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Author: Sicheng Zhang
Publisher: Open Dissertation Press
ISBN: 9781361305997
Category :
Languages : en
Pages :
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Book Description
This dissertation, "An Enhanced Ant Colony Optimization Approach for Integrating Process Planning and Scheduling Based on Multi-agent System" by Sicheng, Zhang, 张思成, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Process planning and scheduling are two important manufacturing planning functions which are traditionally performed separately and sequentially. Usually, the process plan has to be prepared first before scheduling can be performed. However, due to the complexity of manufacturing systems and the uncertainties and dynamical changes encountered in practical production, process plans and schedules may easily become inefficient or even infeasible. The concept of integrated process planning and scheduling (IPPS) has been proposed to improve the efficiency, effectiveness as well as flexibility of the respective process plan and schedule. By combining both functions together, the process plan for producing a part could be dynamically arranged in accordance with the availability of manufacturing resources and current status of the system, and its operations' schedule could be determined concurrently. Therefore, IPPS could provide an essential solution to the dynamic process planning and scheduling problem in the practical manufacturing environment. Nevertheless, process planning and scheduling are both complex functions that depend on many factors and flexibilities in the manufacturing system, IPPS is therefore a highly complex NP-hard problem. Ant colony optimization (ACO) is a widely applied meta-heuristics, which has been proved capable of generating feasible solutions for IPPS problem in previous research. However, due to the nature of the ACO algorithm, the performance is not that favourable compared with other heuristics. This thesis presents an enhanced ACO approach for IPPS. The weaknesses and limitations of standard ACO algorithm are identified and corresponding modifications are proposed to deal with the drawbacks and improve the performance of the algorithm. The mechanism is implemented on a specifically designed multi-agent system (MAS) framework in which ants are assigned as software agents to generate solutions. First of all, the manufacturing processes of the parts are graphically formulated as a disjunctive AND/OR graph. In applying the ACO algorithm, ants are deployed to find a path on the disjunctive graph. Such an ant route indicates a corresponding solution with associated operations scheduled by the sequence of ant visit. The ACO in this thesis is enhanced with the novel node selection heuristic and pheromone update strategy. With the node selection heuristic, pheromone is deposited on the nodes as well as edges on the ant path. This is contrast to the conventional ACO algorithm that pheromone is only deposited on edges. In addition, a more reasonable strategy based on "earliest completion time" of operations are used to determine the heuristic desirability of ants, instead of the "greedy" strategy used in standard ACO, which is based on the "shortest processing time." The approach is evaluated by a comprehensive set of problems with a full set of flexibilities, while multiple performance measurements are considered, including makespan, mean flow time, average machine utilization and CPU time, among which makespan is the major criterion. The results are compared with other approaches and encouraging improvements on solution quality could be observed. DOI: 10.5353/th_b4961806 Subjects: Production planning - Mathematical models Production scheduling - Mathematical models Multiagent sy
Author: Sicheng Zhang
Publisher: Open Dissertation Press
ISBN: 9781361305997
Category :
Languages : en
Pages :
Get Book Here
Book Description
This dissertation, "An Enhanced Ant Colony Optimization Approach for Integrating Process Planning and Scheduling Based on Multi-agent System" by Sicheng, Zhang, 张思成, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Process planning and scheduling are two important manufacturing planning functions which are traditionally performed separately and sequentially. Usually, the process plan has to be prepared first before scheduling can be performed. However, due to the complexity of manufacturing systems and the uncertainties and dynamical changes encountered in practical production, process plans and schedules may easily become inefficient or even infeasible. The concept of integrated process planning and scheduling (IPPS) has been proposed to improve the efficiency, effectiveness as well as flexibility of the respective process plan and schedule. By combining both functions together, the process plan for producing a part could be dynamically arranged in accordance with the availability of manufacturing resources and current status of the system, and its operations' schedule could be determined concurrently. Therefore, IPPS could provide an essential solution to the dynamic process planning and scheduling problem in the practical manufacturing environment. Nevertheless, process planning and scheduling are both complex functions that depend on many factors and flexibilities in the manufacturing system, IPPS is therefore a highly complex NP-hard problem. Ant colony optimization (ACO) is a widely applied meta-heuristics, which has been proved capable of generating feasible solutions for IPPS problem in previous research. However, due to the nature of the ACO algorithm, the performance is not that favourable compared with other heuristics. This thesis presents an enhanced ACO approach for IPPS. The weaknesses and limitations of standard ACO algorithm are identified and corresponding modifications are proposed to deal with the drawbacks and improve the performance of the algorithm. The mechanism is implemented on a specifically designed multi-agent system (MAS) framework in which ants are assigned as software agents to generate solutions. First of all, the manufacturing processes of the parts are graphically formulated as a disjunctive AND/OR graph. In applying the ACO algorithm, ants are deployed to find a path on the disjunctive graph. Such an ant route indicates a corresponding solution with associated operations scheduled by the sequence of ant visit. The ACO in this thesis is enhanced with the novel node selection heuristic and pheromone update strategy. With the node selection heuristic, pheromone is deposited on the nodes as well as edges on the ant path. This is contrast to the conventional ACO algorithm that pheromone is only deposited on edges. In addition, a more reasonable strategy based on "earliest completion time" of operations are used to determine the heuristic desirability of ants, instead of the "greedy" strategy used in standard ACO, which is based on the "shortest processing time." The approach is evaluated by a comprehensive set of problems with a full set of flexibilities, while multiple performance measurements are considered, including makespan, mean flow time, average machine utilization and CPU time, among which makespan is the major criterion. The results are compared with other approaches and encouraging improvements on solution quality could be observed. DOI: 10.5353/th_b4961806 Subjects: Production planning - Mathematical models Production scheduling - Mathematical models Multiagent sy
Author: Sicheng Zhang
Publisher:
ISBN:
Category : Ant algorithms
Languages : en
Pages : 274
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Author: Sicheng Zhang
Publisher:
ISBN:
Category : Ant algorithms
Languages : en
Pages : 0
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Author: Rakesh Kumar Phanden
Publisher: CRC Press
ISBN: 0429667442
Category : Technology & Engineering
Languages : en
Pages : 226
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Book Description
Both process planning and scheduling are very important functions of manufacturing, which affect together the cost to manufacture a product and the time to deliver it. This book contains various approaches proposed by researchers to integrate the process planning and scheduling functions of manufacturing under varying configurations of shops. It is useful for both beginners and advanced researchers to understand and formulate the Integration Process Planning and Scheduling (IPPS) problem effectively. Features Covers the basics of both process planning and scheduling Presents nonlinear approaches, closed-loop approaches, as well as distributed approaches Discuss the outfit of IPPS in Industry 4.0 paradigm Includes the benchmarking problems on IPPS Contains nature-algorithms and metaheuristics for performance measurements in IPPS Presents analysis of energy-efficient objective for sustainable manufacturing in IPPS
Author: Chun-Wai David Leung
Publisher: Open Dissertation Press
ISBN: 9781361476680
Category :
Languages : en
Pages :
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Book Description
This dissertation, "An Agent-based Approach for Integrating Process Planning and Scheduling" by Chun-wai, David, Leung, 梁俊偉, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled An Agent-Based Approach for Integrating Process Planning and Scheduling Submitted by David Chun Wai Leung for the degree of Doctor of Philosophy at The University of Hong Kong August 2006 Conventionally, manufacturing process planning and scheduling functions are performed separately and sequentially. In general, process plans have to be determined prior to scheduling. Due to the complexity of manufacturing systems, process plans and schedules may easily become inefficient or even infeasible. In this respect, integrated process planning and scheduling (IPPS) plays an important role in the generation of feasible process plans and optimal schedules. With IPPS, the process plan for producing a part and its operations' schedule are determined concurrently, taking into account process planning flexibilities and scheduling requirements. This integrative approach can address the actual and practical needs of industry, but due to the flexibilities in the manufacturing systems IPPS is a very complex problem. This thesis presents an agent-based approach for integrating process planning with scheduling. Firstly, a simple multi-agent system (MAS) architecture using the Multi-Agent Negotiation (MAN) approach is proposed to solve IPPS problems. Under MAN, part agents (PAs) and machine agents (MAs), which represent parts and machines respectively, determine the actual process plan and schedule via a currency-based negotiation. Each PA is given with an AND/OR graph recording all flexible routings and alternative machines of a product. It enters the system with some fictitious currency incorporating important IPPS parameters including processing time and due date and bargains with MAs to purchase their processing capability. These agents aim to achieve their individual objectives so as to minimize the parts' flowtimes and to maximize machine utilization. A contract-net based negotiation protocol is introduced to coordinate their negotiation. A hybrid MAS architecture using Hybrid-based Agent Negotiation (HAN) approach is then proposed to improve the global objectives of IPPS. HAN is similar to MAN except that a supervisory agent (SA) is added. The primary role of the SA is to observe and guarantee the global objectives which are neglected in MAN. The SA can access and intervene in the local decisions made by the local agents. A hybrid contract net negotiation protocol is established to facilitate negotiations in HAN. Dynamic process planning and rescheduling problems have also been addressed. Online Multi-Agent Negotiation (oMAN) and Online Hybrid-based Agent Negotiation (oHAN) are established to solve the dynamic IPPS problems based on MAN and HAN respectively. Two types of disturbances (machine breakdowns and rush order arrivals) are investigated. A simulated manufacturing system is established to evaluate the effectiveness of MAN and HAN and oMAN and oHAN approaches. Simulation results show that the performance of these methods exceeds that of an evolutionary algorithm in general. On the other hand, in terms of achieving the global objectives, the hybrid approaches HAN and oHAN are the most effective, outperforming MAN and oMAN respectively. Finally, a search-based Ant Colony Optimization (ACO) algorithm is proposed and is incorporated into the established MAS platform. Artificial ants are implemented as software agents. A graph-based solution method is pr
Author: Rodrigo Righi
Publisher: BoD – Books on Demand
ISBN: 9533079355
Category : Technology & Engineering
Languages : en
Pages : 246
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Book Description
Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume.
Author: Jie Zhang
Publisher: John Wiley & Sons
ISBN: 1118890094
Category : Technology & Engineering
Languages : en
Pages : 368
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Book Description
At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today’s competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control Written by an author with more than 20 years’ experience in studying and formulating a complete theoretical system in production planning technologies Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.
Author: Sze-Yuen Wan
Publisher: Open Dissertation Press
ISBN: 9781361306017
Category :
Languages : en
Pages :
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Book Description
This dissertation, "Integrated Process Planning and Scheduling With Setup Time Consideration by Ant Colony Optimization" by Sze-yuen, Wan, 溫思源, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In recent years, lots of research effort was spent on the integration of process planning and job-shop scheduling. Various integrated process planning and scheduling (IPPS) models and solution approaches have been proposed. The previous and existing research approaches are able to demonstrate the feasibility of implementing IPPS. However, most of them assumed that setup time is negligible or only part of the processing time. For machined parts, the setup for each operation includes workpiece loading and unloading, tool change, etc. For setup that depends only on the operation to be processed (sequence-independent), it is applicable to adopt the assumption of not considering setup in IPPS. For setup that depends on both the operation to be processed and the immediately preceding operation (sequence-dependent), it is an oversimplification to adopt such assumption. In such cases, the setup time varies with the sequence of the operations. The process plans and schedules constructed under such assumption are not realistic or not even feasible. In actual practice, therefore, the setup time should be separated from the process time in performing the IPPS functions. In this thesis, a new approach is proposed for IPPS problems with setup time consideration for machined parts. Inseparable and sequence-dependent setup requirements are added into the IPPS problems. The setup times are separated from the process times and they vary with the sequence of the operations. IPPS is regarded as NP-hard problem. With the separated consideration of setup times, it becomes even more complicated. An Ant Colony Optimization (ACO) approach is proposed to handle this complicated problem. The system is constructed under a multi-agent system (MAS). AND/OR graph is used to record the set of feasible production procedures and sequences. The ACO algorithm computes results by an autocatalytic process with the objective to minimize the makespan. Software agents called "artificial ants" traverse through the feasible routes in the graph and finally construct a schedule. A setup time parameter is added into the algorithm to influence the ants to select the process with less setup time. The approach is able to construct a feasible solution with less setup time. Experimental studies have been performed to evaluate the performance of MAS-ACO approach in solving IPPS problems with separated consideration of setup times. The experimental results show that the MAS-ACO approach can effectively handle the problem. DOI: 10.5353/th_b4961807 Subjects: Production planning - Mathematical models Production scheduling - Mathematical models Ant algorithms
Author: Jinfeng Wang
Publisher: Springer Nature
ISBN: 9819920116
Category : Technology & Engineering
Languages : en
Pages : 241
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Book Description
This book focuses on the basic common technologies of smart manufacturing system and smart workshop. The applications of artificial intelligence in manufacturing system have been addressed from different perspectives, for example, smart optimization of cutting parameters, smart process planning, smart workshop and scheduling, and smart integration of process planning and scheduling. In the process of writing, this book highlights the combination of basic theory and engineering practice. In the basic theory part, the book clearly explains the basic issues of smart manufacturing system, including the core support technology of smart manufacturing, the basic theories and models of cutting parameter optimization, process optimization and scheduling, and the basic concepts and intelligence of smart manufacturing workshop model, optimization methods, etc. In the engineering practice part, this book enumerates a large number of research cases, trying to clearly demonstrate the basic problems of manufacturing system intelligence, and each chapter is accompanied by typical cases to help readers better understand and master the basic theories involved in stamping.
Author: Wenhui Fan
Publisher: Springer Nature
ISBN: 9811991987
Category : Computers
Languages : en
Pages : 648
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Book Description
The two-volume set CCIS 1712 and 1713 constitutes the proceedings of the 21st Asian Simulation Conference, AsiaSim 2022, which took place in Changsha, China, in January 2023. Due to the Covid pandemic AsiaSim 2022 has been postponed to January 2023. The 97 papers presented in the proceedings were carefully reviewed and selected from 218 submissions. The contributions were organized in topical sections as follows: Modeling theory and methodology; Continuous system/discrete event system/hybrid system/intelligent system modeling and simulation; Complex systems and open, complex and giant systems modeling and simulation; Integrated natural environment and virtual reality environment modeling and simulation; Networked Modeling and Simulation; Flight simulation, simulator, simulation support environment, simulation standard and simulation system construction; High performance computing, parallel computing, pervasive computing, embedded computing and simulation; CAD/CAE/CAM/CIMS/VP/VM/VR/SBA; Big data challenges and requirements for simulation and knowledge services of big data ecosystem; Artificial intelligence for simulation; Application of modeling/simulation in science/engineering/society/economy /management/energy/transportation/life/biology/medicine etc; Application of modeling/simulation in energy saving/emission reduction, public safety, disaster prevention/mitigation; Modeling/simulation applications in the military field; Modeling/simulation applications in education and training; Modeling/simulation applications in entertainment and sports.
