A multi-level optimization approach for energy-efficient flexible flow shop scheduling
Introduction
Due to the mounting concerns on fossil fuel depletion and climate change, manufacturing industries, along with other industrial sectors, are under pressure to reduce energy consumption. According to the International Energy Outlook proposed by the U.S. Energy Information Administration, delivered energy consumption in the industrial sector totaled 24.5 quadrillion Btu in 2013, representing approximately 34% of total U.S. delivered energy consumption (EIA, 2015). Some preliminary studies on the environmental performance of machine tools indicate that more than 90% of the environmental impact (including energy consumption) is due to the electrical energy consumption during the use stage (CECIMO, 2009). In addition, over long term energy prices are expected to go through large increase and many manufacturing companies have brought the cost of energy onto the agenda (Dietmair and Verl, 2009). In recent years, research on energy efficient manufacturing has attracted increasing interests due to environmental concerns and the rising energy costs.
Energy efficient manufacturing can be achieved through developing new or advancing existing machines and processes, which has been the main approach in the past. Recently, developing new operation strategies (e.g. manufacturing scheduling) for improved energy efficiency have gained interests. Compared with hardware upgrades, operation changes need less capital investment and may only incur minimal production disruption. From the perspective of the organization of a system, manufacturing activities can be considered at multiple levels. As suggested by Duflou et al. (2012), five different levels can be used: device/unit machine tool level, line/cell/multi-machine system, facility, multi-factory system and enterprise/global supply chain. The energy efficiency analysis and efforts on energy efficiency improvement can take place at different levels. However, Salonitis and Ball (2013) pointed out that synergistic improvements can be achieved if manufacturing activities are considered at different levels, by capturing opportunities that may have been missed if only a single level is considered.
Research to date on energy efficient manufacturing scheduling largely focuses at the shop floor level while machine level energy consumption is treated as given constants. This work addresses this problem for the case of flexible flow shop (FFS) by developing a novel multi-level (machine tool and shop floor level) optimization method. This research is largely centered on the integration of single machine and shop floor, particularly from a modeling and optimization perspective. The paper is organized as follows. Section 2 focuses on the state of art in energy consumption modelling and energy-efficient FFS scheduling. Section 3 describes the energy consumption model of FFS and the proposed multi-level optimization method. Section 4 presents a case study to demonstrate the feasibility of the proposed approach. Section 5 concludes the paper with some perspectives.
Section snippets
Literature review
The problem considered in this paper involves two important research areas. They are: (1) energy consumption modeling and optimization of single machine and (2) energy-efficient flow shop scheduling at shop floor level. The rest of this section will discuss the related work in these two areas.
Multi-level optimization for energy-efficient FFS scheduling
In this work, makespan and total energy consumption are used as the optimization objectives at shop floor level. Cutting energy and cutting time are used as the optimization objectives at machine tool level. Fig. 1 shows the framework of multi-level optimization for energy-efficient FFS scheduling. To start, energy consumption model at machine tool level is established based on experimental investigations. Energy consumption is measured under combinations of cutting parameters selected based on
Case study
In order to validate the proposed multi-level optimization method, the scheduling of 4 jobs in a FFS composed by 4 stages was examined. The number of parallel machines for each stage is 2 (M1 and M2), 2 (M3 and M4), 3 (M5, M6, and M7), and 2 (M8 and M9), respectively. The power demand of the transport vehicle is set to 25 kJ/m and the power demand of auxiliary equipment and supporting facility in FFS is assumed to be 1 kW. The material removal volume (MRV), transporting distance (m), and setup
Conclusions and future research
This paper proposes a multi-level optimization method for reducing the total energy consumption and makespan of a flexible flow shop. In this multi-level optimization method, cutting parameters of each machine are allowed to vary to affect the processing time and energy consumption. This is different from common scheduling method where the processing time of each job in each stage is fixed. The scheduling results show that the multi-level optimization method can assist manufacturers to reduce
Acknowledgments
This research is funded by the National Natural Science Foundation of China (#51561125002) and Short-term Visiting Program of Harbin Institute of Technology (#AUDB98322026). The authors wish to thank Xin Li, Zimo Wang, Wei Wang, and Shuo Zhang for their insightful contributions to this work. Zengguang Yang is thanked for providing technical support during the experiments.
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