INTRODUCTION - OPTIMISATION OF JSS USING GA

In continuation to the previous posts related to JSS Using GA

Many prevailing industrial production environments still rely on pure professional knowledge for production planning and scheduling purposes. Optimization in an algorithmic sense is mostly not performed at all. This may seem amazing at first glance since computation have invaded almost all fields of modern industries. In particular, systems such as Production, Planning and Control Systems serve as a support tool for production related management activities. Such systems are designed in a highly generic and versatile way in order to be applied in many different companies no matter what products they actually manufacture.

In general, the objectives of optimization may be different from company to company such as minimization of make-span, maximizing machine occupation or both. Therefore, the concept of Production, Planning and Control Systems is not applicable for the optimization of production planning and scheduling in most of the cases. Due to its increasing significance, the optimization of production planning and scheduling attracted the attention of academic research.

The Job Shop Scheduling Problem (JSSP) and similar scheduling problems are combinatorial optimization problems and commonly classified as NP-hard ordering problems which makes almost impossible to solve these problems exactly, even for relatively small problem instances. Exact methods exist, like the branch and bound method which are only of theoretical relevance due to their exponential runtime complexity.

In reality compute results close to the optimum but in a reasonable amount of time is highly enough rather optimality. In such a case heuristic methods such as Local Search, Tabu Search, Simulated annealing and Evolutionary Algorithms, especially Genetic Algorithms (GA), are dominating in the field of JSSP.

Among the three kinds of JSSP such as static, dynamic and non-deterministic^† ,In this project, the static JSSP is used to evaluate the sequence using GA. JSSP description in terms of GA is initial phase challenge and machine side scheduling also challenged at final phase using the result of job side sequence. This report briefly describes the GA and its parameter along with the JSS parameters.

References:

† C. Bierwirth and D. Mattfeld, “Production Scheduling and Rescheduling with Genetic Algorithms”, Evolutionary Computation Volume 7, Number 1: 1-17, Massachusetts Institute of Technology (1999), Germany.

Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING

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© 2006 Kumaravel & Project Team