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Abstract: Job
shop scheduling problem in green manufacturing not only aims to
minimize the makespan and process cost, but also to decrease consumption
of resource and negative effects on the environment. A Perti net model
whose transitions are endowed with process time, process cost, resource
consumption and negative effects on the environment is developed. The
conflicts, which are caused by jobs sharing the same machine places in
the model, are eliminated by arranging machining order for different
jobs, and then a marked graph that stands for a feasible scheduling is
derived. Three methods are proposed to produce a marked graph of
feasible scheduling from another. A hybrid multi-objective heuristic
which ingrates Mu-rata’s Multi-objective genetic algorithm and Czyzak’s
Pareto simulated annealing algorithm is used to optimize the feasible
scheduling. Numerical simulation demonstrates that the proposed
heuristic is feasible and effective.
Key words: Green
manufacturing Job shop scheduling Petri net Multi-objective genetic
algorithm Pareto simulated annealing algorithm
CLC No: TN43
国家自然科学基金(50275078)和山东省自然科学基金(2004ZX14)资助项目.
Received 20050914, received in revised form 20060328
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