High-performing heuristics to minimize flowtime in no-idle permutation flowshop

Nagano, Marcelo Seido; Rossi, Fernando Luís; Martarelli, Nádia Junqueira

doi:10.1080/0305215x.2018.1444163

Cited by 27 publications

(3 citation statements)

References 48 publications

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“…Equation (9) shows the idle time on each machine. Equations (10), (11), and (12) are used to ensure that no idling occurs between jobs on each machine. Equation ( 13) is the calculation formula of TEC minimization.…”

Section: Assumptions Notations and Mathematical Modelsmentioning

confidence: 99%

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Wati,

Amallynda

2023

JTI

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The current energy crisis is a pressing global challenge, with the industrial sector accounting for half of global energy consumption. Scheduling is considered one of the potential methods to reduce energy consumption. This article introduces the Fire Hawk Optimizer (FHO) algorithm to solve the no-idle flow shop scheduling problem to minimize overall energy consumption. FHO organizes the job sequence in no-idle flow shop scheduling for reduce energy consumption. This research investigates the use of different machine speed levels, namely slow, fast, and normal, based on case data of manufacturing industries in Indonesia. The results of this study compare the performance of the FHO algorithm with the Adaptive Integrated Greedy (AIG) heuristic method and compare it with the Grey Wolf Optimizer (GWO) algorithm. The experimental results showed that total energy consumption tends to be high when processed at high speed. Conversely, low-speed results in lower energy consumption but requires longer processing time. The comparison results show that the Fire Hawk Optimizer is more efficient in reducing total energy consumption than the AIG heuristic method. Meanwhile, the FHO algorithm performs comparably to the GWO algorithm and completes enumeration. These findings confirm that the proposed procedure can be an alternative to the scheduling optimization process.

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Section: Assumptions Notations and Mathematical Modelsmentioning

confidence: 99%

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Wati,

Amallynda

2023

JTI

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“…Rossi and Nagano [14] proposed a heuristic method to evaluate the total time of permutation sequences and an acceleration method for calculating the total flow time for the MNIFSP. Rossi and Nagano proposed a constructive heuristic for the MNIFSP with the minimization of makespan [15] and total tardiness [16] under sequence-dependent setup times. Nagano et al [17] proposed an efficient constructive heuristic method by combining an iterated greedy algorithm for the NIFSP with minimization of the total flow time.…”

Section: Related Workmentioning

confidence: 99%

Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

Zhang

Hou

et al. 2021

Complex Syst. Model. Simul.

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The Mixed No-Idle Flow-shop Scheduling Problem (MNIFSP) is an extension of flow-shop scheduling, which has practical significance and application prospects in production scheduling. To improve the efficacy of solving the complicated multiobjective MNIFSP, a MultiDirection Update (MDU) based Multiobjective Particle Swarm Optimization (MDU-MoPSO) is proposed in this study. For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time, the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism. Each subgroup prefers one convergence direction. Two subgroups are individually close to the two edge areas of the Pareto Front (PF) and serve two objectives, whereas the other one approaches the central area of the PF, preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation, which can better reflect the characteristics of sequence differences among particles. The MDU-MoPSO updates the particle in multiple directions and interacts in each direction, which speeds up the convergence while maintaining a good distribution performance. The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.

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“…Several proposed procedures for reducing tardiness include bi-population estimation of distribution algorithm (Shen et al, 2015), cuckoo search (CS) (Sun & Gu, 2017), and hybrid discrete water wave (Zhao et al, 2020). Furthermore, Nagano, Rossi, and Martarelli (2019) proposed an integrated greedy method to minimize total flow time.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

Al-Imron

Utama²,

Dewi³

2022

JITI

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Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To investigate this problem, 9 cases were used. The experiments show that it produces a massive amount of energy when a job is processed fast. Energy consumption is lower when machining at a slower speed. The performance of the GWO algorithm has been compared to that of the Cuckoo Search (CS) algorithm in several experiments. In tests, the Grey Wolf Optimizer (GWO) outperforms the Cuckoo Search (CS) algorithm.

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High-performing heuristics to minimize flowtime in no-idle permutation flowshop

Cited by 27 publications

References 48 publications

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

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