Energy-aware integrated optimization of process planning and scheduling considering transportation

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840114 ◽  
Author(s):  
Mei Dai ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Process Planning ◽  
Job Shop ◽  
Great Influence ◽  
Work Piece ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Shop Scheduling ◽  
Integrated Optimization

This paper concentrates on energy conservation in flexible manufacturing system. In addition to the energy saving of single machine tool, it is significant to reduce energy consumption in the two sub-systems of process planning and shop scheduling. Compared to traditional methods that consider the two sub-systems separately, integrated optimization of these sub-systems further improves the energy efficiency of the job shop. Furthermore, the transportation of jobs and semi-manufactured jobs in the process have been ignored in previous research, which has a great influence on the process routes selecting, machine dispatching and energy consumption. Therefore, this paper proposes an energy-aware multi-objective integrated optimization model of process planning and shop scheduling considering transportation. Parameters are optimized simultaneously including work piece machining feature selecting, process method selecting, processing sequence and machine dispatching of each job. The non-dominated sorting genetic algorithm is adopted to minimize the total energy consumption and makespan. Finally, a case study using the proposed model is employed to verify that energy consumption of transportation has authentically influence on total energy consumption and scheduling scheme.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

Research on the Integration Model of Process Planning and Job-Shop Scheduling

2014 ◽  
Vol 513-517 ◽  
pp. 1605-1608
Author(s):  
Yan Qiu Xiao ◽  
Lei Wang ◽  
Qi Li
Keyword(s):  
Process Planning ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Operation Mode ◽  
Virtual Manufacturing ◽  
Parallel Operation ◽  
Integration Model ◽  
Shop Scheduling ◽  
Integrated Optimization ◽  
Process Route

In order to meet the flexible requirements of enterprise production, an integrated optimization model based on the virtual manufacturing unit of process planning and job-shop scheduling is established. The model makes manufacturing resources information as a combining point and makes the two separate system integrated optimization. Then process route is established in three stages. At the same time uses hierarchy parallel operation mode to transfer and sharing the information of manufacturing resource by the layer of enterprise, work shop and parts. And the parallel mode of process and scheduling work meet the match selection of process and resources, this provides a theoretical basis for the real-time optimization of process planning and shop scheduling.

scholarly journals Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

2021 ◽  
Author(s):  
Leilei Meng
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Job Shop ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Total Energy Consumption ◽  
Sequence Dependent ◽  
Sequence Dependent Setup ◽  
Milp Model ◽  
Searching Ability

Abstract As environmental awareness grows, energy-aware scheduling is attracting increasing attention. This paper investigates the flexible job shop scheduling problem with sequence-dependent setup times and transportation times (FJSP-SDST-T) and the objective is to minimize total energy consumption. To begin with, the total energy consumption of the workshop is analyzed and a novel mixed integer linear programming (MILP) model is formulated. Due to that FJSP-SDST-T is NP-hard, an effective hybrid algorithm (HGA) that hybridizes the genetic algorithm (GA) and variable neighborhood search (VNS) algorithm is proposed to solve the problem specifically for that with large size. HGA takes advantage of the good global searching ability of GA and the powerful local searching ability of VNS, and it can have a good balance of intensification and diversification. Then, four energy-conscious decoding methods are designed, in which two energy-saving strategies namely postponing strategy and Turn Off/On strategy are specially designed according to the characteristics of FJSP-SDST-T. Finally, experiments are carried out and the results show the effectiveness of the MILP model, the energy-conscious decoding methods and HGA.

scholarly journals Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 44 ◽  
Author(s):  
Hongchan Li ◽  
Haodong Zhu ◽  
Tianhua Jiang
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Migrating Birds Optimization ◽  
Migrating Birds

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

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