section: Graph Representation For Spspmentioning

confidence: 99%

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

et al. 2015

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“…Berrichi et al [22] proposed multi-objective ACO based approach to optimize production and maintenance scheduling. The goal of their approach is to identify the best assignment of production tasks to machines along with the reduction in preventive maintenance (PM) periods of the production system.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Black Hole Algorithm for Bi-Criteria Job Scheduling on Parallel Machines

Jeet¹,

Dhir²,

Singh³

et al. 2016

IJISA

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Abstract-Nature-inspired algorithms are recently being appreciated for solving complex optimization and engineering problems. Black hole algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from black hole theory of universe. In this paper, four formulations of multi-objective black hole algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA). These formulations are further applied for scheduling jobs on parallel machines while optimizing bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Black Hole algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting job sequence. The proposed algorithms are further compared to some of the existing algorithms, and empirically found to be better. The results have been validated by numerical illustrations and statistical tests.

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“…[16,65,66,67]), but incorporates the preference model from [15]. The algorithm performs the optimization process through a set of agents called ants.…”

Section: Our Proposalmentioning

confidence: 99%

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Cruz¹,

Fernández²,

Gómez³

et al. 2014

Appl. Math. Inf. Sci.

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Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

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Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

Cited by 147 publications

References 24 publications

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

Hybrid Black Hole Algorithm for Bi-Criteria Job Scheduling on Parallel Machines

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

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