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Author: Dachao Li
Publisher: OAE Publishing Inc.
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 26
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Book Description
End-of-life products recycling can reduce the waste of resources, and disassembly line scheduling planning can effectively improve the recycling efficiency and reduce the pollution of the environment. This work addresses a bi-objective disassembly line scheduling problem with considering time interference between tasks. The weighted sum of the cycle time and hazard coefficients is optimized. First, a mathematical model of the disassembly line scheduling problem is established under the constraints of priority and time interference relationships. Second, four meta-heuristics are improved to solve the concerned problems, including particle swarm optimization, artificial bee colony, genetic algorithm and variable neighborhood search. Ten objective-oriented local search operations are designed for improving meta-heuristics’ performance. A reinforcement learning algorithm, Sarsa, is employed to guide task assignment among workstations and local search selection during iterations, respectively. Finally, experiments are carried out for 10 instances with different scales. The effectiveness of the improving strategies is verified; the meta-heuristics combined with Sarsa based task assignment and local search strategies has better robustness and stability than the classical ones. Comparisons and discussions show that the particle swarm optimization with improved strategies outperforms other algorithms.
Author: Dachao Li
Publisher: OAE Publishing Inc.
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 26
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Book Description
End-of-life products recycling can reduce the waste of resources, and disassembly line scheduling planning can effectively improve the recycling efficiency and reduce the pollution of the environment. This work addresses a bi-objective disassembly line scheduling problem with considering time interference between tasks. The weighted sum of the cycle time and hazard coefficients is optimized. First, a mathematical model of the disassembly line scheduling problem is established under the constraints of priority and time interference relationships. Second, four meta-heuristics are improved to solve the concerned problems, including particle swarm optimization, artificial bee colony, genetic algorithm and variable neighborhood search. Ten objective-oriented local search operations are designed for improving meta-heuristics’ performance. A reinforcement learning algorithm, Sarsa, is employed to guide task assignment among workstations and local search selection during iterations, respectively. Finally, experiments are carried out for 10 instances with different scales. The effectiveness of the improving strategies is verified; the meta-heuristics combined with Sarsa based task assignment and local search strategies has better robustness and stability than the classical ones. Comparisons and discussions show that the particle swarm optimization with improved strategies outperforms other algorithms.
Author: Bassem Jarboui
Publisher: John Wiley & Sons
ISBN: 1848214979
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France. Jacques Teghem is Professor at the University of Mons, Belgium.
Author: Seamus M. McGovern
Publisher: McGraw Hill Professional
ISBN: 0071626050
Category : Technology & Engineering
Languages : en
Pages : 397
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Book Description
The definitive guide to the disassembly line The Disassembly Line: Balancing and Modeling provides in-depth information on this complex process essential to remanufacturing, recycling, and environmentally conscious manufacturing. This pioneering work offers efficient techniques required to solve problems involving the number of workstations required and the disassembly sequencing of end-of-life products on the disassembly line. In this book, the disassembly line balancing problem (DLBP) is described, defined mathematically, and illustrated by case studies. Combinatorial optimization methodologies are presented as solutions to the DLBP. Coverage includes: Graphical representations of products to be disassembled Computational complexity of combinatorial problems Description of the disassembly line and the mathematical model Computational complexity of the DLBP Combinatorial optimization searches Experimental instances Analytical methodologies Exhaustive search Genetic algorithm Ant colony optimization Greedy algorithm Greedy/adjacent element hill climbing hybrid Greedy/2-opt hybrid H-K heuristic Quantitative and qualitative comparative analysis This authoritative volume also covers product planning, line and facility design, sequencing and scheduling, inventory, just in time, revenue, and unbalanced lines.
Author: Aidin Delgoshaei
Publisher: Academic Press
ISBN: 0128204249
Category : Business & Economics
Languages : en
Pages : 512
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Book Description
Meta-Heuristic Algorithms in Production Engineering is a ground-breaking exploration of the new ways that meta-heuristic algorithms can be used to optimize planning and scheduling problems in production engineering. Putting equal emphasis on mathematics and engineering knowledge, this book provides the perfect guide to using meta-heuristics in a range of innovative ways, addressing problems in areas like facility location, location-allocation, job shop-scheduling, flow shop-scheduling, and group technology. To start with, the author draws on production engineering knowledge to help the reader to analyse and frame their problem for solution. Next, the different algorithms are dealt with in detail, starting with the meta-heuristic, its logic, and its standard mechanism. Then the very latest applications are described, before fully worked examples are provided with MATLAB code. This invaluable resource also acts as a review of the latest advances in meta-heuristics including genetic algorithm, simulated annealing, tabu search, ant colony optimization, particle swarm methods, and artificial neural networks. Provides step-by-step instructions to applying meta-heuristics algorithms to real production engineering problems Explores numerous pioneering applications of meta-heuristics in industrial engineering and industrial management Examines a wide range of algorithms, enabling comparison and informing selection decisions · Includes Matlab codes that will save users many hours of their own time and effort
Author: Luping Zhang
Publisher: Open Dissertation Press
ISBN: 9781361370551
Category :
Languages : en
Pages :
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Book Description
This dissertation, "Solving Integrated Process Planning and Scheduling Problems With Metaheuristics" by Luping, Zhang, 张路平, 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 closely related to each other. Usually, process planning and scheduling have to be performed sequentially, whereby the process plans are the input for scheduling. Many investigations have shown that the separate conduction of the two functions is much likely to ruin the effectiveness and feasibility of the process plans and schedules, and it is also difficult to cater for the occurrence of uncertainties in the dynamic manufacturing environment. The purpose of integrated process planning and scheduling (IPPS) is to perform the two functions concurrently. IPPS is a typical combinatorial optimization problem which belongs to the category of NP-hard problems. Research on IPPS has intensified in recent years. Researchers have reported various IPPS systems and solution approaches which are able to generate good solutions for specific IPPS problems. However, there is in general an absence of theoretical models for the IPPS problem representation, and research on the theoretical aspects of the IPPS is limited. The objective of this research is to establish a metaheuristic-based solution approach for the IPPS problem in flexible jobshop type of manufacturing systems. To begin with, a graph-based modeling approach for formulating the IPPS problem domain is proposed. This approach defines a way to use a category of AND/OR graphs to construct IPPS models. The graph-based IPPS model can be formulated using mathematical programming tools including polynomial mixed integer programming (PMIP) and mixed integer linear programming (MILP). The analytical mathematical programming approaches can be used to solve simple IPPS instances but they are not capable for large-scale IPPS problems. This research proposes a new IPPS modelling approach to incorporate metaheuristics in the solution strategy. Actually, the solution strategy comprises the metaheuristics and a mapping function. The metaheuristic is responsible for generating the operation sequences; a mapping function is then used to assign the operations to appropriate time slots on a schedule. General studies of applying constructive and improvement metaheuristics to solve the IPPS problem are conducted in this research. The ant colony optimization (ACO) is applied as a representative constructive metaheuristic, and a nonstandard genetic algorithm approach object-coding genetic algorithm (OCGA) is implemented as an improvement metaheuristic. The OCGA contains dedicated genetic operations to support the object-based genetic representation, and three particular mechanisms for population evolution. The metaheuristic-based solution approaches are implemented in a multi-agent system (MAS) platform. The hybrid MAS and metaheuristics based IPPS solution methodology is able to carry out dynamic rescheduling to cope with occurrence of uncertainties in practical manufacturing environments. Experiments have been carried out to test the IPPS solution approach proposed in this thesis. It is shown that both metaheuristics, ACO and OCGA, are having good performance in terms of solution quality and computational efficiency. In particular, due to the special genetic operations and population evolutionary mechanisms, the OCGA shows great advantages in experiments on benchmark problems. Finally, it is shown that the hybrid approach of MSA and metaheuristics is
Author: M. Surico
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.
Author: Turan Paksoy
Publisher: CRC Press
ISBN: 1000245101
Category : Technology & Engineering
Languages : en
Pages : 369
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Book Description
Industrial revolutions have impacted both, manufacturing and service. From the steam engine to digital automated production, the industrial revolutions have conduced significant changes in operations and supply chain management (SCM) processes. Swift changes in manufacturing and service systems have led to phenomenal improvements in productivity. The fast-paced environment brings new challenges and opportunities for the companies that are associated with the adaptation to the new concepts such as Internet of Things (IoT) and Cyber Physical Systems, artificial intelligence (AI), robotics, cyber security, data analytics, block chain and cloud technology. These emerging technologies facilitated and expedited the birth of Logistics 4.0. Industrial Revolution 4.0 initiatives in SCM has attracted stakeholders’ attentions due to it is ability to empower using a set of technologies together that helps to execute more efficient production and distribution systems. This initiative has been called Logistics 4.0 of the fourth Industrial Revolution in SCM due to its high potential. Connecting entities, machines, physical items and enterprise resources to each other by using sensors, devices and the internet along the supply chains are the main attributes of Logistics 4.0. IoT enables customers to make more suitable and valuable decisions due to the data-driven structure of the Industry 4.0 paradigm. Besides that, the system’s ability of gathering and analyzing information about the environment at any given time and adapting itself to the rapid changes add significant value to the SCM processes. In this peer-reviewed book, experts from all over the world, in the field present a conceptual framework for Logistics 4.0 and provide examples for usage of Industry 4.0 tools in SCM. This book is a work that will be beneficial for both practitioners and students and academicians, as it covers the theoretical framework, on the one hand, and includes examples of practice and real world.
Author: David Edward Goldberg
Publisher: Addison-Wesley Professional
ISBN:
Category : Computers
Languages : en
Pages : 436
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Book Description
A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
Author: Margarita N. Favorskaya
Publisher: Springer Nature
ISBN: 9811546924
Category : Technology & Engineering
Languages : en
Pages : 775
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Book Description
The book is a compilation of selected papers from 2020 International Conference on Electrical and Electronics Engineering (ICEEE 2020) held in National Power Training Institute HQ (Govt. of India) on February 21 – 22, 2020. The work focuses on the current development in the fields of electrical and electronics engineering like power generation, transmission and distribution, renewable energy sources and technology, power electronics and applications, robotics, artificial intelligence and IoT, control, and automation and instrumentation, electronics devices, circuits and systems, wireless and optical communication, RF and microwaves, VLSI, and signal processing. The book is beneficial for readers from both academia and industry.
Author: Salah Kabanda
Publisher: Springer
ISBN: 3030058131
Category : Education
Languages : en
Pages : 383
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Book Description
This book constitutes the refereed proceedings of the 47th Annual Conference of the Southern African Computer Lecturers' Association on ICT Education, SACLA 2018, held in Gordon's Bay, South Africa, in June 2018. The 23 revised full papers presented together with an extended abstract of a keynote paper were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections: playfulness, media and classrooms, academia and careers, teaching programming, adaptation and learning, teamwork and projects, learning systems, topic teaching.
