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.

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 Improved artificial immune algorithm for the flexible job shop problem with transportation time

2020 ◽  
pp. 002029402096213
Author(s):  
Xiao-long Chen ◽  
Jun-qing Li ◽  
Yu-yan Han ◽  
Hong-yan Sang
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Immune Algorithm ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Transportation Time ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

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