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Transcript of Application of Nature Inspired Genetic Algorithms For Job Shop Scheduling Bhuvan Sharma Research...
![Page 1: Application of Nature Inspired Genetic Algorithms For Job Shop Scheduling Bhuvan Sharma Research Associate Advanced Computation in Design and Decision.](https://reader030.fdocuments.us/reader030/viewer/2022032516/56649c7b5503460f9492e76e/html5/thumbnails/1.jpg)
Application of Nature Inspired Genetic Algorithms For
Job Shop Scheduling
Bhuvan Sharma
Research AssociateAdvanced Computation in Design and Decision Making Group
University West of England, Bristol, [email protected]
School of Computer Science
University of Birmingham
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Aim of Presentation
Understanding job shop scheduling
Why Nature Inspired Algorithms
Issues in GA, when applied to job shop problems
Review of various approaches within GA
Practical Problem from Rolls Royce
My approach
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Job Shop Scheduling
Job : A piece of work that goes through series of
operations.
Shop : A place for manufacturing or repairing of
goods or machinery.
Scheduling : Decision process aiming to deduce the
order of processing.
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A typical Job Shop Problem
Objectives Minimization of make span
Minimization of cost
Minimization of delays
Parameters Number of jobs
Number of operations within each job
Processing time of each operation within each job
Machining sequence of operations within each job
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A 3 job 3 machine problem
J1 M1 M2 M3
J2 M1 M3 M2
J3 M3 M2 M1
Machining Sequence
Jobs Operations
Processing time
Jobs Operations
J1 6 5 3
J2 4 3 4
J3 10 5 2
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Why Nature Inspired Algorithms
• Evaluation on a set of points. Better search.
• Better chances for global optimal solution.
• Suitable for Multi-objective optimisation.
• Flexibility, because constraints can be taken care of.
• Evaluation on a point each time.
• Often terminate into local optima.
• Not suited for multi-objective optimisation.
• Not flexible, driven by heuristics, constraints not handled easily.
GA’s vs Other methods
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Issues in Genetic Algorithms when applied to job shop problems
Representation of schedule(phenotype) by suitable genotype.
Conversion of genotype to phenotype
Choice of Schedule Builder
Type of Crossover and Mutation to be used
Avoiding Premature convergence.
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Schedule Builder!! What’s That
J1 M1 M2 M3
J2 M1 M3 M2
J3 M3 M2 M1
J1 6 5 3
J2 4 3 4
J3 10 5 2
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Representation schemes for schedules in job shop
Conventional Binary representation
Job Based Representation
Permutation Representation (Partitioned)
Permutation Representation (Repetitive)
Priority Rule Based Representation (Random /
guided)
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1. Binary Representation
Genotype is binary matrix of
Rows = Number of job pairs
Columns = Number of machines
Interpretation
Mij = 0 / 1 depending on whether job1
is executed later or prior to job2.
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Job 1 M1 M3 M2
Job 2 M2 M3 M1
Job 3 M2 M1 M3
Job1 & 2 1 1 0
Job1 & 3 1 1 0
Job2 & 3 0 1 0
(b) Binary Representation
(a) Machine Sequence
M/c 1 J1 J3 J2
M/c 2 J3 J2 J1
M/c 3 J1 J2 J3
(c) Symbolic Representation
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Crossover
The crossover applied is simple one point
crossover.
Demerits
Redundancy in representation.
2mj(j-1)/2 bits are required for (!j)m schedules.
Forcing techniques required for replacement of
illegal schedules.
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2. Job Based Representation Typical chromosome [ Ji Jj Jk ]
For [ J2 J1 J3 ]
All operations of job 2 folllowed by 1 and then by 3 are scheduled in the available processing times.
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Demerits
Approach is very constrained
Not many possibilities are explored
Merits
Scheduling is very easy
Always yields a feasible schedule, hence forcing
not required.
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3. Permutation Representation (Partitioned)
Chromosome is set of permutation of jobs on
each machine. M1 M2 M3
1 3 2 3 2 1 3 1 2
Cross Over (SXX) Subsequence Exchange Crossover
Searches for exchangeable subsequence pairs
in parents, and swaps them.
Job sequence matrix for 3 X 3 problem
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Subsequence Exchange Crossover
M1 M2 M3
P0 1 2 3 6 4 5 3 2 1 5 6 4 2 3 5 6 1 4
P1 6 2 1 3 4 5 3 2 6 4 5 1 6 3 5 4 2 1
C0 2 1 3 4 6 5 3 2 5 1 6 4 2 6 3 5 1 4
C1 6 1 2 3 4 5 3 2 6 4 1 5 3 5 6 4 2 1
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Merits
Demerits
GA operators used for TSP can be applied here
Simple representation
Does not always give active schedules
Robust Schedule builder is required
SXX does not always guarantee a crossover
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4. Permutation Representation (Repetitive) Also known as operation based representation
Typical genotype is a unpartitioned permutation
with m repetitions for each job.
1 2 2 3 1 3 2 1 3
M1 1 3 2
M2 2 3 1
M3 2 1 3
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Crossover (PPX)
Precedence Preservation Crossover (PPX)
The offspring inherits partial characteristic
of both parents
P0 3 2 2 3 1 1 3 1 2
0 1 1 1 0 0 1 0 1
P1 2 3 3 2 1 3 1 1 2
C1 3 2 3 2 1 1 3 1 2
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Merits Very simple representation
All decoding leads to active schedules
Schedule building is straightforward
Crossover results in passing of characteristics from
both parents in most cases.
Demerits
Problem of Premature convergence
This is often case with long chromosomes
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5. Priority(Random/Guided)Rule Based Representation
Characteristics Use of GT Algorithm, with one of priority rule
used in ith iteration to select ith operation
Priority rules could be assigned randomly, or
guided by heuristics.
Representation [ SPT, LPT, MTPT, LTPT, MLFT, ….. ]
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Crossover
Both PPX and SXX can be used
Merits
Always give feasible active schedules
Incorporates heuristics to an extent
Demerits Problem of fast, premature convergence of
first few genes in the chromosome
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Practical Problem from Rolls Royce
Parameters 17 batches of jobs
10 operations per job
4 identical machines, each can perform any
operation subject to tool set
Constraints Only one tool-set for each operation
Opn. 2 must not begin until opn 1 is complete
Opn 3-9 can be performed in any sequence
Opn 10 should be the last for each batch of job
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Basic Scenario
Operation Time (min)
1 120
2 288
Leave Shop 24hrs
3 180
4 90
5 288
6 120
7 60
8 60
9 90
Leave Shop 24 hrs
10 60
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My Approach
Representation
Permutation based
The catch here is that it is permutation of machines
not jobs
For eg. [ 3 5 9 6 : 7 4 8 | 10 ]
Crossover Precedence Preservation Crossover (PPX)
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Start with heuristics
Schedule builder
Identify conflicting set of jobs
Selection of one from conflicting set based on
one of heuristic priority rules
Change toolset for machine as it finishes requisite
jobs. Change is guided by time factor.
Select a set of jobs out of 17 to process first. (random)
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