Documents disponibles écrits par cet auteur

An Improved particle swarm optimization algorithm for the hybrid flowshop scheduling to minimize total weighted completion time in process industry / Lixin Tang in IEEE Transactions on control systems technology, Vol. 18 N° 6 (Novembre 2010) 

Public ISBD [article]  in IEEE Transactions on control systems technology > Vol. 18 N° 6 (Novembre 2010) . - pp. 1303-1314 Titre : An Improved particle swarm optimization algorithm for the hybrid flowshop scheduling to minimize total weighted completion time in process industry Type de document : texte imprimé Auteurs : Lixin Tang, Auteur ; Xianpeng Wang, Auteur Année de publication : 2011 Article en page(s) : pp. 1303-1314 Note générale : Génie Aérospatial Langues : Anglais (eng) Mots-clés : Hybrid  flowshop  scheduling  (HFS)  Hybrid  simulated  annealing  Hybrid  variable  neighborhood  search  Improved  particle  swarm  optimization  Three  level  population  update  method Index. décimale : 629.1 Résumé : In this paper, we present an improved particle swarm optimization (PSO) algorithm for the hybrid flowshop scheduling (HFS) problem to minimize total weighted completion time. This problem has a strong practical background in process industry. For example, the integrated production process of steelmaking, continuous-casting, and hot rolling in the iron and steel industry, and the short-term scheduling problem of multistage multiproduct batch plants in the chemical industry can be reduced to a HFS problem. To make PSO applicable in the HFS problem, we use a job permutation that is the processing order of jobs in the first stage to represent a solution, and construct a greedy method to transform this job permutation into a complete HFS schedule. In addition, a hybrid variable neighborhood search (VNS) incorporating variable depth search, a hybrid simulated annealing incorporating stochastic local search, and a three-level population update method are incorporated to improve the search intensification and diversification of the proposed PSO algorithm. Computational experiments on practical production data and randomly generated instances show that the proposed PSO algorithm can obtain good solutions compared to the lower bounds and other metaheuristics. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5357401 [article] An Improved particle swarm optimization algorithm for the hybrid flowshop scheduling to minimize total weighted completion time in process industry [texte imprimé] / Lixin Tang, Auteur ; Xianpeng Wang, Auteur . - 2011 . - pp. 1303-1314. Génie Aérospatial Langues : Anglais (eng) in IEEE Transactions on control systems technology > Vol. 18 N° 6 (Novembre 2010) . - pp. 1303-1314 Mots-clés : Hybrid  flowshop  scheduling  (HFS)  Hybrid  simulated  annealing  Hybrid  variable  neighborhood  search  Improved  particle  swarm  optimization  Three  level  population  update  method Index. décimale : 629.1 Résumé : In this paper, we present an improved particle swarm optimization (PSO) algorithm for the hybrid flowshop scheduling (HFS) problem to minimize total weighted completion time. This problem has a strong practical background in process industry. For example, the integrated production process of steelmaking, continuous-casting, and hot rolling in the iron and steel industry, and the short-term scheduling problem of multistage multiproduct batch plants in the chemical industry can be reduced to a HFS problem. To make PSO applicable in the HFS problem, we use a job permutation that is the processing order of jobs in the first stage to represent a solution, and construct a greedy method to transform this job permutation into a complete HFS schedule. In addition, a hybrid variable neighborhood search (VNS) incorporating variable depth search, a hybrid simulated annealing incorporating stochastic local search, and a three-level population update method are incorporated to improve the search intensification and diversification of the proposed PSO algorithm. Computational experiments on practical production data and randomly generated instances show that the proposed PSO algorithm can obtain good solutions compared to the lower bounds and other metaheuristics. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5357401

Parallel machine scheduling under the disruption of machine breakdown / Lixin Tang in Industrial & engineering chemistry research, Vol. 48 N° 14 (Juillet 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 14 (Juillet 2009) . - pp. 6660–6667 Titre : Parallel machine scheduling under the disruption of machine breakdown Type de document : texte imprimé Auteurs : Lixin Tang, Auteur ; Yanyan Zhang, Auteur Année de publication : 2009 Article en page(s) : pp. 6660–6667 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Scheduling  problem  Dealing  Machine  breakdown Résumé : This paper investigates the scheduling problem of dealing with disruption caused by machine breakdown under the environment of identical parallel machines. When a machine breakdown changes the scheduling environment, the original schedule cannot be executed according to the prescribed plan. The objective after disruption is to create a recovery schedule that minimizes the original objective function and the deviation from the original schedule. The original objective function is the total weighted completion time, and the deviation is the completion time of jobs from the planned one. The recovery scheduling problem is formulated as an integer programming model. A Lagrangian relaxation (LR) framework that relaxes the machine capacity constraints is proposed for solving the model. The subgradient method is used to update the values of Lagrangian multipliers. The extensive computational experiments are carried out, and the numerical results demonstrate that the proposed LR approach based on the developed model can provide an excellent recovery schedule solution in a timely manner. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801868f [article] Parallel machine scheduling under the disruption of machine breakdown [texte imprimé] / Lixin Tang, Auteur ; Yanyan Zhang, Auteur . - 2009 . - pp. 6660–6667. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 14 (Juillet 2009) . - pp. 6660–6667 Mots-clés : Scheduling  problem  Dealing  Machine  breakdown Résumé : This paper investigates the scheduling problem of dealing with disruption caused by machine breakdown under the environment of identical parallel machines. When a machine breakdown changes the scheduling environment, the original schedule cannot be executed according to the prescribed plan. The objective after disruption is to create a recovery schedule that minimizes the original objective function and the deviation from the original schedule. The original objective function is the total weighted completion time, and the deviation is the completion time of jobs from the planned one. The recovery scheduling problem is formulated as an integer programming model. A Lagrangian relaxation (LR) framework that relaxes the machine capacity constraints is proposed for solving the model. The subgradient method is used to update the values of Lagrangian multipliers. The extensive computational experiments are carried out, and the numerical results demonstrate that the proposed LR approach based on the developed model can provide an excellent recovery schedule solution in a timely manner. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801868f

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]  in Industrial & 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

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]  in Industrial & 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

Rolling horizon approach for dynamic parallel machine scheduling problem with release times / Lixin Tang in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 381–389 Titre : Rolling horizon approach for dynamic parallel machine scheduling problem with release times Type de document : texte imprimé Auteurs : Lixin Tang, Auteur ; Shujun Jiang, Auteur ; Jiyin Liu, Auteur Année de publication : 2010 Article en page(s) : pp. 381–389 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Rolling  Horizon  Approach  for  Dynamic  Parallel  Machine  Scheduling  Problem  with  Release  Times Résumé : In this paper, we study a dynamic parallel machine scheduling problem with release times, where the release times and processing times of jobs may change during the production process due to uncertainties. The problem is different from classical scheduling problems in the deterministic environment where all information of jobs is known at the beginning of the scheduling horizon and will not change during the operations throughout the whole horizon. In practice, there are often unpredictable events causing dynamic changes in job release times and/or processing times. Traditional optimization methods cannot solve the dynamic scheduling problem directly even though they have been successful in solving the static version of the problem. A model predictive control (MPC) strategy based rolling horizon approach is applied to tackle the dynamic parallel machine scheduling problem with the objective of minimizing the total weighted completion times of jobs, the energy consumption due to job waiting, and the total deviation of actual job completion times from those in the original schedule. When the MPC is applied to the problem, the rolling horizon approach allows applying a Lagrangian relaxation (LR) algorithm to solve the model of the scheduling problem in a rolling fashion. Computational experiments are carried out comparing the proposed method with the passive adjustment method often adopted by human schedulers. The result shows that the proposed method yields significantly better results, with 11.72% improvement on average. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900206m [article] Rolling horizon approach for dynamic parallel machine scheduling problem with release times [texte imprimé] / Lixin Tang, Auteur ; Shujun Jiang, Auteur ; Jiyin Liu, Auteur . - 2010 . - pp. 381–389. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 381–389 Mots-clés : Rolling  Horizon  Approach  for  Dynamic  Parallel  Machine  Scheduling  Problem  with  Release  Times Résumé : In this paper, we study a dynamic parallel machine scheduling problem with release times, where the release times and processing times of jobs may change during the production process due to uncertainties. The problem is different from classical scheduling problems in the deterministic environment where all information of jobs is known at the beginning of the scheduling horizon and will not change during the operations throughout the whole horizon. In practice, there are often unpredictable events causing dynamic changes in job release times and/or processing times. Traditional optimization methods cannot solve the dynamic scheduling problem directly even though they have been successful in solving the static version of the problem. A model predictive control (MPC) strategy based rolling horizon approach is applied to tackle the dynamic parallel machine scheduling problem with the objective of minimizing the total weighted completion times of jobs, the energy consumption due to job waiting, and the total deviation of actual job completion times from those in the original schedule. When the MPC is applied to the problem, the rolling horizon approach allows applying a Lagrangian relaxation (LR) algorithm to solve the model of the scheduling problem in a rolling fashion. Computational experiments are carried out comparing the proposed method with the passive adjustment method often adopted by human schedulers. The result shows that the proposed method yields significantly better results, with 11.72% improvement on average. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900206m

The charge batching planning problem in steelmaking process using lagrangian relaxation algorithm / Lixin Tang in Industrial & engineering chemistry research, Vol. 48 N° 16 (Août 2009) 

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A Two-phase heuristic for the production scheduling of heavy plates in steel industry / Lixin Tang in IEEE Transactions on control systems technology, Vol. 18 N° 1 (Janvier 2010) 

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