184 research outputs found
A survey of scheduling problems with setup times or costs
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Two-machine flowshop batching and scheduling
Author name used in this publication: T. C. E. Cheng2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Scheduling in an assembly-type production chain with batch transfer
Author name used in this publication: T. C. E. Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Native metaheuristics for non-permutation flowshop scheduling
The most general flowshop scheduling problem is also addressed in the literature as non-permutation flowshop
(NPFS). Current processors are able to cope with the combinatorial complexity of (n!)exp m. NPFS scheduling by
metaheuristics. After briefly discussing the requirements for a manufacturing layout to be designed and
modeled as non-permutation flowshop, a disjunctive graph (digraph) approach is used to build native
solutions. The implementation of an Ant Colony Optimization (ACO) algorithm has been described in detail;
it has been shown how the biologically inspired mechanisms produce eligible schedules, as opposed to most
metaheuristics approaches, which improve permutation solutions. ACO algorithms are an example of native
non-permutation (NNP) solutions of the flowshop scheduling problem, opening a new perspective on building
purely native approaches. The proposed NNP-ACO has been assessed over existing native approaches
improving most makespan upper bounds of the benchmark problems from Demirkol et al. (1998)
Parameterized complexity of machine scheduling: 15 open problems
Machine scheduling problems are a long-time key domain of algorithms and
complexity research. A novel approach to machine scheduling problems are
fixed-parameter algorithms. To stimulate this thriving research direction, we
propose 15 open questions in this area whose resolution we expect to lead to
the discovery of new approaches and techniques both in scheduling and
parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc
BALANCING TRADE-OFFS IN ONE-STAGE PRODUCTION WITH PROCESSING TIME UNCERTAINTY
Stochastic production scheduling faces three challenges, first the inconsistencies among key performance indicators (KPIs), second the trade-offs between the expected return and the risk for a portfolio of KPIs, and third the uncertainty in processing times. Based on two inconsistent KPIs of total completion time (TCT) and variance of completion times (VCT), we propose our trade-off balancing (ToB) heuristic for one-stage production scheduling. Through comprehensive case studies, we show that our ToB heuristic with preference =0.0:0.1:1.0 efficiently and effectively addresses the three challenges. Moreover, our trade-off balancing scheme can be generalized to balance a number of inconsistent KPIs more than two. Daniels and Kouvelis (DK) proposed a scheme to optimize the worst-case scenario for stochastic production scheduling and proposed the endpoint product (EP) and endpoint sum (ES) heuristics to hedge against processing time uncertainty. Using 5 levels of coefficients of variation (CVs) to represent processing time uncertainty, we show that our ToB heuristic is robust as well, and even outperforms the EP and ES heuristics on worst-case scenarios at high levels of processing time uncertainty. Moreover, our ToB heuristic generates undominated solution spaces of KPIs, which not only provides a solid base to set up specification limits for statistical process control (SPC) but also facilitates the application of modern portfolio theory and SPC techniques in the industry
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