Advanced dispatching rules for large-scale manufacturing systems
Toly Chen
0
1
Chandrasekharan Rajendran
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1
Chien-Wei Wu
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1
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C.-W. Wu Department of Industrial Engineering, National Tsing Hua University
, Hsinchu,
Taiwan
1
C. Rajendran Department of Management Studies, Indian Institute of Technology Madras
, Chennai,
India
2
) Department of Industrial Engineering and Systems Management, Feng Chia University
, Taichung,
Taiwan
Dispatching rules have been successfully applied to job sequencing and scheduling in large-scale manufacturing systems such as wafer fabrication plants, automatic guided vehicle systems, etc. Because they can be easily communicated and implemented, and because they can be speedily applied, dispatching rules are also one of the most prevalent approaches in this field. However, naysayers often criticize the sluggish performance levels of traditional dispatching rules. Furthermore, in many large-scale factories, scheduling systems have been installed and operational for more than 5 years with satisfactory results, but managers still believe that more beneficial modifications are possible. Specifically, better scheduling methods, dispatching rules, test environments, and reporting tools are needed. Over the years, a few new solutions have been proposed to address these issues. For instance, most traditional dispatching rules are based on historical data. With the emergence of data mining and online analytic processing, dispatching rules can now take predictive information into account. Further, rather than concentrating on a single performance measure, some dispatching rules are designed to optimize multiple objectives at the same time. Moreover, the content of a dispatching rule can be optimized for a largescale manufacturing system. In light of advanced computing
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systems, dispatching rules continue to be one of the most
promising technologies for practical applications. This
special issue focuses on innovative but practical dispatching
rules rather than complex algorithms. This type of
dispatching rule will continue to drive the mainstream of
practical applications in factories for the foreseeable future.
This special issue is intended to provide the details of
advanced dispatching rule development and applications of
those rules to job sequencing and scheduling in large-scale
manufacturing systems. We are very grateful for the positive
responses we have received from the authors who submitted
papers and the marvelous help provided by a number of
referees in the paper reviewing process. After a strict review,
25 papers were finally accepted for publication in this
special issue.
Zhang et al. used a genetic algorithm (GA) to optimize a
set of dispatching rules for scheduling a job shop. Bayesian
networks were also utilized to model the distribution of
high-quality solutions in the population and to produce each
new generation of individuals. In addition, some selected
individuals were further improved by a special local search.
One advantage of their method is that it can be readily
applied in various dynamic scheduling environments which
must be investigated with simulation.
Lu and Romanowski considered a dynamic job shop
problem in which job shops are disrupted by unforeseen
events such as job arrivals and machine breakdowns. They
used multi-contextual functions (MCFs) to describe the
unique characteristics of a dynamic job shop at a specific
time and examined 11 basic dispatching rules and 33
composite rules made with MCFs that describe machine idle
time and job waiting time. The experimental data showed
that schedules made by the composite rules outperformed
schedules made by conventional rules.
Lin et al. integrated an ant colony optimization (ACO)
algorithm with a number of new ideas (heuristic initial
solution, machine reselection step, and local search procedure) and
proposed a new apparent tardiness cost (ATCw) heuristic to
minimize the total weighted tardiness for unrelated parallel
machines. The computational results showed that the
proposed ACO algorithm outperformed other existing algorithms
in terms of total weighted tardiness.
To consider uncertainty and improve the scheduling of a
wafer fabrication factory, Chen proposed an innovative fuzzy
rule that solves the problem of slack overlapping in a
nonsubjective way. The fuzzy rule considers the uncertainty
in the remaining cycle time and is aimed at the simultaneous
optimization of the average cycle time and cycle time standard
deviation. Chen established a systematic procedure to
optimize the values of adjustable parameters in the fuzzy rule.
Kuo and Cheng solved a job shop scheduling problem
with due date time windows and release times, in order to
minimize the sum of earliness times and tardiness times.
They proposed a novel hybrid meta-heuristic which
combines ACO and particle swarm optimization (PSO), called
ACPSO. The computational results indicated that ACPSO
performs better than ACO and PSO.
Hung et al. dealt with the rescheduling of
photolithography in semiconductor (...truncated)