In most practical environments, scheduling is an ongoing reactive process where the presence of realtime
information continually forces reconsideration and revision of pre-established schedules. The objectives
of the research reported in this paper are to respond to changes in the problem, to solve the new problem
faster and to use some parts ofthe previous solution for the next problem. In this paper, based on Network
Simplex Algorithm, a dynamic algorithm, which is called Dynamic Network Simplex Algorithm (DNSA), is
presented. Although the traditional network simplex algorithm is at least one hundred times faster than
traditional simplex algorithm for Linear Programs (through specialization), for dynamic scheduling with
large scale problems it still takes time to make a new graph model and to solve it. The overall approach of
DNSA is to update the graph model dynamically and repair its spanning tree by some strategies when any
changes happen. To test the algorithm and its performance, an application of this algorithm to Dynamic
Scheduling of Automated Guided Vehicles in the container terminal is used. The dynamic problem arises
when new jobs are arrived, the fulfilled jobs are removed and the links or junctions are blocked (which
results in distances between points being changed). The results show considerable improvements, in
terms of reducing the number of iterations and CPU time, to solve randomly generated problems
In most practical environments, scheduling is an ongoing reactive process where the presence of realtimeinformation continually forces reconsideration and revision of pre-established schedules. The objectivesof the research reported in this paper are to respond to changes in the problem, to solve the new problemfaster and to use some parts ofthe previous solution for the next problem. In this paper, based on NetworkSimplex Algorithm, a dynamic algorithm, which is called Dynamic Network Simplex Algorithm (DNSA), ispresented. Although the traditional network simplex algorithm is at least one hundred times faster thantraditional simplex algorithm for Linear Programs (through specialization), for dynamic scheduling withlarge scale problems it still takes time to make a new graph model and to solve it. The overall approach ofDNSA is to update the graph model dynamically and repair its spanning tree by some strategies when anychanges happen. To test the algorithm and its performance, an application of this algorithm to DynamicScheduling of Automated Guided Vehicles in the container terminal is used. The dynamic problem ariseswhen new jobs are arrived, the fulfilled jobs are removed and the links or junctions are blocked (whichresults in distances between points being changed). The results show considerable improvements, interms of reducing the number of iterations and CPU time, to solve randomly generated problems
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