Particle swarm optimization algorithm for a batching problem in the process industry / Lixin Tang in Industrial & engineering chemistry research, Vol. 48 N° 20 (Octobre 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 20 (Octobre 2009) . - pp. 9186–9194 Titre : Particle swarm optimization algorithm for a batching problem in the process industry Type de document : texte imprimé Auteurs : Lixin Tang, Auteur ; Ping Yan, Auteur Année de publication : 2010 Article en page(s) : pp. 9186–9194 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Particle swarm optimization algorithmBatch processing plant Résumé : An improved particle swarm optimization (PSO) algorithm is proposed to solve a typical batching problem in a batch processing plant of the process industry. The batching problem (BP) is to transform the primary requirements for products into sets of batches for each task with the objective of minimizing the total workload. On the basis of some preliminary properties, a novel particle solution representation is designed for the BP. Unlike the ordinary idea of taking an objective function as the fitness function for PSO, the original objective function incorporated with a constraint function is to act as the fitness function of the PSO where the constraint and the objective functions are evaluated successively. Such a fitness function, together with a forward repair mechanism, makes it possible for a faster convergence. Further, for each iterative generation, a local search heuristic is used to improve the global best particle found so far. To verify the performance of the proposed PSO algorithm, the well-known benchmark batching instances are tested. The relatively large-scale instances are also added to evaluate the algorithm. The computational results show that the improved PSO may find optimal or suboptimal solutions within a much shorter run time for all the instances. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801742m [article] Particle swarm optimization algorithm for a batching problem in the process industry [texte imprimé] / Lixin Tang, Auteur ; Ping Yan, Auteur . - 2010 . - pp. 9186–9194. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 20 (Octobre 2009) . - pp. 9186–9194 Mots-clés : Particle swarm optimization algorithmBatch processing plant Résumé : An improved particle swarm optimization (PSO) algorithm is proposed to solve a typical batching problem in a batch processing plant of the process industry. The batching problem (BP) is to transform the primary requirements for products into sets of batches for each task with the objective of minimizing the total workload. On the basis of some preliminary properties, a novel particle solution representation is designed for the BP. Unlike the ordinary idea of taking an objective function as the fitness function for PSO, the original objective function incorporated with a constraint function is to act as the fitness function of the PSO where the constraint and the objective functions are evaluated successively. Such a fitness function, together with a forward repair mechanism, makes it possible for a faster convergence. Further, for each iterative generation, a local search heuristic is used to improve the global best particle found so far. To verify the performance of the proposed PSO algorithm, the well-known benchmark batching instances are tested. The relatively large-scale instances are also added to evaluate the algorithm. The computational results show that the improved PSO may find optimal or suboptimal solutions within a much shorter run time for all the instances. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801742m

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Particle swarm optimization algorithm for a campaign planning problem in process industries / Lixin Tang ; Ping Yan in Industrial & engineering chemistry research, Vol. 47 n°22 (Novembre 2008)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 47 n°22 (Novembre 2008) . - p. 8775–8784 Titre : Particle swarm optimization algorithm for a campaign planning problem in process industries Type de document : texte imprimé Auteurs : Lixin Tang, Auteur ; Ping Yan, Auteur Année de publication : 2008 Article en page(s) : p. 8775–8784 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : swarm algorithm Résumé : Campaign planning problem (CPP) is to determine the number and length of campaigns for different products over a planning horizon such that the setup and inventory holding costs are minimized. This problem can be found frequently in a multiproduct batch processing plant in the processing industry, such as chemical or pharmaceutical industries. This paper investigates a typical CPP and proposes a hybrid approach of heuristic and particle swarm optimization (PSO) algorithms where the PSO is applied to solve one subproblem with binary variables while the heuristic is applied to the other subproblem with remaining variables by fixing binary variables. As for the evaluation of particles, we take the whole objective function of the primal problem as a fitness function which can be calculated by solving the two subproblems. In implementing the PSO, by designing a “product-to-period” representation for a discrete particle, we redefine the particle position and velocity which are different from the standard PSO. Furthermore, a new strategy is developed to move a particle to the new position. To escape from local minima, a disturbance strategy is also introduced during the iteration process of the PSO. Computational results show that the proposed PSO may find optimal or near optimal solutions for the 180 instances generated randomly within a reasonable computational time. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800383y [article] Particle swarm optimization algorithm for a campaign planning problem in process industries [texte imprimé] / Lixin Tang, Auteur ; Ping Yan, Auteur . - 2008 . - p. 8775–8784. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°22 (Novembre 2008) . - p. 8775–8784 Mots-clés : swarm algorithm Résumé : Campaign planning problem (CPP) is to determine the number and length of campaigns for different products over a planning horizon such that the setup and inventory holding costs are minimized. This problem can be found frequently in a multiproduct batch processing plant in the processing industry, such as chemical or pharmaceutical industries. This paper investigates a typical CPP and proposes a hybrid approach of heuristic and particle swarm optimization (PSO) algorithms where the PSO is applied to solve one subproblem with binary variables while the heuristic is applied to the other subproblem with remaining variables by fixing binary variables. As for the evaluation of particles, we take the whole objective function of the primal problem as a fitness function which can be calculated by solving the two subproblems. In implementing the PSO, by designing a “product-to-period” representation for a discrete particle, we redefine the particle position and velocity which are different from the standard PSO. Furthermore, a new strategy is developed to move a particle to the new position. To escape from local minima, a disturbance strategy is also introduced during the iteration process of the PSO. Computational results show that the proposed PSO may find optimal or near optimal solutions for the 180 instances generated randomly within a reasonable computational time. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800383y

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
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