A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning effect

This paper addresses the hybrid flow shop batch scheduling problem with sequence- and machine-dependent family setup times where the objective is to simultaneously minimize the weighted sum of the total weighted completion time and total weighted tardiness, being mindful of the producer and customers, respectively. In order to reflect the industry requirements, machine availability times, job release times, machine capability and eligibility for processing jobs, stage skipping, and learning effect are considered. Unlike group scheduling, batch scheduling disregards the group technology assumptions by splitting pre-determined groups of jobs into inconsistent batches to perform timely processing of jobs with higher priority and utilize the maximum available capacity of the machines. One of the contributions of this research is to realize the benefits of integrating the batching decision into the group scheduling approach. Another contribution is to develop robust meta-heuristics based on hybridization of local search and population-based structures along with the stage-based interdependency strategy to solve the research problem. An initial solution finding mechanism and a comprehensive data generation mechanism are developed. The efficiency and effectiveness of the meta-heuristic algorithms are verified by lower bounds obtained by two mixed-integer linear programming models. The benefits of considering the batching decision with respect to desired lower bounds on batch sizes will hopefully encourage practitioners to apply the batch scheduling approach instead of the group scheduling approach.