Multi-objective energy-saving scheduling for a permutation flow line

2016 ◽  
Vol 232 (5) ◽  
pp. 879-888 ◽  
Author(s):  
Shunjiang Li ◽  
Fei Liu ◽  
Xiaona Zhou
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Production Scheduling ◽  
Flow Line ◽  
Numerical Control ◽  
System Level ◽  
Scheduling Problem ◽  
Multi Objective ◽  
Total Flowtime ◽  
Fixed Energy

Nowadays, manufacturing enterprises, as larger energy consumers, face the severe environmental challenge and the mission of reducing energy consumption. Therefore, how to reduce energy consumption becomes a burning issue for manufacturing. Production scheduling provides a feasible scheme for energy saving on the system level. However, the existing researches of energy-saving scheduling rarely focus on the permutation flow line scheduling problem. This article proposes an energy-saving method for permutation flow line scheduling problem. First, a mathematical model for the permutation flow line scheduling problem is developed based on the principle of multiple energy source system of the computer numerical control machine tool. The optimization objective of this model is to simultaneously minimize the total flowtime and the fixed energy consumption. Since permutation flow line scheduling problem is a well-known NP-hard problem, the non-dominated sorting genetic algorithm II is adopted to solve the multi-objective permutation flow line scheduling problem. Finally, the effectiveness of this method is verified by numerical illustration. The computation results show that a significant trade-off between total flowtime and fixed energy consumption for the permutation flow line scheduling problem, and there would be potential for saving energy consumption by using the proposed method.

scholarly journals Research on hot rolling scheduling problem based on Two-phase Pareto algorithm

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0241077
Author(s):  
Wang Chen ◽  
Zhang Xiufeng ◽  
Zhao Guohua
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Vehicle Routing ◽  
Hot Rolling ◽  
Vehicle Routing Problem ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Two Phase ◽  
Routing Problem ◽  
Multi Objective

Under the background of excess capacity and energy saving in iron and steel enterprises, the hot rolling batch scheduling problem based on energy saving is a multi-objective and multi constraint optimization problem. In this paper, a hybrid multi-objective prize-collecting vehicle routing problem (Hybrid Price Collect Vehicle Routing Problem, HPCVRP) model is established to ensure minimum energy consumption, meet process rules, and maximize resource utilization. A two-phase Pareto search algorithm (2PPLS) is designed to solve this model. The improved MOEA/D with a penalty based boundary intersection distance (PBI) algorithm (MOEA/D-PBI) is introduced to decompose the HPCVRP in the first phase. In the second phase, the multi-objective ant colony system (MOACS) and Pareto local search (PLS) algorithm is used to generate approximate Pareto-optimal solutions. The final solution is then selected according to the actual demand and preference. In the simulation experiment, the 2PPLS is compared with five other algorithms, which shows the superiority of 2PPLS. Finally, the experiment was carried out on actual slab data from a steel plant in Shanghai. The results show that the model and algorithm can effectively reduce the energy consumption in the process of hot rolling batch scheduling.

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

2019 ◽  
Vol 11 (19) ◽  
pp. 5381 ◽  
Author(s):  
Yueyue Liu ◽  
Xiaoya Liao ◽  
Rui Zhang
Keyword(s):  
Energy Consumption ◽  
Local Search ◽  
Single Machine ◽  
Optimization Algorithm ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective

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.

scholarly journals Multi-Objective Casting Production Scheduling Problem by a Neighborhood Structure Enhanced Discrete NSGA-II: An Application from Real-World Workshop

2021 ◽  
Author(s):  
Weihua Tan ◽  
Xiaofang Yuan ◽  
Yuhui Yang ◽  
Lianghong Wu
Keyword(s):  
Energy Consumption ◽  
Real World ◽  
Production Scheduling ◽  
Scheduling Problem ◽  
Neighborhood Structure ◽  
Total Energy Consumption ◽  
Nsga Ii ◽  
Research Attention ◽  
Multi Objective ◽  
Casting Production

Abstract Casting production scheduling problem (CPSP) has attracted increasing research attention in recent years to facilitate the profits, efficiency, and environment issues of casting industry. Casting is often characterized by the properties of intensive energy consumption and complex process routes, which motivate the in-depth investigation on construction of practical multi-objective scheduling models and development of effective algorithms. In this paper, for the first time, the multi-objective casting production scheduling problem (MOCPSP) is constructed to simultaneously minimize objectives of defective rate, makespan, and total energy consumption. Moreover, a neighborhood structure enhanced discrete NSGA-II (N-NSGA-II) is designed to better cope with the proposed MOCPSP. In the N-NSGA-II, the advantage of selection mechanism of NSGA-II is fully utilized for selecting non-dominate solution, three neighborhood structures are elaborately designed to strengthen the ability of the local search, and a novel solution generating approach is proposed to increase the diversity of solutions for global search. Finally, a real-world case is illustrated to evaluate the performance of the N-NSGA-II. Computational results show that the proposed N-NSGA-II obtains a wider range of non-dominated solutions with better quality compared to other well-known multi-objective algorithms.

Multi-objective discrete water wave optimization algorithm for solving the energy-saving job shop scheduling problem with variable processing speeds

2021 ◽  
pp. 1-15
Author(s):  
Huiqi Zhu ◽  
Tianhua Jiang ◽  
Yufang Wan ◽  
Guanlong Deng
Keyword(s):  
Energy Saving ◽  
Optimization Algorithm ◽  
Production Scheduling ◽  
Water Wave ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Total Energy Consumption ◽  
Multi Objective ◽  
Variable Processing

For the job shop with variable processing speeds, the aim of energy saving and consumption reduction is implemented from the perspective of production scheduling. By analyzing the characteristics of the workshop, a multi-objective mathematical model is established with the objective of reducing the total energy consumption and shortening the makespan. A multi-objective discrete water wave optimization (MODWWO) algorithm is proposed for solving the problem. Firstly, a two-vector encoding method is adopted to divided the scheduling solution into two parts, which represent speed selection and operation permutation in the scheduling solution, respectively. Secondly, some dispatching rules are used to initialize the population and obtain the initial scheduling solutions. Then, three operators of the basic water wave optimization algorithm are redesigned to make the algorithm adaptive for the multi-objective discrete scheduling problem under study. A new propagation operator is presented with the ability of balancing global exploration and local exploitation based on individual rank and neighborhood structures. A novel refraction operator is designed based on crossover operation, by which each individual can learn from the current best individual to absorb better information. And a breaking operator is modified based on the local search strategy to enhance the exploitation ability. Finally, extensive simulation experiments demonstrate that the proposed MODWWO algorithm is effective for solving the considered energy-saving scheduling problem.

An improved SFLA for flexible job shop scheduling problem considering energy consumption

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840112 ◽  
Author(s):  
Xiaoxing Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

scholarly journals Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
Author(s):  
Yongmao Xiao ◽  
Qingshan Gong ◽  
Xiaowu Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Low Cost ◽  
Minimum Cost ◽  
Processing Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

scholarly journals Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes

2021 ◽  
Vol 13 (6) ◽  
pp. 3454
Author(s):  
Yu Lin ◽  
Hongfei Jia ◽  
Bo Zou ◽  
Hongzhi Miao ◽  
Ruiyi Wu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Travel Time ◽  
Heterogeneous Traffic ◽  
System Level ◽  
Network Equilibrium ◽  
Nsga Ii ◽  
The Sustainable Development ◽  
Multi Objective ◽  
Upper Level

The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.

Minimizing Total Idle Energy Consumption in the Permutation Flow Shop Scheduling Problem

2018 ◽  
Vol 35 (06) ◽  
pp. 1850041 ◽  
Author(s):  
Guo-Sheng Liu ◽  
Jin-Jin Li ◽  
Ying-Si Tang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Heuristic Algorithms ◽  
Flow Shop ◽  
Power Level ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

In this paper, we investigate the well-known permutation flow shop (PFS) scheduling problem with a particular objective, the minimization of total idle energy consumption of the machines. The problem considers the energy waste induced by the machine idling, in which the idle energy consumption is evaluated by the multiplication of the idle time and power level of each machine. Since the problem considered is NP-hard, theoretical results are given for several basic cases. For the two-machine case, we prove that the optimal schedule can be found by employing a relaxed Johnson’s algorithm within O([Formula: see text]) time complexity. For the cases with multiple machines (not less than 3), we propose a novel NEH heuristic algorithm to obtain an approximate energy-saving schedule. The heuristic algorithms are validated by comparison with NEH on a typical PFS problem and a case study for tire manufacturing shows an energy consumption reduction of approximately [Formula: see text] by applying the energy-saving scheduling and the proposed algorithms.

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