Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered; however, the flexible jobshop and parallel batch processing machine scheduling problem (FJPBPMSP) is prevalent in real-life manufacturing processes and is seldom investigated. In this study, FJPBPMSP is examined, where flexible processing and batch processing are performed sequentially. An adaptive imperialist competitive algorithm with cooperation (CAICA) is proposed to minimize makespan and total energy consumption simultaneously. In CAICA, a four-string representation is adopted, and initial empires with novel structures are formed by uniformly dividing the population. An adaptive assimilation and revolution are designed. An adaptive assimilation and revolution are designed. An adaptive imperialist competition with cooperation is provided. Search strategies, imperialists, and colonies are also renewed by new procedures. Computational experiments are conducted on 50 instances. The computational results show that the new strategies of CAICA are effective, and CAICA can provide better results than its comparative algorithms in solving FJPBPMSP.

This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires are maintained throughout the search process. Two types of assimilations are achieved: The strongest and weakest empires cooperate in their assimilation, while the remaining two empires, having a close normalization total cost, combine in their assimilation. A new form of imperialist competition is proposed to prevent insufficient competition, and the unique features of the problem are effectively utilized. Computational experiments are conducted across several instances, and a significant amount of experimental results show that the new strategies of CICA are effective, indicating promising advantages for the considered BPM scheduling problems.

As a complicated optimization problem, parallel batch processing machines scheduling problem (PBPMSP) exists in many real-life manufacturing industries such as textiles and semiconductors. Machine eligibility means that at least one machine is not eligible for at least one job. PBPMSP and scheduling problems with machine eligibility are frequently considered; however, PBPMSP with machine eligibility is seldom explored. This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition (CSFLA) to minimize makespan. In CSFLA, the initial population is produced in a heuristic and random way, and the competitive search of memeplexes comprises two phases. Competition between any two memeplexes is done in the first phase, then iteration times are adjusted based on competition, and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase. An adaptive population shuffling is given. Computational experiments are conducted on 100 instances. The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.

Hybrid flow shop scheduling problem (HFSP) has been extensively considered, however, some real-life conditions are seldom investigated. In this study, HFSP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm (ASFLA) is developed to optimize makespan. A new solution representation and a decoding procedure are presented, an adaptive memeplex search and dynamical population shuffling are implemented together. Many computational experiments are implemented. Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher’s self-learning phase, and learner’s self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.