New Optimization Approach for Scheduling the Batch Heat Treatment Process with Sequence Dependent Setup Times

Ehab A. Abdelhafiez and Tawfeeq Alkanhal


Shaking Optimization Algorithm (SOA), Evolutionary Computations, Job Shop Scheduling problem, Batch Annealing Process


The Shaking Optimization Algorithm is among the newly developed optimization algorithms that belong to the Evolutionary Computations Algorithms. It combines heuristics with randomness in the evolution process rather than just apply operators e.g., mutation, recombination, and/or selection rules. It has been used successfully to solve the classic Job Shop Scheduling Problem, the Cutting Stock Problem, and the Unconstrained Engineering Optimization Problems, and the results show that the algorithm is a good competitive to other evolutionary computation algorithms e.g., the Genetic Algorithm and the Particle Swarm Optimization regarding solution time, efficiency, and complicity. This paper uses the SOA algorithm to solve the scheduling problem of the Batch Annealing Process with Sequence-Dependent Setup Times in the heat treatment of steel coils as one of complex scheduling problems. The objective is to determine the movement schedule of shared equipment among parallel bases that gives minimum makespan for processing a given batch size. The bell-type batch annealing process is used here for the comparative study. The results show that the proposed approach is able to solve this class of problems as well in a reasonable time and gives competitive results to other algorithms.

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