Open AccessThe hybrid whale optimization algorithm: A new metaheuristic algorithm for energy-efficient on flow shop with dependent sequence setup
Author(s) -
Dana Marsetiya Utama,
Dian Setiya Widodo,
Muhammad Faisal Ibrahim,
Khoirul Hidayat,
Teguh Baroto,
Aminatul Yurifah
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1569/2/022094
Subject(s) - algorithm , computer science , mathematical optimization , job shop scheduling , energy consumption , flow shop scheduling , sequence (biology) , metaheuristic , bat algorithm , schedule , mathematics , engineering , particle swarm optimization , biology , electrical engineering , genetics , operating system
Recently, The industrial sector produces about half of the worlds total energy consumption. Manufacturing companies are required to reduce energy consumption. This article aims to develop a Hybrid Whale Optimization Algorithm (HWOA). We use the objective function of minimizing energy consumption. It solves the problem with permutation flow scheduling problems (PFSSP). Dependent sequence setup is a PFSSP problem with setups that depend on schedule sequence. We offer HWOA with local search strategies. The solution in each HWOA iteration is improved using flip and swap mutations. Furthermore, HWOA is compared with several algorithms. We use numerical experiments to show the performance of the proposed algorithm. Comparative analysis with several algorithms has previously been carried out with ten variations of PFSSP problems. Based on numerical experiments, HWOA proved to be competitive compared to other algorithms.