Diversified teaching-learning-based optimization for fuzzy two-stage hybrid flow shop scheduling with setup time

Abstract

Distributed scheduling has attracted much attention in recent years; however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem.

An Adaptive Two-Class Teaching-Learning-Based Optimization for Energy-Efficient Hybrid Flow Shop Scheduling Problems with Additional Resources

“…TLBO usually has only one class. A multi-class TLBO is considered to improve the performance of TLBO for scheduling problems [32,38]. These algorithms have more than two classes and the number of classes is the algorithm parameter.…”

“…A flowchart of ATLBO is shown in Figure 2 . Unlike the existing TLBOs [32,38], ATLBO just uses two classes. After two classes are formed, a teacher phase with self-learning and teaching is executed on multiple teachers.…”

“…Compared with other algorithms, TLBO has a simple structure, fewer parameters, and is both easy to understand and implement. It has become an effective method for addressing production scheduling problems [7,[31][32][33][34][35][36][37][38]. Lei et al [34] proposed a novel TLBO for EHFSPs.…”

“…Buddala and Mahapatra [37] presented a two-stage TLBO to solve an HFSP involving unexpected machine breakdowns. Lei and Xi [38] proposed a Q-learning-based TLBO for two-stage fuzzy HFSPs.…”

Collaborative variable neighborhood search for multi-objective distributed scheduling in two-stage hybrid flow shop with sequence-dependent setup times

“…For this problem with the optimization objective of minimizing maximum completion time, the common methods are iterative greed algorithm 44 , 60 , shuffled frog leaping algorithm 47 , hybrid brain storm optimization algorithm 49 , 54 , artificial bee colony algorithm 55 – 57 , and cooperative memetic algorithm 62 etc. For the problem with multi-objective optimization, the methods used are genetic algorithm 59 , variable neighborhood search 48 , shuffled frog leaping algorithm 45 , 46 , 51 , 64 , teaching-learning-based optimization 52 , cooperative coevolution algorithm 63 , 65 , decomposition-based multi-objective optimization 50 , iterated greedy algorithm 61 and other evolutionary algorithms 53 .…”