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Fuzzy Scheduling

Fuzzy Scheduling. Contents 1. Introduction to Fuzzy Sets 2. Application of Fuzzy Sets to Scheduling Problems 3. A Genetic Algorithm for Fuzzy Flowshop Scheduling Problem. Literature 1. Fuzzy sets, uncertainty, and information , G. J. Klir and T.A. Folger.

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Fuzzy Scheduling

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  1. Fuzzy Scheduling Contents 1. Introduction to Fuzzy Sets 2. Application of Fuzzy Sets to Scheduling Problems 3. A Genetic Algorithm for Fuzzy Flowshop Scheduling Problem

  2. Literature 1. Fuzzy sets, uncertainty, and information, G. J. Klir and T.A. Folger. Englewood Cliffs, N.J : Prentice Hall, 1988. 2."Flowshop Scheduling with Fuzzy Duedate and Fuzzy Processing Time", by H. Ishibuchi and T. Nurata, in: Scheduling Under Fuzziness, R. Slowinski, and M. Hapke, (eds), Physica-Verlag, A Springer-Verlag Company, 2000, pages 113-143 3. "Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems", Ishibuchi, H., Yamamoto, N., Murata, T., Tanaka., H, Fuzzy Sets and Systems Vol. 67, No.1, 1994, pages 81-100.

  3. Introduction to Fuzzy Sets crisp versus fuzzy • dichotomous yes-or-no-type • more-or-less type • conventional logic: statement is true • "classical" set theory: an element belongs to a set or not • optimisation: a solution is feasible or not • Basic foundations of fuzzy sets: Lotfi Zadeh, Fuzzy Sets, Information and Control, Vol. 8, 1965

  4. Classical set: x  A or x  A • Example. • A - set of even natural numbers less than 10 • A = {2, 4, 6, 8} • A(x) degree of membership of x in A  A(1) = 0  A(2) = 1 ... or • Fuzzy sets : takes on values in the range [0, ], is usually 1

  5. = "real number close to 10" = ”integers close to 10” = { (7, 0.1), (8, 0.5), (9, 0.8), (10, 1), (11, 0.8), (12, 0.5), (13, 0.1) Examples. 1.0 0 x 5 10 15

  6. Fuzzy sets and probability measure two bottles of liquid first bottlehas membership 0.91in the fuzzy setof all drinkable liquids second bottleprobability that it containsdrinkable liquids is 0.91 Which bottle would you choose?

  7. Application of Fuzzy Sets to Scheduling Problems • 1. Approximate reasoning • Approximate Reasoning is the process by which a possible imprecise conclusion is deduced from a collection of imprecise premises. • Fuzzy IF-THEN Production Rules • 2. Fuzzy constraints • How to calculate satisfaction of the flexible constraints? • 3. Fuzzy parameters • Most often imprecise or incomplete are: • fuzzy due dates • fuzzy processing times

  8. Approximate Reasoning Example of a rule-based system for dispatching Release job j next Time characteristics Machine utilisation External priority Slack time Waiting time

  9. Waiting time: {long, medium, short} Slack time: {critically short, short, sufficient} Time characteristics: {urgent, not urgent} IF Waiting time is long AND Slack time is critically short THEN Time characteristics is urgent IF Waiting time is medium AND Slack time is critically short THEN Time characteristics is urgent IF Waiting time is short AND Slack time is critically short THEN Time characteristics is urgent IF Waiting time is medium AND Slack time is short THEN Time characteristics is not urgent ... j(T) criticallyshort short sufficient 1.0 0 T 3 4.5 6 8

  10. Fuzzy Rules + Observed System State Fuzzy Advice Waiting time is A - crisp Slack time is B - crisp Time criterion is C - fuzzy DEFUZZIFICATION

  11. Fuzzy Constraints C(x) satisfaction grade of the solution x crisp constraint fuzzy constraint

  12. Fuzzy Due Date dj duedate of job j Cj completion time of job j satisfaction grade j(Cj) 1.0 0 dj Cj

  13. j(Cj) 1.0 0 Cj djL djU j(Cj) 1.0 0 Cj ejL ejU djL djU

  14. A Genetic Algorithm for Fuzzy Flowshop Scheduling Problems Problem Statement flow shop scheduling n jobs which have fuzzy duedates m machines sequence of n jobs is represented by a vectorx=(x1, x2, ... ,xn) pi,j - processing time of job j on machine i Ci,j - completion time of job j on machine i C1,x1 = p1,x1 Ci,x1 = Ci-1,x1 + pi,x1 for machines i=2,3,...,m C1,xk = C1,xk-1 + p1,xk for jobsk=2,3,...,n Ci,xk= max {Ci-1,xk , Ci,xk-1 } + pi,xk for machines i=2,3,...,m and for jobs k=2,3,...,n

  15. Objective 1: Maximisation of minimum satisfaction grade Maximise fmin= min{j(Cj) : j=1,2,...,n} Objective 2: Maximisation of total satisfaction grade

  16. total number of possible solutions: n! • optimal solution can be found only for small-size problems • for large-size problems search techniques could be used • Genetic algorithm • Notation • N number of individuals in each population • xpii-th individual in the p-th population • p = {xpi : i=1,2, ... , N} individuals in the p-th population • Characteristics of the algorithm • selection based on the roulette wheel • two-point order crossover • shift mutation • elite strategy

  17. Step 1. Initialisation Randomly generate N individuals to construct an initial population 1 Step 2. Selection Select N-1 individuals from the current population usingthe roulette wheel selection method with linear scaling objective functionof the individual minimum objective functionof the whole population   Selection probability g(x) objective function fmin or fsum gmin(p ) = min { g(xpj ) : xpj p}

  18. Step 3. Crossover • Two-point order crossover is applied to each of the selected pairs with a pre-specified crossover probability. • When the crossover operator is not applied one parent becomes an offspring.

  19. Step 4. Mutation • Applied to each of the individuals generated by the crossover with a pre-specified mutation probability.

  20. Step 5. Elitist strategy • Add the best individual from the previous population to the newN-1 individuals generated by the crossover and mutation. • Step 6. Termination test • If a pre-specified stopping condition is satisfied stop.

  21. The value of the Objective function 1 (maximisation of the minimum satisfaction grade) is 0 if the satisfaction grade of at least one job is 0. j(Cj) 1.0  is a positive constant 0 Cj ejL ejU djL djU Objective 1’: Maximisation of minimum satisfaction grade Maximise f’min= min{j(Cj) : j=1,2,...,n}

  22. Summary • Fuzzy sets and logic can be successfully used to treat various types of uncertainty that exist in scheduling problems. • Fuzzy duedate and fuzzy processing time are typical examples of such uncertainty. • Many conventional scheduling problems could be reformulated as fuzzy scheduling problems.

  23. Experiments • Population size N = 50 • Crossover probability = 0.9 • Mutation probability = 0.3 • Stopping condition = 2000 generations • Number of machines m = 10 • 100 problems with n = 10 jobs • 100 problems with n = 20 jobs • 100 problems with n = 50 jobs • Processing time pi,j is a random integer from the interval [1,99]

  24. j(Cj) 1.0 0 Cj djL djU • Each job has a fuzzy duedate • How to generate djL and djU ? • 1. Randomly generate a sequence x=(x1, x2, ... ,xn), n=10, 20, 50 • for each test problem • 2. Calculate the completion time of each job Cj(x) • 3. Calculate for each job j • djL = Cj(x) - djU = Cj(x) +  •  is a random integer from the interval [100, 200]

  25. j(Cj) 1.0 0 Cj djL djU j(Cj)=0.5 if the jobs are processed in the order x x is the optimal solution for Objective 1(maximisation of the minimum satisfaction grade)

  26. The value of the Objective function 1 (maximisation of the minimum satisfaction grade) is 0 if the satisfaction grade of at least one job is 0.

  27. j(Cj) 1.0 0 Cj ejL ejU djL djU  is a positive constant Objective 1’: Maximisation of minimum satisfaction grade Maximise f’min= min{j(Cj) : j=1,2,...,n}

  28. Heuristic method for initial solution: • Process the given jobs in an increasing order of their duedates. • With fuzzy duedates we can use values of djU to order the jobs • Initialisation: one heuristic schedule and N-1 randomly generated • Genetic algorithm can effectively utilise heuristic initial solutions!

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