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Scheduling with batch setup times and earliness-tardiness penalties
Institution:3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-8809, USA;1. Department of Physical Metallurgy and Materials Testing, Montanuniversität Leoben, Franz-Josef-Straße 18, Leoben A-8700, Austria;2. Materials Center Leoben Forschung GmbH, Roseggerstraße 12, Leoben A-8700, Austria;3. Department of Materials Physics, Montanuniversität Leoben, Jahnstrasse 12, Leoben A-8700, Austria;4. CERATIZIT Austria GmbH, Metallwerk-Plansee-Straße 71, Reutte A-6600, Austria;5. European Synchrotron Radiation Facility, Cedex 9, Grenoble F-38043, France;6. Department of Analytical Chemistry, Ghent University, Krijgslaan 281, S12, Ghent B-9000, Belgium;7. CERATIZIT Luxembourg S.àr.l., Route de Holzem, B.P.51, Mamer L-8201, Luxembourg
Abstract:We consider a scheduling model in which several batches of jobs need to be processed by a single machine. During processing, a setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. All the jobs in the same batch have a common due date that is either externally given as an input data or internally determined as a decision variable. Two problems are investigated. One problem is to minimize the total earliness and tardiness penalties provided that each due date is externally given. We show that this problem is NP-hard even when there are only two batches of jobs and the two due dates are unrestrictively large. The other problem is to minimize the total earliness and tardiness penalties plus the total due date penalty provided that each due date is a decision variable. We give some optimality properties for this problem with the general case and propose a polynomial dynamic programming algorithm for solving this problem with two batches of jobs. We also consider a special case for both of the problems when the common due dates for different batches are all equal. Under this special case, we give a dynamic programming algorithm for solving the first problem with an unrestrictively large due date and for solving the second problem. This algorithm has a running time polynomial in the number of jobs but exponential in the number of batches.
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