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...aling with large combinatorial optimisation problems (Nagar et al., 1995). Genetic algorithms (GA) are stochastic search techniques for approximating ÔoptimalÕ solutions within complex search spaces (=-=Goldberg, 1989-=-; Gen and Cheng, 1997). The technique is based upon the theory of evolution, in which the fitness of an individual determines its ability to survive and reproduce. Many authors including Croce et al. ...

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...facturing processes. In this research, the general procedure for GA (Goldberg, 1989) was modified. The modified GAsprogram was written approximately 4000 lines of code in Tcl/Tk programming language (=-=Ousterhout, 1994-=-). The algorithm is illustrated in Fig. 2. A repair process was added in order to rectify infeasible schedules that can be produced by genetic operations. 3.1. Problem encoding P. Pongcharoen et al. /...

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...; Complex products 1. Introduction European Journal of Operational Research 152 (2004) 215–225 Scheduling may be defined as ‘‘the allocation of resources over time to perform a collection of tasks’’ (=-=Baker, 1974-=-). Scheduling problems in their simple static and deterministic forms are extremely simple to describe and formulate, but are difficult to solve because they involve complex combina* Corresponding aut...

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...ur in the literature (Jain and Meeran, 1999). Algorithms may be classified into two types: conventional optimisation algorithms, such as integer linear programming (Manne, 1960), dynamic programming (=-=Held and Karp, 1962-=-) and branch and bound (Greenberg, 1968); or approximation optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (Nowicki and Smutnicki, 1996 and Ben-Daya and Al-Fawzan, ...

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... (Manne, 1960), dynamic programming (Held and Karp, 1962) and branch and bound (Greenberg, 1968); or approximation optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (=-=Nowicki and Smutnicki, 1996-=- and Ben-Daya and Al-Fawzan, 1998) and genetic algorithms (Tsujimura et al., 1997; Todd, 1997). The approximation optimisation algorithms tend to be most suitable for dealing with large combinatorial ...

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... as integer linear programming (Manne, 1960), dynamic programming (Held and Karp, 1962) and branch and bound (Greenberg, 1968); or approximation optimisation algorithms including simulated annealing (=-=Kolonko, 1999-=-), taboo search (Nowicki and Smutnicki, 1996 and Ben-Daya and Al-Fawzan, 1998) and genetic algorithms (Tsujimura et al., 1997; Todd, 1997). The approximation optimisation algorithms tend to be most su...

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... scheduling problems has found favour in the literature (Jain and Meeran, 1999). Algorithms may be classified into two types: conventional optimisation algorithms, such as integer linear programming (=-=Manne, 1960-=-), dynamic programming (Held and Karp, 1962) and branch and bound (Greenberg, 1968); or approximation optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (Nowicki and S...

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...wzan, 1998) and genetic algorithms (Tsujimura et al., 1997; Todd, 1997). The approximation optimisation algorithms tend to be most suitable for dealing with large combinatorial optimisation problems (=-=Nagar et al., 1995-=-). Genetic algorithms (GA) are stochastic search techniques for approximating ÔoptimalÕ solutions within complex search spaces (Goldberg, 1989; Gen and Cheng, 1997). The technique is based upon the th...

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...tion optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (Nowicki and Smutnicki, 1996 and Ben-Daya and Al-Fawzan, 1998) and genetic algorithms (Tsujimura et al., 1997; =-=Todd, 1997-=-). The approximation optimisation algorithms tend to be most suitable for dealing with large combinatorial optimisation problems (Nagar et al., 1995). Genetic algorithms (GA) are stochastic search tec...

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...ing (Held and Karp, 1962) and branch and bound (Greenberg, 1968); or approximation optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (Nowicki and Smutnicki, 1996 and =-=Ben-Daya and Al-Fawzan, 1998-=-) and genetic algorithms (Tsujimura et al., 1997; Todd, 1997). The approximation optimisation algorithms tend to be most suitable for dealing with large combinatorial optimisation problems (Nagar et a...

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...). Algorithms may be classified into two types: conventional optimisation algorithms, such as integer linear programming (Manne, 1960), dynamic programming (Held and Karp, 1962) and branch and bound (=-=Greenberg, 1968-=-); or approximation optimisation algorithms including simulated annealing (Kolonko, 1999), taboo search (Nowicki and Smutnicki, 1996 and Ben-Daya and Al-Fawzan, 1998) and genetic algorithms (Tsujimura...

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...nce relationships between operation and assembly sequences. 3. Genetic algorithms in production scheduling The selection of algorithms to solve scheduling problems has found favour in the literature (=-=Jain and Meeran, 1999-=-). Algorithms may be classified into two types: conventional optimisation algorithms, such as integer linear programming (Manne, 1960), dynamic programming (Held and Karp, 1962) and branch and bound (...