The Experts below are selected from a list of 32130 Experts worldwide ranked by ideXlab platform

Chinchih Chang - One of the best experts on this subject based on the ideXlab platform.

  • hybrid Simulated Annealing Algorithm with mutation operator to the cell formation problem with alternative process routings
    Expert Systems With Applications, 2009
    Co-Authors: Taihsi Wu, Shuhsing Chung, Chinchih Chang
    Abstract:

    In this study, a hybrid Simulated Annealing Algorithm with mutation operator is proposed to solve the manufacturing cell formation problem considering multiple process routings for parts, so that either the intercellular movements are minimized or the grouping efficacy is maximized, depending on the definition of the decision objective. The proposed Algorithm is designed mainly to explore solution regions efficiently and to expedite the solution search process. The performance of the proposed Algorithm is tested by a range of test problems, some of which are from the literature and some of which are generated within this study. The comparative study shows that the proposed Algorithm improves the best results found in the literature for 28.6% of the test problems and the percentages of improvement are even higher than 18% in several test instances.

  • a Simulated Annealing Algorithm for manufacturing cell formation problems
    Expert Systems With Applications, 2008
    Co-Authors: Chinchih Chang, Shuhsing Chung
    Abstract:

    The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective Simulated Annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic Algorithm. The corresponding results are compared to several well-known Algorithms published. The comparative study shows that the proposed SACF Algorithm improves the grouping efficacy for 72% of the test problems. The proposed Algorithm should thus be useful to both practitioners and researchers.

Shuhsing Chung - One of the best experts on this subject based on the ideXlab platform.

  • hybrid Simulated Annealing Algorithm with mutation operator to the cell formation problem with alternative process routings
    Expert Systems With Applications, 2009
    Co-Authors: Taihsi Wu, Shuhsing Chung, Chinchih Chang
    Abstract:

    In this study, a hybrid Simulated Annealing Algorithm with mutation operator is proposed to solve the manufacturing cell formation problem considering multiple process routings for parts, so that either the intercellular movements are minimized or the grouping efficacy is maximized, depending on the definition of the decision objective. The proposed Algorithm is designed mainly to explore solution regions efficiently and to expedite the solution search process. The performance of the proposed Algorithm is tested by a range of test problems, some of which are from the literature and some of which are generated within this study. The comparative study shows that the proposed Algorithm improves the best results found in the literature for 28.6% of the test problems and the percentages of improvement are even higher than 18% in several test instances.

  • a Simulated Annealing Algorithm for manufacturing cell formation problems
    Expert Systems With Applications, 2008
    Co-Authors: Chinchih Chang, Shuhsing Chung
    Abstract:

    The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective Simulated Annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic Algorithm. The corresponding results are compared to several well-known Algorithms published. The comparative study shows that the proposed SACF Algorithm improves the grouping efficacy for 72% of the test problems. The proposed Algorithm should thus be useful to both practitioners and researchers.

Chandrasekharan Rajendran - One of the best experts on this subject based on the ideXlab platform.

  • a multi objective Simulated Annealing Algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs
    European Journal of Operational Research, 2005
    Co-Authors: T K Varadharajan, Chandrasekharan Rajendran
    Abstract:

    In this paper, we consider the problem of permutation flowshop scheduling with the objectives of minimizing the makespan and total flowtime of jobs, and present a Multi-Objective Simulated-Annealing Algorithm (MOSA). Two initial sequences are obtained by using simple and fast existing heuristics, supplemented by the implementation of three improvement schemes. Each of the two resultant sequences corresponds to a possible non-dominated solution containing the minimum value of one objective function. These sequences, taken one at a time, are given as the starting sequences to the MOSA. The MOSA seeks to obtain non-dominated solutions through the implementation of a simple probability function that attempts to generate solutions on the Pareto-optimal front. The probability function selects probabilistically a particular objective function, considering which the Algorithm uncovers non-dominated solutions. Moreover, the probability function is varied in such a way that the entire objective-function space is covered uniformly so as to obtain as many non-dominated and well-dispersed solutions as possible. The parameters in the proposed MOSA are determined after conducting a pilot study. Two variants of the proposed Algorithm, called MOSA-I and MOSA-II, with different parameter settings with respect to the temperature and epoch length, are considered in the performance evaluation of Algorithms. In order to evaluate MOSA-I and MOSA-II, we have made use of 90 benchmark problems provided by Taillard [Eur. J. Operation. Res. 64 (1993) 278]. After an extensive literature survey, the following flowshop multi-objective scheduling Algorithms have been identified as benchmark procedures: (a) MOGLS (Multi-Objective Genetic Local Search) by Ishibuchi and Murata [IEEE Trans. Syst., Man, Cybernet. C: Appl. Rev. 28 (1998) 392]; (b) Elitist Non-dominated sorting Genetic Algorithm (ENGA) by Bagchi [Multi-Objective Scheduling by Genetic Algorithms, Kluwer Academic Publishers, 1999]; (c) GPW (Gradual Priority Weighting) approach by Chang, Hsieh and Lin [Int. J. Prod. Econ. 79 (2002) 171]; and (d) a posteriori approach based heuristic by Framinan, Leisten and Ruiz-Usano [Eur. J. Operation. Res. 141 (2002) 559]. The non-dominated sets obtained from each of the existing benchmark Algorithms and the proposed MOSA-I and MOSA-II are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by MOSA-I and MOSA-II. In addition, it is noteworthy that both MOSA-I and MOSA-II require less computational effort than the MOGLS, ENGA and GPW.

Hongbing Liu - One of the best experts on this subject based on the ideXlab platform.

  • hybrid Simulated Annealing Algorithm based on adaptive cooling schedule for tsp
    Genetic and Evolutionary Computation Conference, 2009
    Co-Authors: Yi Liu, Shengwu Xiong, Hongbing Liu
    Abstract:

    The traveling salesman problem is one of the most notoriously intractable NP-complete optimization problems. Over the last 10 years, Simulated Annealing and tabu search have emerged as an effective Algorithm for the TSP. However, the quality of solutions found by using tabu search approach depends on the initial solution and the iteration process of Simulated Annealing is slow. To overcome this problem and provide an efficient methodology for the TSP, the heuristic search approach based on Simulated Annealing which combining tabu search strategy and two neighborhood perturbation factor is developed. The proposed hybrid Algorithm is tested on standard benchmark sets and compared with the conventional Simulated Annealing Algorithm. The computational results show that the proposed Algorithm has significantly better convergence speed compared with conventional Simulated Annealing Algorithm and can obtain high-quality solutions within reasonable computing times.

Taihsi Wu - One of the best experts on this subject based on the ideXlab platform.

  • hybrid Simulated Annealing Algorithm with mutation operator to the cell formation problem with alternative process routings
    Expert Systems With Applications, 2009
    Co-Authors: Taihsi Wu, Shuhsing Chung, Chinchih Chang
    Abstract:

    In this study, a hybrid Simulated Annealing Algorithm with mutation operator is proposed to solve the manufacturing cell formation problem considering multiple process routings for parts, so that either the intercellular movements are minimized or the grouping efficacy is maximized, depending on the definition of the decision objective. The proposed Algorithm is designed mainly to explore solution regions efficiently and to expedite the solution search process. The performance of the proposed Algorithm is tested by a range of test problems, some of which are from the literature and some of which are generated within this study. The comparative study shows that the proposed Algorithm improves the best results found in the literature for 28.6% of the test problems and the percentages of improvement are even higher than 18% in several test instances.