A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling p
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RESEARCH PAPER
A modified teaching learning metaheuristic algorithm with opposite‑based learning for permutation flow‑shop scheduling problem Umesh Balande1 · Deepti shrimankar1 Received: 2 March 2020 / Revised: 18 July 2020 / Accepted: 8 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Teaching-Learning-Based Optimization is one of the well-known metaheuristic algorithm in the research industry. Recently, various population-based algorithms have been developed for solving optimization problems. In this paper, a random scale factor approach is proposed to modify the simple TLBO algorithm. Modified Teaching-Learning-Based Optimization with Opposite-Based-Learning algorithm is applied to solve the Permutation Flow-Shop-Scheduling Problem with the purpose of minimizing the makespan. The OBL approach is used to enhance the quality of the initial population and convergence speed. PFSSP is used extensively for solving scheduling problem, which belongs to the category of NP-hard optimization problems. First, MTLBO is developed to effectively determine the PFSSP using the Largest Order Value rule-based random key, so that individual job schedules are converted into discrete schedules. Second, new initial populations are generated in MTLBO using the Nawaz–Enscore–Ham heuristic mechanism. Finally, the local exploitation ability is enhanced in the MTLBO using effective swap, insert and inverse structures. The performance of proposed algorithm is validated using ten benchmark functions and the Wilcoxon rank test. The computational results and comparisons indicate that the proposed algorithm outperformed over five well-known datasets such as Carlier, Reeves, Heller, Taillards and VRF benchmark test functions, compared to other metaheuristic algorithms. The p-value indicated the significance and superiority of the proposed algorithm over other metaheuristic algorithms. Keywords Evolutionary algorithms · Opposite-based learning · Permutation flow-shop scheduling problem · Teachinglearning-based optimization
1 Introduction Scheduling is a decision-making process, perform vital role in services, manufacturing and production industry. It is traditionally defined as a process of allocating job sequence on different machines that reduces the makespan (completiontime) of a job sequence [1]. Scheduling problems are found in many real-world industries such as textile [2], discrete manufacturing industries [3], electronics [4], chemical [5], production of concrete [6], manufacturing of photographic * Umesh Balande [email protected] Deepti shrimankar [email protected] 1
Visvesvaraya National Institute of Technology, Nagpur 440010, India
film [7], iron and steel [8], and internet service architecture [9]. Typically, scheduling problems are classified on the basis of production environments such as single machines, flow-shop, parallel-machines, open-shop, cyclic flow shop and Flexible Job-Shop (FJS). PFSSP is an ultimate engaging research problem in the manufacturing industry with an
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