A solution framework for scheduling a BPM with non-identical job dimensions

A Batch Processing Machine (BPM) is one which processes a number of jobs simultaneously as a batch with common beginning and ending times. Also, a BPM, once started cannot be interrupted in between (Pre-emption not allowed). This research is motivated by a BPM in steel casting industry. There are three main stages in any steel casting industry viz., pre-casting stage, casting stage and post-casting stage. A quick overview of the entire process, is shown in Figure 1. There are two BPMs : (1) Melting furnace in the pre-casting stage and (2) Heat Treatment Furnace (HTF) in the post casting stage of steel casting manufacturing process. This study focuses on scheduling the latter, namely HTF. Heat-treatment operation is one of the most important stages of steel casting industries. It determines the final properties that enable components to perform under demanding service conditions such as large mechanical load, high temperature and anti-corrosive processing. In general, different types of castings have to undergo more than one type of heat-treatment operations, where the total heat-treatment processing times change. To have a better control, castings are primarily classified into a number of job-families based on the alloy type such as low-alloy castings and high alloy castings. For technical reasons such as type of alloy, temperature level and the expected combination of heat-treatment operations, the castings from different families can not be processed together in the same batch.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.