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Abstract
In this study, a batch scheduling with job grouping and batch sequencing is considered. A clustering algorithm and dispatching rule selection model is developed to minimize total tardiness. The model and algorithm are based on the constrained k-means algorithm and neural network. We also develop a method to generate a training dataset from historical data to train the neural network. We use numerical examples to demonstrate that the proposed algorithm and model efficiently and effectively solve batch scheduling problems.
Highlights
The aim of this study involves developing a framework that schedules batch jobs with identical machines to minimize total tardiness
We design a dispatching rule selection model based on neural networks for batch sequencing
We suggest that the proposed framework determines an optimal schedule for small problems and a better schedule than those obtained via a single dispatching rule for large problems
Summary
The aim of this study involves developing a framework that schedules batch jobs with identical machines to minimize total tardiness. Heuristic and meta-heuristic approaches are usually dependent on the types of problems that are considered [1,2] Since they are designed for a specific problem, they may yield relatively poor results when applied to different problems, or a substantial modification to the model is necessary before application. The dispatching rule approach is suitable for the aforementioned goals because it is relatively robust in terms of problem types, and can be applied to various problems without substantial modification to yield a schedule quickly. There developing a dispatching rule selection model for for the batch scheduling problem. We design a dispatching rule selection model based on neural networks for batch. We design a dispatching rule selection model based on neural networks for batch sequencing.
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