Energy-aware decision support for production scheduling

Plitsos, Stathis; Repoussis, Panagiotis P.; Mourtos, Ioannis; Tarantilis, Christos D.

doi:10.1016/j.dss.2016.09.017

Cited by 35 publications

(17 citation statements)

References 23 publications

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“…Production Optimisation: This module will be based on algorithms to solve complex scheduling problems with various side constraints, such as utility (renewable resources) and machine maintenance constraints, to offer the capability of treating multiple scheduling objectives in a hierarchical fashion, including combined temporal and energy-aware objectives [12], [13]. In particular, the platform will develop scheduling algorithms in several ways that go beyond the current state-of-the-art, adding significantly to the provided level of accuracy and enhancing their planning capability as follows: i) Multiple machine configurations will be incorporated into the existing models as decision variables.…”

Section: Modelling and Design Tool: Enterprise Modelling Is A Particumentioning

confidence: 99%

A Proposal of Decentralised Architecture for Optimised Operations in Manufacturing Ecosystem Collaboration

Eirinakis

Buenabad-Chávez

Fornasiero

et al. 2017

Collaboration in a Data-Rich World

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Modelling and Design Tool: Enterprise Modelling Is A Particumentioning

confidence: 99%

A Proposal of Decentralised Architecture for Optimised Operations in Manufacturing Ecosystem Collaboration

Eirinakis

Buenabad-Chávez

Fornasiero

et al. 2017

Collaboration in a Data-Rich World

Self Cite

View full text Add to dashboard Cite

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“…As such, sensor measurements are likely to supersede pure condition-based signals in their contribution to overall prognostic capability. In order to carry out preemptive measures, one estimates the remaining useful life (RUL) and then applies a suitable strategy for scheduling maintenance operations (such as a simple threshold rule that triggers a maintenance once RUL undercuts a safety margin) in a cost-efficient manner [26,16,27]. Mathematically, the RUL at time t can be formalized as a random variable Y t that depends on the operative environment and its past use X t , .…”

Section: Introductionmentioning

confidence: 99%

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Kraus

Feuerriegel

2019

Decision Support Systems

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Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power.A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual timeto-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.

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“…Energy-efficient production scheduling problems have been extensively studied by a number of researchers in recent years [1][2][3][4]. In the existing research, considerations for energy efficiency can be divided into two categories, i.e., energy-related objectives and energy-related constraints.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling

2019

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In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.

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Energy-aware decision support for production scheduling

Cited by 35 publications

References 23 publications

A Proposal of Decentralised Architecture for Optimised Operations in Manufacturing Ecosystem Collaboration

A Proposal of Decentralised Architecture for Optimised Operations in Manufacturing Ecosystem Collaboration

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling

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