Abstract
This paper proposes a new and efficient hybrid heuristic scheme for solving job-shop scheduling problems (JSP). A new and efficient population initialization and local search concept, based on genetic algorithms, is introduced to search the solution space and to determine the global minimum solution to the JSP problem. Simulated results imply that the proposed novel JSP method (called the PLGA algorithm) outperforms several currently used approaches. This investigation also considers a real-life job-shop scheduling system design, which optimizes the performance of the job-shop scheduling system subject to a required service level. Simulation results demonstrate that the proposed method is very efficient and potentially useful in solving job-shop scheduling problems.
Highlights
Most scheduling problems are NP-hard; the time required to solve the problem optimally, increases exponentially with the size of the problem
These instances are widely utilized in literatures [8], [13], [27] and are available from an anonymous ftp site ftp://mscmga.ms.ic.ac.uk/pub/jobshop1.txt
The experiments were conducted on a Pentium IV -1.7G with 2G RAM using C++ running on windows 2000 operating system
Summary
Most scheduling problems are NP-hard; the time required to solve the problem optimally, increases exponentially with the size of the problem. The problem may be described as follows: n different jobs are to be scheduled on m different machines. Each job involves a set of operations, which are performed on the machines in a prespecified order.
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