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Experiences with FineGrained Parallel Genetic Algorithms
 Annals of Operations Research
, 1996
"... this paper we present some results of our systematic studies of finegrained parallel versions of the island model of genetic algorithms and of variants of the neighborhood model (also called diffusion model) on the massively parallel computer MasPar MP1 with 16k processing elements. These parallel ..."
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Cited by 29 (2 self)
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this paper we present some results of our systematic studies of finegrained parallel versions of the island model of genetic algorithms and of variants of the neighborhood model (also called diffusion model) on the massively parallel computer MasPar MP1 with 16k processing elements. These parallel genetic algorithms have been applied to a range of different problems (e.g. traveling salesperson, capacitated lot sizing, ressource constrained project scheduling, flow shop, and warehouse location problems) in order to obtain an empirical basis for statements on their optimization quality.
A Genetic Algorithm for Multilevel, Multimachine Lot Sizing and Scheduling,
 Computers & Operations Research,
, 1999
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A FixandOptimize Approach for the MultiLevel Capacitated Lot Sizing Problem
, 2008
"... This paper presents an optimizationbased solution approach for the dynamic multilevel capacitated lot sizing problem (MLCLSP) with positive lead times. The key idea is to solve a series of mixedinteger programs in an iterative fixandoptimize algorithm. Each of these programs is optimized over ..."
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Cited by 4 (2 self)
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This paper presents an optimizationbased solution approach for the dynamic multilevel capacitated lot sizing problem (MLCLSP) with positive lead times. The key idea is to solve a series of mixedinteger programs in an iterative fixandoptimize algorithm. Each of these programs is optimized over all realvalued variables, but only a small subset of binary setup variables. The remaining binary setup variables are tentatively fixed to values determined in previous iterations. The resulting algorithm is transparent, flexible, accurate and relatively fast. Its solution quality outperforms those of the approaches by Tempelmeier/Derstroff and by Stadtler. 1
Improved Lower Bounds for the Proportional Lot Sizing and Scheduling Problem
, 1996
"... Where standard MIPsolvers fail to compute optimum objective function values for certain MIPmodel formulations, lower bounds may be used as a point of reference for evaluating heuristics. In this paper, we compute lower bounds for the multilevel proportional lot sizing and scheduling problem wi ..."
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Where standard MIPsolvers fail to compute optimum objective function values for certain MIPmodel formulations, lower bounds may be used as a point of reference for evaluating heuristics. In this paper, we compute lower bounds for the multilevel proportional lot sizing and scheduling problem with multiple machines (PLSPMM). Four approaches are compared: Solving LPrelaxations of two different model formulations, solving a relaxed MIPmodel formulation optimally, and solving a Lagrangean relaxation. Keywords: Multilevel lot sizing, scheduling, lower bounds, PLSP 1 Introduction The problem we are focussing at, can be described as follows: Several items are to be produced in order to meet some known (or estimated) dynamic demand without backlogs and stockouts. Precedence relations among these items define an acyclic gozintostructure of the general type. In contrast to many authors who allow demand for end items only, now, demand may occur for all items including component ...