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Energy-efficient integration of process planning and scheduling in discrete parts manufacturing with a heuristic-based two-stage approach

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第5-6期106卷 2415-2432页

作者： Liu, N. Zhang, Y. F. Lu, W. F. Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117576 Singapore

Energy-efficient manufacturing is playing an important role in addressing worldwide problems like air pollution, climate change, and energy crisis. Despite of the close relationship between process planning and scheduling, existing research efforts often consider energy efficiency-related issues by treating them separately and sequentially. This may cause various problems such as unbalanced resource allocation and infeasible production solutions, thus limiting their potential application. To advance the current state of the art in this area, this paper investigates energy-efficient integration of process planning and scheduling. Specifically, a mixed-integer linear programming model is firstly developed to formulate this problem mathematically. Subsequently, to solve more realistic large-sized problems, a heuristic-based two-stage approach is proposed. Stage 1 and Stage 2 aim at minimizing the total tardiness and reducing the total energy consumption, respectively. A realistic case study shows that the proposed two-stage approach works more efficiently and effectively than a modified genetic algorithm from existing literature. Through instructive modification of the solution space, the proposed approach can effectively reduce the total tardiness and total energy consumption;meanwhile the machining cost of individual process plans can also be kept at low level throughout the searching process. Results show that the proposed approach can allocate the machining resources in a more reasonable manner. As such, this paper may be a valuable supplement to existing efforts aiming at developing energy-efficient manufacturing techniques.

关键词： Energy efficiency Discrete parts manufacturing integration of process planning and scheduling Multi-objective optimization Heuristic-based algorithm

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Energy-aware integration of process planning and scheduling of advanced machining workshop

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第11期231卷 2040-2055页

作者： Zhang, Chaoyang Jiang, Pingyu Zhang, Lei Gu, Peihua Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Shaanxi Peoples R China Shantou Univ Coll Engn 243 Daxue Rd Shantou Shantou 515063 Guangdong Peoples R China

Increasing energy consumption of manufacturing industry demands novel approaches to achieve energy conservation and emission reduction. Most of the previous research efforts in this area focused more on analyzing manufacturing energy consumption of a process or that of a machine tool with less concern on the system level of advanced machining workshop, especially a flexible manufacturing system. In this article, a new energy-saving approach of flexible manufacturing system is put forward based on energy evaluation model for integration of process planning and scheduling problem in flexible manufacturing system (flexible manufacturing system-integration of process planning and scheduling). First, complying with feature precedence and other technological requirements, flexible manufacturing system -integration of process planning and scheduling is mapped as an asymmetric traveling salesman problem of which operations are provinces and candidate operations are cities belonging to different provinces. To evaluate the performance of each solution of the asymmetric traveling salesman problem, energy consumption evaluation criteria for flexible manufacturing system-integration of process planning and scheduling are established and three energy efficiency indicators are also provided to perform further analysis on manufacturing energy consumption, that is, part energy efficiency, machine tool energy efficiency and feasible solution energy efficiency. Then, a mutation-combined ant colony optimization algorithm is proposed to solve the flexible manufacturing system-integration of process planning and scheduling which combined roulette and mutation selection methods to pick out the next candidate operation. The pheromone trails associated with edges are released by the so-far-best ant or the iteration-best ant probabilistically to both keep the search directed and avoid converging to the local best. Finally, a case study of flexible manufacturing system in advanced machining w

关键词： Flexible manufacturing system integration of process planning and scheduling energy conservation energy efficiency ant colony optimization

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A method for minimizing the energy consumption of machining system: integration of process planning and scheduling

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JOURNAL OF CLEANER PRODUCTION 2016年第0期137卷 1647-1662页

作者： Zhang, Zhongwei Tang, Renzhong Peng, Tao Tao, Liyan Jia, Shun Zhejiang Univ Coll Mech Engn State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Zhejiang Peoples R China Hangzhou Dianzi Univ Res Inst Ind Engn & Management Hangzhou 310018 Zhejiang Peoples R China Shandong Univ Sci & Technol Dept Ind Engn Qingdao 266590 Peoples R China

Green Low-carbon development has been recognized as a key requirement for advanced manufacturing, and reducing the energy consumption of machining system is a critical aspect. Traditionally, process planning and scheduling were carried out separately and sequentially in manufacturing industry, which limits the work for energy-efficient machining system. Considering the fact that the functions of these two systems are usually complementary, the energy-saving of machining system can be further improved if they are tightly integrated. In this paper, an integration model based on nonlinear process planning (NLPP) is proposed to implement such energy-saving method, and the Therblig-based model is used to predict the energy consumption of machine tools in product manufacturing process. Then, a genetic algorithm-based approach is adopted to solve the established integration model. Given the alternative process schemes generated by NLPP, such integration model can help select the suitable process plan and machines for each job and generate the scheduling scheme simultaneously for saving energy. To verify the energy-saving effect and the performance of the algorithm, case studies have been conducted and the experimental results show that the energy-saving potential in a shop floor environment can be further enhanced through the integration of process planning and scheduling and the solution method is feasible. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Machining Machining system Energy consumption Therblig integration of process planning and scheduling Genetic algorithm

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integration of process planning and scheduling using a hybrid GA/PSO algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期78卷 583-592页

作者： Yu, Mingrang Zhang, Yingjie Chen, Kun Zhang, Ding Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

process planning and scheduling are two of the most important activities in the manufacturing system. integration of process planning and scheduling (IPPS) is treated as a crucial technology to realize computer-integrated manufacturing system (CIMS). This paper proposes a new IPPS method which contains two phases: static phase and dynamic phase. In the static phase, the process network of each job is generated according to the static shop floor status, and in the dynamic phase, the process plan of each job and the scheduling plan are generated according to the current shop floor status. With consideration of the optimization criteria of both process planning and scheduling, a hybrid algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) is presented for optimizing the IPPS problem. Furthermore, a new reactive scheduling method, in which the flexibility of the process plan is taken into account, is presented to improve the flexibility of rescheduling. Finally, the proposed IPPS method is compared with the three existing IPPS methods, which are nonlinear process planning (NLPP), closed-loop process planning (CLPP) and distributed process planning (DPP). Meanwhile, the reactive scheduling method is also compared with the right-shift rescheduling method and the route changing rescheduling strategy. The results show the effectiveness of the proposed IPPS method and the reactive scheduling method.

关键词： integration of process planning and scheduling Hybrid algorithm Reactive scheduling

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General Particle Swarm Optimization Algorithm for integration of process planning and scheduling

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2nd International Conference on Advances in Product Development and Reliability (PDR)

作者： Xu Shaotan Li Xinyu Gao Liang Sun Yi Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780878492541

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles' local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

关键词： Particle swarm optimization integration of process planning and scheduling Tabu search

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Investigation of reconfiguration effect on makespan with social network method for flexible job shop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 110卷 231-241页

作者： Reddy, M. B. S. Sreekara Ratnam, C. H. Agrawal, Rajeev Varela, M. L. R. Sharma, Ila Manupati, V. K. AUCE Dept Mech Engn Visakhapatnam Andhra Pradesh India Birla Inst Technol Dept Prod Engn Ranchi Bihar India Univ Minho Sch Engn Dept Prod & Syst Braga Portugal VIT Univ Sch Mech Engn Vellore Tamil Nadu India

This paper presents a novel social network analysis based method (SNAM) to evaluate the reconfiguration effect i.e., identification of key machines and their influence on the system performance in the context of Flexible job shop scheduling problem (FJSSP). This research formulates a mathematical model along with the constraints by incorporating the total completion time of jobs as an objective function. The proposed SNAM has been applied to generate the collaboration networks by transforming the input data and presenting them in the form of an affiliation matrix to the network analysis software. Thereafter, to analyze the collaboration networks various SNA measures that have been calculated and different functional properties are evaluated. Finally, to investigate the reconfiguration effect on makespan integration of process planning and scheduling (IPPS) has been implemented with adopted effective game theory based hybrid deoxyribonucleic acid (DNA) algorithm. The validation of the proposed approach and its effectiveness is conducted through comparisons with benchmark instances and results confirm the efficiency of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flexible job shop scheduling problem Social network analysis method integration of process planning and scheduling Deoxyribonucleic acid

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integration of process planning and scheduling-A modified genetic algorithm-based approach

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COMPUTERS & OPERATIONS RESEARCH 2009年第6期36卷 2082-2096页

作者： Shao, Xinyu Li, Xinyu Gao, Liang Zhang, Chaoyong Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： process planning scheduling integration of process planning and scheduling Genetic algorithm

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More MILP models for integrated process planning and scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第14期54卷 4387-4402页

作者： Jin, Liangliang Tang, Qiuhua Zhang, Chaoyong Shao, Xinyu Tian, Guangdong Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Mech Engn Wuhan Peoples R China Northeast Forestry Univ Transportat Coll Harbin Peoples R China

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.

关键词： flexible manufacturing mixed-integer linear programming integration of process planning and scheduling

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A Genetic Algorithm for integration of process planning and scheduling Problem

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1st International Conference Intelligent Robotics and Applications

作者： Li, Xinyu Gao, Liang Zhang, Guohui Zhang, Chaoyong Shao, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783540885160

Traditionally, process planning and scheduling for parts are carried Out in a sequential way, where scheduling is done after process plans have been generated. Considering the two functions are usually complementary, there is a great potential to integrate them more tightly so that greater performance and higher productivity of a manufacturing system can be achieved. In this paper, a genetic algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the genetic algorithm-based approach, efficient genetic representations and operator schemes have been developed. Experiment Studies have been conducted. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.

关键词： integration of process planning and scheduling process planning scheduling Genetic algorithm

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An optimization tool for operational tolerances allocation, work in process inventory minimization, and machines assignment in a discrete part manufacturing environment

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第9-12期55卷 1069-1078页

作者： Altarazi, Safwan A. German Jordanian Univ Dept Ind Engn Amman Jordan

In the last two decades, many researchers have addressed the superior system performance resulted from the integration of process planning and scheduling functions. However, most of the published solution methods in this field fall short in three accounts. First, while integrating with scheduling, they ignore the checking of process planning feasibility with respect to tolerances allocation. Thus, operational tolerances may stackup beyond the blue print tolerances making the process plans infeasible. Second, they ignore the machines capabilities during the integration modeling which make these solution models practically inapplicable. Third, they focus on time consideration, such as makespan or lateness, and do not consider manufacturing cost related to operations-machines assignment. This paper presents an innovative model for the integration of process planning and scheduling in a job-shop environment. The model simultaneously serves three purposes: allocating operational tolerances while minimizing its manufacturing cost, minimizing the work in process inventory, and figuring the operation-machine assignments. The preemptive-goal programming method is used to solve the proposed multiobjective non-linear mixed integer model, and an implementation example is presented to demonstrate the effectiveness of the proposed modeling approach.

关键词： process planning scheduling integration of process planning and scheduling Operational tolerances allocation Work in process minimization

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