Browsing by Author "Schultz, S. R."
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Item Open Access Minimizing Lmax for the single machine scheduling problem with family set-ups(Taylor & Francis, 2004) Schultz, S. R.; Hodgson, T. J.; King, R. E.; Taner, M. R.A procedure for the single machine-scheduling problem of minimizing the maximum lateness for jobs with sequence independent set-ups is presented. The procedure provides optimal/near-optimal solutions over a wide range of problems. It performs well compared with other heuristics, and it is effective in finding solutions for large problems.Item Open Access Parallel dedicated machine scheduling with a single server: full precedence case(IIE, 2004) Schultz, S. R.; Taner, Mehmet RüştüThe motivation for this study was the observation of a practical scenario that involves scheduling of two parallel machines attended by a single setup crew so as to minimize the makespan. This problem is known in scheduling literature as the parallel machine scheduling problem with a single server, P2S1/ / C max. In order to gain insight on this problem, we analyzed a constrained version of it. In this constrained case, jobs are dedicated to each machine, and the processing sequence on each machine is given and fixed. The problem is thus referred to as the parallel, dedicated machine scheduling problem with a single server and full precedence, PD2S1/full prec./ C max. We explore the combinatoric structure of the problem, and develop a branch and bound procedure and five heuristic algorithms.Item Open Access Satisfying due-dates in the presence of sequence dependent family setups with a special comedown structure(Elsevier, 2007-12-22) Taner, M. R.; Hodgson, T. J.; King, R. E.; Schultz, S. R.This paper addresses a static, n-job, single-machine scheduling problem with sequence dependent family setups. The setup matrix follows a special structure where a constant setup is required only if a job from a smaller indexed family is an immediate successor of one from a larger indexed family. The objective is to minimize the maximum lateness (Lmax). A two-step neighborhood search procedure and an implicit enumeration scheme are proposed. Both procedures exploit the problem structure. The enumeration scheme produces optimum solutions to small and medium sized problems in reasonable computational times, yet it fails to perform efficiently in larger instances. Computational results show that the heuristic procedure is highly effective, and is efficient even for extremely large problems.