SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling.The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

[1]  Jaime Cerdá,et al.  Knowledge-based predictive and reactive scheduling in industrial environments , 2000 .

[2]  Jan Van Damme,et al.  Project scheduling under uncertainty survey and research potentials , 2002 .

[3]  Stephen J. Childe,et al.  Editorial Six things to manage – Operators , 2004 .

[4]  Ihsan Sabuncuoglu,et al.  Analysis of reactive scheduling problems in a job shop environment , 2000, Eur. J. Oper. Res..

[5]  J. Sun,et al.  A dynamic reactive scheduling mechanism for responding to changes of production orders and manufacturing resources , 2001, Comput. Ind..

[6]  Djamila Ouelhadj,et al.  Multi-agent architecture for distributed monitoring in flexible manufacturing systems (FMS) , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  A. Tharumarajah,et al.  Approaches and issues in scheduling a distributed shop-floor environment , 1997 .

[8]  Reha Uzsoy,et al.  Rolling horizon procedures for dynamic parallel machine scheduling with sequence-dependent setup times. , 1995 .

[9]  George Rzevski,et al.  Magenta Multi-Agent Systems for Dynamic Scheduling , 2009, ICAART.

[10]  James J. Solberg,et al.  INTEGRATED SHOP FLOOR CONTROL USING AUTONOMOUS AGENTS , 1992 .

[11]  Anil K. Jain,et al.  PRODUCTION SCHEDULING/RESCHEDULING IN FLEXIBLE MANUFACTURING , 1997 .

[12]  P. Cowling,et al.  Contract Net Protocol for Cooperative Optimisation and Dynamic Scheduling of Steel Production , 2003 .

[13]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[14]  M. Selim Akturk,et al.  Match-up scheduling under a machine breakdown , 1999, Eur. J. Oper. Res..

[15]  Shimon Y. Nof,et al.  Adaptive/predictive scheduling: review and a general framework , 1991 .

[16]  Jiyin Liu,et al.  Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling , 1993 .

[17]  Reha Uzsoy,et al.  Minimizing makespan on a single batch processing machine with dynamic job arrivals , 1999 .

[18]  Vincent C. S. Wiers,et al.  The complexity of scheduling in practice , 1996 .

[19]  Sanjay Mehta,et al.  Predictable scheduling of a single machine subject to breakdowns , 1999, Int. J. Comput. Integr. Manuf..

[20]  James O'Kane,et al.  A knowledge-based system for reactive scheduling decision-making in FMS , 2000, J. Intell. Manuf..

[21]  A. G. Lockett,et al.  Job shop scheduling heuristics and frequency of scheduling , 1982 .

[22]  Reha Uzsoy,et al.  Analysis of periodic and event-driven rescheduling policies in dynamic shops , 1992 .

[23]  Ihsan Sabuncuoglu,et al.  An analysis of heuristics in a dynamic job shop with weighted tardiness objectives , 1999 .

[24]  Mark S. Fox,et al.  Intelligent Scheduling , 1998 .

[25]  Mehmet Emin Aydin,et al.  Dynamic job-shop scheduling using reinforcement learning agents , 2000, Robotics Auton. Syst..

[26]  Sanjay B. Joshi,et al.  Dynamic single-machine scheduling under distributed decision-making , 2000 .

[27]  Pei-Chann Chang,et al.  One-machine rescheduling heuristics with efficiency and stability as criteria , 1993, Comput. Oper. Res..

[28]  Nicholas R. Jennings,et al.  Foundations of distributed artificial intelligence , 1996, Sixth-generation computer technology series.

[29]  Sanja Petrovic,et al.  Dynamic scheduling of steel casting and milling using multi-agents , 2004 .

[30]  Reha Uzsoy,et al.  Executing production schedules in the face of uncertainties: A review and some future directions , 2005, Eur. J. Oper. Res..

[31]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.

[32]  Mohsen Jahangirian,et al.  Intelligent dynamic scheduling system: the application of genetic algorithms , 2000 .

[33]  Weiming Shen,et al.  MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing , 2000, J. Intell. Manuf..

[34]  P. Brunn,et al.  Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach , 1999 .

[35]  Vcs Vincent Wiers,et al.  A review of the applicability of OR and AI scheduling techniques in practice , 1997 .

[36]  Ihsan Sabuncuoglu,et al.  Routing-based reactive scheduling policies for machine failures in dynamic job shops , 2001 .

[37]  J. A. Svestka,et al.  Rescheduling job shops under random disruptions , 1997 .

[38]  Michael J. Shaw,et al.  Dynamic scheduling in cellular manufacturing systems , 1987 .

[39]  Oliver Holthaus,et al.  Scheduling in job shops with machine breakdowns: an experimental study , 1999 .

[40]  James J. Solberg,et al.  An agent-based flexible routing manufacturing control simulation system , 1994, Proceedings of Winter Simulation Conference.

[41]  Marco Macchi,et al.  An experimental benchmarking of two multi-agent architectures for production scheduling and control , 2000 .

[42]  Djamila Ouelhadj,et al.  Multi-agent system for dynamic scheduling and control in manufacturing cells , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[43]  F. Frank Chen,et al.  The state of the art in intelligent real-time FMS control: a comprehensive survey , 1996, J. Intell. Manuf..

[44]  Heng Li,et al.  A production rescheduling expert simulation system , 2000, Eur. J. Oper. Res..

[45]  H. V. Parunak.  Chapter 10 – Manufacturing Experience with the Contract Net , 1987 .

[46]  A. Tharumarajah,et al.  Survey of resource allocation methods for distributed manufacturing systems , 2001 .

[47]  Peter Cowling,et al.  Production, Manufacturing and Logistics Using real time information for effective dynamic scheduling , 2002 .

[48]  Djamila Ouelhadj,et al.  A multi-contract net protocol for dynamic scheduling in flexible manufacturing systems (FMS) , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[49]  Steven Y. Goldsmith,et al.  An Autonomous Manufacturing Collective for Job Shop Scheduling , 1998 .

[50]  Mikkel T. Jensen,et al.  Improving robustness and flexibility of tardiness and total flow-time job shops using robustness measures , 2001, Appl. Soft Comput..

[51]  Xiaoli Wang,et al.  Minimizing makespan on a single batching machine with release times and non-identical job sizes , 2005, Oper. Res. Lett..

[52]  H. Van Dyke Parunak,et al.  The AARIA agent architecture: an example of requirements-driven agent-based system design , 1997, AGENTS '97.

[53]  Hong Zhou,et al.  The hybrid heuristic genetic algorithm for job shop scheduling , 2001 .

[54]  James C. Bean,et al.  Matchup Scheduling with Multiple Resources, Release Dates and Disruptions , 1991, Oper. Res..

[55]  Dipak Chaudhuri,et al.  Dynamic scheduling—a survey of research , 1993 .

[56]  Elizabeth Szelke,et al.  Artificial Intelligence in Reactive Scheduling , 1995, IFIP Advances in Information and Communication Technology.

[57]  I. M. Ovacikt,et al.  Rolling horizon algorithms for a single-machine dynamic scheduling problem with sequence-dependent setup times , 1994 .

[58]  Sanja Petrovic,et al.  Utility and stability measures for agent-based dynamic scheduling of steel continuous casting , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[59]  Sheik Meeran,et al.  Deterministic job-shop scheduling: Past, present and future , 1999, Eur. J. Oper. Res..

[60]  Agostino Villa,et al.  Modeling manufacturing systems , 1999 .

[61]  H. Van Dyke Parunak.  Agents in Overalls: Experiences and Issues in the Development and Deployment of Industrial Agent-Based Systems , 2000, Int. J. Cooperative Inf. Syst..

[62]  Elizabeth Szelke,et al.  Knowledge-based reactive scheduling , 1994 .

[63]  Neil A. Duffie,et al.  Nonhierarchical control of manufacturing systems , 1986 .

[64]  Günter Schmidt,et al.  How to Apply fuzzy Logic to Reactive Production Scheduling , 1993, Knowledge-Based Reactive Scheduling.

[65]  George Chryssolouris,et al.  Dynamic scheduling of manufacturing job shops using genetic algorithms , 2001, J. Intell. Manuf..

[66]  Khairy A.H. Kobbacy,et al.  Intelligent systems in manufacturing: current developments and future prospects , 2000 .

[67]  Subhash C. Sarin,et al.  Development of a knowledge-based system for dynamic scheduling , 1990 .

[68]  P. Brunn,et al.  Workshop scheduling using practical (inaccurate) data Part 2: An investigation of the robustness of scheduling rules in a dynamic and stochastic environment , 1999 .

[69]  Diego Andina,et al.  Application of Neural Networks , 2007 .

[70]  Jeffrey W. Herrmann,et al.  Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies , 2000 .

[71]  Robert W. Brennan,et al.  Evaluating the performance of reactive control architectures for manufacturing production control , 2001, Comput. Ind..

[72]  Sanja Petrovic,et al.  A multi-agent architecture for dynamic scheduling of steel hot rolling , 2003, J. Intell. Manuf..

[73]  Jeffrey W. Herrmann,et al.  Predicting the performance of rescheduling strategies for parallel machine systems , 2000 .

[74]  Reha Uzsoy,et al.  Predictable scheduling of a single machine with breakdowns and sensitive jobs , 1999 .

[75]  Fred W. Glover,et al.  Genetic algorithms and tabu search: Hybrids for optimization , 1995, Comput. Oper. Res..

[76]  Stephen F. Smith,et al.  Reactive Scheduling Systems , 1995 .

[77]  Peter Mertens,et al.  Combining knowledge-based systems and simulation to solve rescheduling problems , 1996, Decis. Support Syst..

[78]  Robert H. Storer,et al.  Robustness Measures and Robust Scheduling for Job Shops , 1994 .

[79]  J. Christopher Beck,et al.  Slack-based Techniques for Robust Schedules , 2014 .

[80]  Amitava Dutta.  Reacting to scheduling exceptions in FMS environments , 1990 .

[81]  H. Van Dyke Parunak,et al.  Applications of distributed artificial intelligence in industry , 1996 .

[82]  Weiming Shen,et al.  MetaMorph: An adaptive agent-based architecture for intelligent manufacturing , 1999 .

[83]  Jeffrey W. Herrmann,et al.  Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods , 2003, J. Sched..

[84]  Chandrasekharan Rajendran,et al.  A comparative study of dispatching rules in dynamic flowshops and jobshops , 1999, Eur. J. Oper. Res..

[85]  Jürgen Dorn,et al.  Reactive Scheduling in a Fuzzy-Temporal Framework , 1993, Knowledge-Based Reactive Scheduling.

[86]  Panagiotis Kouvelis,et al.  Robust scheduling to hedge against processing time uncertainty in single-stage production , 1995 .

[87]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[88]  Jürgen Dorn,et al.  Case-based reactive scheduling , 1995 .

[89]  Parag C. Pendharkar,et al.  A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments , 1999 .

[90]  Kazuo Miyashita,et al.  CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair , 1995, Artif. Intell..

[91]  Carlos Ramos,et al.  A distributed architecture and negotiation protocol for scheduling in manufacturing systems , 1999 .

[92]  Tuomas Sandholm,et al.  Automated contracting in distributed manufacturing among independent companies , 2000, J. Intell. Manuf..

[93]  Yeong-Dae Kim,et al.  Simulation-based real-time scheduling in a flexible manufacturing system , 1993 .

[94]  Shimon Y. Nof,et al.  Scheduling/rescheduling in the manufacturing operating system environment† , 1985 .

[95]  Duc Truong Pham,et al.  Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks , 2011 .

[96]  Sadiq M. Sait,et al.  Evolutionary algorithms, simulated annealing, and Tabu search: a comparative study , 1998, Optics & Photonics.

[97]  Weiming Shen,et al.  Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing , 2000 .

[98]  Grzegorz Waligóra,et al.  Local search metaheuristics for discrete-continuous scheduling problems , 1998, Eur. J. Oper. Res..

[99]  Jürgen Dorn,et al.  Reactive scheduling: improving the robustness of schedules and restricting the effects of shop floor disturbances by fuzzy reasoning , 1995, Int. J. Hum. Comput. Stud..

[100]  Botond Kádár,et al.  Hierarchy in distributed shop floor control , 2000 .

[101]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[102]  Luis Puigjaner,et al.  On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants , 2001 .

[103]  Willy Herroelen,et al.  The complexity of machine scheduling for stability with a single disrupted job , 2005, Oper. Res. Lett..

[104]  Pei-Chann Chang,et al.  A Rescheduling Procedure for Manufacturing Systems Under Random Disruptions , 1992 .

[105]  M. Garetti,et al.  Using neural networks for reactive scheduling , 1995 .

[106]  Ihsan Sabuncuoglu,et al.  A STUDY OF SCHEDULING RULES OF FLEXIBLE MANUFACTURING SYSTEMS : A SIMULATION APPROACH , 1998 .

[107]  Kyo-Il Lee,et al.  An intelligent operations scheduling system in a job shop , 1996 .

[108]  Ranga V. Ramasesh.  Dynamic job shop scheduling: A survey of simulation research , 1990 .

[109]  Carlos Ramos,et al.  An architecture and a negotiation protocol for the dynamic scheduling of manufacturing systems , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[110]  Ihsan Sabuncuoglu,et al.  Rescheduling frequency in an FMS with uncertain processing times and unreliable machines , 1999 .

[111]  Peter B. Luh,et al.  Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation , 1997, Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[112]  D. Petrovic,et al.  A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions , 2006, Fuzzy Sets Syst..

[113]  Andrea Rossi,et al.  Dynamic scheduling of FMS using a real-time genetic algorithm , 2000 .

[114]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.