A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling

Schulz, Sven; Neufeld, Janis S.; Buscher, Udo

doi:10.1016/j.jclepro.2019.03.155

Cited by 94 publications

(27 citation statements)

References 43 publications

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“…The growing number of published articles, especially in the past 5 years, makes energy-efficiency scheduling a hot research topic. In the review process of this review work, there are several newest publications related to the scope of this review paper [95][96][97][98][99] which shows the importance and high relevancy of the studied subject. Intelligent strategies are used by many researchers in scientific community.…”

Section: Discussionmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

182

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Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

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Section: Discussionmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

182

View full text Add to dashboard Cite

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“…The discrete time modelling and mixed-integer linear programming-based approach reduce energy costs and harmful gas emissions by optimising the use of cogeneration and more efficient exploitation of utility resources. Schulz et al [13] developed an approach for considering the three strategies for reducing energy costs: energy efficiency, energyflexibility and avoiding peak loads. It is based on constructing a mathematical model that considers volatile energy prices and an iterated local search algorithm.…”

Section: Energy-oriented Ppcmentioning

confidence: 99%

Development and evaluation of risk treatment paths within energy-oriented production planning and control

Roth

Kalchschmid

Reinhart

2021

Prod. Eng. Res. Devel.

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Production planning and control pursues high delivery reliability and short delivery time of the production system at the lowest possible costs. Especially in energy-intensive industries, energy cost account for a significant amount of manufacturing costs. The consideration of variable electricity market prices using energy-flexibility measures facilitates reduced costs by adapting the load profile of production to an electricity price forecast. However, it also increases the production planning and control system’s complexity by additional input variables and possible risks due to the influence of flexibility measures on the production system. In the case of unexpected events, such as failure of machines or faulty materials, it is difficult to adapt the complex production system to the new situation quickly. There is a risk of high additional costs by various causes, such as delays in deadlines or load peaks. Therefore, this paper presents an approach for developing and evaluating risk treatment paths, which include possible combinations of risks and measures for the mitigation of risk effects. The advantage of these paths compared to a situational reaction is that all effects and possible further interactions can be considered and thus overall cost-efficient solutions can be found. The approach is based on the determination of interactions through interpretive structural modelling and the calculation of conditional probabilities using Bayesian Networks. The approach was implemented in MATLAB® and applied using real order and energy data from a foundry. The results show that the presented approach enables structured and data-based comparison of risk treatment paths.

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“…A particle swarm optimization is also used by Nilakantan et al (2015) to minimize makespan and energy consumption in a robotic assembly line system with differently efficient robots. Schulz et al (2019) propose an iterated local search algorithm to optimize three different objectives (makespan, energy costs, peak power) in a heterogeneous hybrid flow shop problem. In this work, however, we focus on identical parallel machines.…”

Section: (D) Increase Utilization Of More Efficient Machinesmentioning

confidence: 99%

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

2020

Self Cite

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Energy costs play an important role in industrial production and are closely related to environmental concerns. As sustainability aspects are coming into focus in recent years, energy-oriented objectives are increasingly being taken into account in scheduling. At the same time, requirements for punctual delivery become more and more important in times of just-in-time delivery and highly networked supply chains. In this paper, a hybrid flow shop scheduling problem with variable discrete production speed levels is considered with the aim of minimizing both energy costs and total tardiness. Although lower speeds can reduce energy consumption, they also increase processing times, which counteract the objective of punctual delivery. Two new model formulations additionally taking time-of-use energy prices into account are presented and compared. The influence of variable discrete production speed levels on energy costs, energy consumption and punctual delivery as well as the interdependencies between these objectives are analysed in a numerical case study.

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A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling

Cited by 94 publications

References 43 publications

A review of energy-efficient scheduling in intelligent production systems

A review of energy-efficient scheduling in intelligent production systems

Development and evaluation of risk treatment paths within energy-oriented production planning and control

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

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