the single machine early/tardy problem* peng si ow & thomas e. morton ie 573 - paper...
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THE SİNGLE MACHİNE EARLY/TARDY PROBLEM*
PENG Sİ OW & THOMAS E. MORTON
IE 573 - Paper Presentation
A. İrfan Mahmutoğulları
*Ow, P. S., & Morton, T. E. (1989). The single machine early/tardy problem. Management Science, 35(2), 177-191.
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Introduction
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Introduction
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Introduction• Heuristics to obtain good solutions to the problem
• Dispatch method: Whenever a machine is free a priority function selects the next job• MRV (Morton, Rachamadugu and Vepsalainen 1984)• Earliest Due Date• LIN-ET• EXP-ET• Filtered Beam Search
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Background• Sidney (1977): minimizing maximum job penalty (early or
tardy)
• Lakshminarayan et al. (1978) later provided an O(n log n) algorithm for this problem.
• Seidmann et al. (1981) considered the problem of assigning individual job due dates and identifying a sequence so as to minimize weighted earliness, tardiness and lead times costs. All jobs had the same weights.
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Background• Search Techniques:
• Best-first search and depth-first search
• Barr and Feigenbaum (1981)• Lawler and Woods (1966) • Nilsson (1980)
• Baker (1974): Neighborhood search• Lowerre (1976): Beam search
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Analysis of the Early/Tardy Problem
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Analysis of the Early/Tardy Problem
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Analysis of the Early/Tardy Problem
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Analysis of the Early/Tardy Problem
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Analysis of the Early/Tardy Problem• Special Cases of the Early/Tardy Problem:
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Heuristics for the Early/Tardy Problem
• Tardiness Heuristics• Morton et al. (1984) on the weighted tardiness problem• A myopic heuristic that attempts to achieve local optimality• Job i immediately precedes job j when
• Pij(si) may be taken to be the priority of job i with respect to j at the earliest time the machine is free
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Heuristics for the Early/Tardy Problem
• A dispatch priority rule was derived by comparing each job's priority to an average job with processing time
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Heuristics for the Early/Tardy Problem
• However, local optimality is far away from global optimality due to «clashes» between multiple jobs.
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Heuristics for the Early/Tardy Problem
• This insight led to the addition of a look ahead parameter, k to the priority function. The resulting function is:
• Morton et al. (1984) experimented with other functions to find a better approximation
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Heuristics for the Early/Tardy Problem
• Linear vs. Exponential priority rules for tardiness problem:
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Heuristics for the Early/Tardy Problem
• Early/Tardy Heuristics• Following Morton et al. (1984)
• If (1) is divided by pipj
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Heuristics for the Early/Tardy Problem
• As in the weighted tardiness case, • A simple dispatch rule may be obtained by comparing each job's
priority to that of a job with average processing time and• A look ahead parameter may be used to attempt to extend the
scope of optimality beyond two adjacent jobs.
• Linear priority rule:
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Heuristics for the Early/Tardy Problem
• Exponential priority rule:
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Heuristics for the Early/Tardy Problem
• Linear vs. Exponential priority rules for early/tardy problem:
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• Choice of k:• k controls the time at which a job's priority begins to increase• Therefore, when job due dates are close together and the lead
times of jobs are not very long, a large look ahead k should be used
• A decision may then be made early enough to avoid the clash. In the case where due dates are evenly distributed, k should be small as few jobs will clash
Heuristics for the Early/Tardy Problem
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• Beam search methods• The goodness of each partial sequence is estimated using a
function known as an «evaluation function» and the «best» two sequences are selected
Heuristics for the Early/Tardy Problem
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• Evaluation Function• Priority search
Priority of last job added to the sequence is used
• Probe search
Schedule cost is estimated for each node
• Filtered beam search
Priority search + Probe search
Heuristics for the Early/Tardy Problem
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
Evaluated by Priority search
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
The best three are selected and Evaluated by Probe search
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
The best two are selected
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
1 2 4 5 1 2 3 5
Evaluated by Priority search
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
1 2 4 5 1 2 3 5
The best three are selected for each parent - Evaluated by Probe search
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Heuristics for the Early/Tardy Problem
1 2 3 4 5
Filter width (α) = 3 Beam width (β) = 2
1 2 4 5 1 2 3 5
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• Design of the experiment• Tardiness factor (coarse measure of the proportion of the jobs that
might be expected to be tardy in an arbitrary sequence)
• Due date range (controls the range of the due date distribution)
Computational Study
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• Processing times and due dates:• A bivariate Normal distribution was used for processing times, due
dates and the correlation between the processing times and due dates.
• Numbers drawn were rounded to the nearest integer.• Population mean for processing times was 15.• Coefficient of variation for the processing times, (std. dev./ mean),
was 0.2.• Due dates range factor, R, was set at 0.4 and 1.0.• Correlation coefficient between processing times and due dates, ρ,
was set at 0 and 0.5.• Tardiness Factor, was set at 0.2 and 0.6.
Computational Study
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• Tardy cost rate: • w/p ~ uniform [0,5]. • wi = (w/p) x pi.
• Early cost rate. • h / w was set at 25%, 10% and 5
• Number of jobs in each set of tests, n. 8, 15, and 25.
• Twenty test problems were generated for each combination of test parameter settings, giving a total of 1440 test problems.
Computational Study
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• A preliminary study of the performances of the three Beam Search methods discussed earlier was conducted using the 25-job problems with early-to-tardy cost rate ratio of 25%.
• The EXP-ET priority function was used for the priority evaluation and to perform the probe in the cost evaluation.
• Based on this study, Filtered beam search was determined to dominate the others in terms of search efficiency and solution quality.
Computational Study
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• Performance = (Cost of Heu. – OPT or LB cost) / OPT or LB cost
• Optimal solutions are obtained via Branch-and-Bound• 8 job and (some) 15 job instances
• LBs are obtained by breaking each jobs that can be solved as assignment problem• (some)15 job and 25 job instances
• Lower bounds were found quite tight
Computational Study
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• Effect of k parameter on EXP-ET
• When the due date range
was wide, larger look
aheads degraded
performance
• When the range was narrow
and tardiness factor was
high performance improved
as k increased
Computational Study
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Computational Study
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Computational Study
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Computational Study
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Computational Study
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