TLBO with variable weights applied to shop scheduling problems. Issue 3 (3rd July 2019)

• Record Type: Journal Article
• Authors: Rodrigues, Leonardo Ramos; Gomes, João Paulo Pordeus
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 3(2019)
• Page Start: 148
• Page End: 158
• Publication Date: 2019-07-03
• Subjects: learning (artificial intelligence) -- teaching -- flow shop scheduling -- search problems -- statistical testing -- simulated annealing -- job shop scheduling -- scheduling -- optimisation; assigning zero -- variable weights -- teaching–learning‐based optimisation algorithm -- population‐based -- teaching–learning process -- variant version -- different weights -- student phase -- higher weights -- assign weights -- flow‐shop -- job‐shop scheduling problems -- original TLBO algorithm -- solution quality -- original version -- fixed weight; B0260 Optimisation techniques -- C1140Z Other topics in statistics -- C1180 Optimisation techniques -- C1290F Systems theory applications in industry -- C6170K Knowledge engineering techniques -- E0210G Optimisation -- E1010 Production management -- E1540 Systems theory applications; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1089
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : The teaching–learning‐based optimisation (TLBO) algorithm is a population‐based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of applications. In this study, the authors present a variant version of TLBO. In the proposed version, different weights are assigned to students during the student phase, with higher weights being assigned to students with better solutions. Three different approaches to assign weights are investigated. Numerical experiments with benchmark instances of the flow‐shop and the job‐shop scheduling problems are carried out to investigate the performance of the proposed approaches. They compare the proposed approaches with the original TLBO algorithm and with two variants of TLBOs proposed in the literature in terms of solution quality, convergence speed and simulation time. The results obtained by the application of a Friedman statistical test showed that the proposed approaches outperformed the original version of TLBO in terms of convergence, with no significant losses in the average makespan. The additional simulation time required by the proposed approaches is small. The best performance was achieved with the approach of assigning a fixed weight to half the students with the best solutions and assigning zero to other students.