Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices
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Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices Sven Schulz1
· Udo Buscher1 · Liji Shen2
© The Author(s) 2020
Abstract 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. Keywords Energy efficient scheduling · Hybrid flow shop · Mixed integer programming · Sustainability · Multi-objective optimization JEL Classification C30 · C61 · Q41
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Sven Schulz [email protected] Udo Buscher [email protected] Liji Shen [email protected]
1
Chair of Business Management, esp. Industrial Management, TU Dresden, 01069 Dresden, Germany
2
WHU-Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany
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S. Schulz et al.
1 Introduction Hardly any product is as socially relevant as electrical energy. Many everyday objects only work with electricity while electrification is steadily increasing. In the course of Industry 4.0, industrial companies rely on automated processes using robots, driverless transport systems or Auto ID technologies. In 2017, German industrial companies consumed 248.6 TWh of electrical energy and overall, the industrial sector is responsible for almost half of the total national electricity consumption (Ziesing 2018). The resulting CO2 emissions amount to about one-fifth of total emissions (Dai et al. 2013). The great importance of energy not only leads to a great social interest in efficient and sustainable use, companies are also increasingly pressured to reduce their energy costs in the face of global competition. Furthermore, they can benefit from an environmentally oriented image. Consequently, energy costs are now being taken into account in many approaches of production planning and control and thus also in operative planning in the form of energy efficient scheduling (EES). Together with approaches to reduce emissions or waste and preserve resources, a completely new branch of green scheduling research has thus developed. A general overview about different approaches to consider energy consumptio
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