Examinando por Autor "Muthuswamy, Shanthi"

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    Ítem
    Makespan minimization in a job shop with a BPM using simulated annealing
    (Springer London, 2013-03-08) Rojas Santiago, Miguel; Vélez Gallego, Mario César; Damodaran, Purushothaman; Muthuswamy, Shanthi; Rojas Santiago, Miguel; Vélez Gallego, Mario César; Damodaran, Purushothaman; Muthuswamy, Shanthi; Universidad EAFIT. Departamento de Ingeniería de Producción; marvelez@eafit.edu.co; Gestión de Producción y Logística
    A scheduling problem commonly observed in the metal working industry has been studied in this research effort -- A job shop equipped with one batch processing machine (BPM) and several unit-capacity machines has been considered. Given a set of jobs, their process routes,processing requirements, and size, the objective is to schedule the jobs such that the makespan is minimized -- The BPM can process a batch of jobs as long as its capacity is not exceeded -- The batch processing time is equal to the longest processing job in the batch -- If no batches were to be formed, the scheduling problem under study reduces to the classicaljob shop problem with makespan objective, which is known to be nondeterministic polynomial time-hard -- A network representation of the problem using disjunctive and conjunctive arcs, and a simulated annealing (SA) algorithm are proposed to solve the problem. The solution quality and run time of SA are compared with CPLEX, a commercial solver used to solve the mathematical formulation and with four dispatching rules -- Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using SA to solve large-scale problems
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    Ítem
    Minimizing makespan in a two-machine no-wait flow shop with batch processing machines
    (Springer-Verlag, 2012-11-01) Muthuswamy, Shanthi; Vélez Gallego, Mario César; Rojas Santiago, Miguel; Maya Toro, Jairo; Muthuswamy, Shanthi; Vélez Gallego, Mario César; Rojas Santiago, Miguel; Maya Toro, Jairo; Universidad EAFIT. Grupo de Investigación Gestión de Producción y Logística; Universidad EAFIT. Departamento de Ingeniería de Producción; smuthuswamy@niu.edu; marvelez@eafit.edu.co; jmaya@eafit.edu.co; miguelrojas@uninorte.edu.co; Gestión de Producción y Logística
    Given a set of jobs and two batch processing machines (BPMs) arranged in a flow shop environment,the objective is to batch the jobs and sequence the batches such that the makespan is minimized. The job sizes, ready times, and processing times on the two BPMs are knowN -- The batch processing machines can process a batch of jobs as long as the total size of all the jobs assigned to a batch does not exceed its capacity -- Once the jobs are batched, the processing time of the batch on the first machine is equal to the longest processing job in the batch; processing time of the batch on the second machine is equal to the sum of processing times of all the jobs in the batch -- The batches cannot wait between two machines (i.e., no-wait) -- The problem under study is NP-hard -- We propose a mathematical formulation and present a particle swarm optimization (PSO) algorithm -- The solution quality and run time of PSO is compared with a commercial solver used to solve the mathematical formulation Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using PSO to solve large-scale problems