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Genetic Algorithms for Capital Goods Production Scheduling

This research explores using Genetic Algorithms for the complex scheduling of capital goods production, involving deep product structures and multiple resource constraints. It compares conventional optimization algorithms like Integer Linear Programming and Dynamic Programming with more recent approaches such as Simulated Annealing, Taboo Search, and Genetic Algorithms. The study highlights the stochastic search nature of Genetic Algorithms and their suitability for combinatorial optimization problems, although an optimal solution is not guaranteed due to combinatorial explosion. The comparison evaluates different evaluation criteria like Determination Coefficient, Mean Square Error, and more. The study concludes with findings that Genetic Algorithms can lead to lower penalty costs compared to other methods for scheduling complex products.

joan-nelson
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Genetic Algorithms for Capital Goods Production Scheduling

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  1. Using Genetic Algorithms for Scheduling the Production of Capital Goods P. Pongcharoen, C. Hicks, P.M. Braiden, A.V. Metcalfe, D.J. Stewardson University of Newcastle upon Tyne

  2. Scheduling • “The allocation of resources over time to perform a collection of tasks” (Baker 1974) • “Scheduling problems in their static and deterministic forms are extremely simple to describe and formulate, but are difficult to solve” (King and Spakis 1980)

  3. Scheduling Problems • Involve complex combinatorial optimisation • For n jobs on m machines there are potentially (n!)msequences, e.g. n=10 m=3 => 1.7 million sequences. • Most problems can only be solved by inefficient non-deterministic polynomial (NP) algorithms. • Even a computer can take large amounts of time to solve only moderately large problems

  4. Scheduling the Production of Capital Goods • Deep and complex product structures • Long routings with many types of machine and process • Multiple constraints such as assembly, precedence operation and resource constraints.

  5. Conventional Optimisation Algorithms • Integer Linear Programming • Dynamic Programming • Branch and Bound These methods rely on enumerative search and are therefore only suitable for small problems

  6. More Recent Approaches • Simulated Annealing • Taboo Search • Genetic Algorithms Characteristics • Stochastic search. • Suitable for combinatorial optimisation problems. • Due to combinatorial explosion, they may not search the whole problem space. Thus, an optimal solution is not guaranteed.

  7. Substance Melting substance Simulated Annealing Heating up Slowly cooling Too fast cooling Equilibrium state with resulting crystal Out of equilibrium state with resulting defecting crystal

  8. Taboo Search

  9. Evaluation criteria • Determination coefficient (R2) • Adjusted determination coefficient (Ra2) • Mean square error (MSE) • Mallow’s statistic (Cp)

  10. Conclusion (1) • BGA has been developed for the scheduling of complex products with deep product structure and multiple resource constraints. • Within a given execution time, large population (fewer generations) produced lower penalty costs and spread than small populations (many generations).

  11. Conclusion (2) • BGA produced lower penalty costs than corresponding plans produced by using simulation.

  12. Any questionsPlease

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