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Author: Sze-Yuen Wan
Publisher: Open Dissertation Press
ISBN: 9781361306024
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: Sze-Yuen Wan
Publisher: Open Dissertation Press
ISBN: 9781361306024
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: Sze-yuen Wan
Publisher:
ISBN:
Category : Ant algorithms
Languages : en
Pages : 0
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Book Description
Author: Sze-yuen Wan
Publisher:
ISBN:
Category : Ant algorithms
Languages : en
Pages : 197
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Book Description
Author: Halil Ibrahim Demir
Publisher: CRC Press
ISBN: 1000919714
Category : Technology & Engineering
Languages : en
Pages : 189
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Book Description
Traditionally, the three most important manufacturing functions are process planning, scheduling, and due-date assignment, which are handled sequentially and separately.This book integrates these manufacturing processes and functions to increase global performance along with manufacturing and production cost savings. Integrated Process Planning, Scheduling, and Due-Date Assignment combines the most important manufacturing functions to use manufacturing resources better, reduce production costs, and eliminate bottlenecks with increased production efficiency. The book covers how the integration will help eliminate scheduling conflicts and how to adapt to irregular shop floor disturbances. It also explains how other elements, such as tardiness and earliness, are penalized and how prioritizing helps improve weight performance function. This book will draw the interest of professionals, students, and academicians in process planning, scheduling, and due-date assignment. It could also be supplemental material for manufacturing courses in industrial engineering and manufacturing engineering departments.
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: 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: Sicheng Sulivan Zhang
Publisher: Open Dissertation Press
ISBN: 9781361013656
Category : Technology & Engineering
Languages : en
Pages : 252
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Book Description
This dissertation, "Multi-objective integrated process planning and scheduling: a hybrid MAS/ACO approach" by Sicheng, Sulivan, 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 encountered in practical production, the sequentially prepared process plans and schedules may sometimes become inefficient or even infeasible. Integrated process planning and scheduling (IPPS) is a relatively new concept in manufacturing domain, which aims at improving the feasibility and optimality of the respective process plan and schedule, by combining both functions together. Scheduling itself is an NP-hard problem. With the complexity and flexibility involved by the incorporation of process planning function, IPPS is an even more difficult problem which is almost impossible to be solved by traditional optimisation methods. In this thesis, a graphical formulation based on disjunctive AND/OR graphs, as well as a mixed integer programming model are presented for representing IPPS problems. Inspired by the negotiation-based multi-agent system (MAS) and ant colony optimisation (ACO) approaches, a novel hybrid MAS/ACO (HMA) approach is developed for solving IPPS problems. The approach is implemented on an MAS with a number of solution generator agents, each controlling a series of jobs agents and machine agents, negotiating with each other to realise the step-by-step assignment of operations, and finally yielding a feasible solution to the problem. In order to improve the performance, the pheromone accumulation, and evaporation mechanism in ACO are introduced into the negotiation process; the probabilistic and iterative features of ACO are also inherited to make the approach non-deterministic, and hence capable of progressively improving the solution quality as a meta-heuristic. For multi-objective optimisation, correlations between different measures of scheduling problems have been experimentally and statistically studied; makespan and total tardiness are then chosen as the objectives to be simultaneously optimised. Furthermore, other extensions of IPPS including the incorporation of sequence dependent setup times, assembly operations, as well as dynamic rescheduling under disruptions, have also been studied and given solutions with the HMA approach. Experiments have been carried out to select an appropriate parameter set, and to evaluate and illustrate the applicability and effectiveness of the approach. The experimental results show that the HMA approach is a competitive and reliable method for solving IPPS problems. Subjects: Production scheduling - Mathematical models Production planning - Mathematical models
Author: Hoai An Le Thi
Publisher: Springer
ISBN: 331918167X
Category : Technology & Engineering
Languages : en
Pages : 497
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Book Description
This proceedings set contains 85 selected full papers presentedat the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11–13, 2015 at Lorraine University, France. The present part II of the 2 volume set includes articles devoted to Data analysis and Data mining, Heuristic / Meta heuristic methods for operational research applications, Optimization applied to surveillance and threat detection, Maintenance and Scheduling, Post Crises banking and eco-finance modelling, Transportation, as well as Technologies and methods for multi-stakeholder decision analysis in public settings.
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: Marco Dorigo
Publisher: MIT Press
ISBN: 9780262042192
Category : Computers
Languages : en
Pages : 324
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Book Description
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
