Advanced Cascaded Scheduling for Highly Autonomous Production Cells with Material Flow and Tool Lifetime Consideration using AGVs
1
Institute of Digital Engineering, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany
Submission date: 2023-07-27
Final revision date: 2023-08-30
Acceptance date: 2023-08-31
Online publication date: 2023-09-06
Publication date: 2023-09-30
Corresponding author
Eddi Miller
Institute of Digital Engineering, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany
Institute of Digital Engineering, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany
Journal of Machine Engineering 2023;23(3):69-85
KEYWORDS
TOPICS
ABSTRACT
In today’s manufacturing systems, especially in Industry 4.0, highly autonomous production cells play an important role. To reach this goal of autonomy, different technologies like industrial robots, machine tools, and automated guided vehicles (AGV) are deployed simultaneously which creates numerous challenges on various automation levels. One of those challenges regards the scheduling of all applied resources and their corresponding tasks. Combining data from a real production environment and Constraint Programming (CP-SAT), we provide a cascaded scheduling approach that plans production orders for machine tools to minimize makespan and tool changeover time while enabling the corresponding robot for robot-collaborated processes. Simultaneously, AGVs provide all production cells with the necessary material and tools. Hereby, magazine capacity for raw material as well as finished parts and tool service life are taken into account.
REFERENCES (30)
1.
YI J., DING S., SONG D., ZHANG M.T., 2008, Steady-State Throughput and Scheduling Analysis of Multicluster Tools: A Decomposition Approach, IEEE Transactions on Automation Science and Engineering, 5/2, 321–336.
2.
WU X., YUAN Q., WANG L., 2020, Multiobjective Differential Evolution Algorithm for Solving Robotic Cell Scheduling Problem with Batch-Processing Machines, IEEE Transactions on Automation Science and Engineering, 18/2, 757–775.
3.
YAN P., LIU S.Q., SUN T., MA K., 2018, A Dynamic Scheduling Approach for Optimizing the Material Handling Operations in a Robotic Cell, Computers & Operations Research, 99, 166–177.
4.
SONMEZ A., BAYKASOGLU A., 1998, A new dynamic programming formulation of (nxm) flowshop sequencing problems with due dates, International Journal of Production Research, 36/8, 2269–2283.
6.
BLAZEWICZ J., EISELT H.A., FINKE G., LAPORTE G., WEGLARZ J., 1991, Scheduling Tasks and Vehicles in a Flexible Manufacturing System, International Journal of Flexible Manufacturing Systems, 4/1, 5–16.
7.
VALLADA E., RUIZ R., 2011, A Genetic Algorithm for the Unrelated Parallel Machine Scheduling Problem with Sequence Dependent Setup Times, European Journal of Operational Research, 211/3, 612–622.
8.
ABDULKADER M., ELBEHEIRY M., AFIA N., EL-KHARBOTLY A., 2013, Scheduling and Sequencing in Four Machines Robotic Cell: Application of Genetic Algorithm and Enumeration Techniques, Ain Shams Engineering Journal, 4/3, 465–474.
9.
LACOMME P., LARABI M., TCHERNEV N., 2013, Job-Shop Based Framework for Simultaneous Scheduling of Machines and Automated Guided Vehicles, International Journal of Production Economics, 143/1, 24–34.
10.
WILSON A.J., PALLAVI D., RAMACHANDRAN M., CHINNASAMY S., SOWMIYA S., 2022, A Review on Memetic Algorithms and its Developments, Electrical and Automation Engineering, 1/1, 7–12.
11.
ZARANDI M.F., MOSADEGH H., FATTAHI M., 2013, Two-Machine Robotic Cell Scheduling Problem with Sequence-Dependent Setup Times, Computers & Operations Research, 40/5, 1420–1434.
12.
GUNDOGDU E., GULTEKIN H., 2016, Scheduling in Two-Machine Robotic Cells with a Self-Buffered Robot, IIE Transactions, 48/2, 170–191.
13.
NOURI H.E., DRISS O.B., GHE´DIRA K., 2016, Simultaneous Scheduling of Machines and Transport Robots in Flexible Job Shop Environment Using Hybrid Metaheuristics Based on Clustered Holonic Multiagent Model, Computers & Industrial Engineering, 102, 488–501.
14.
ZABIHZADEH S.S., REZAEIAN J., 2016, Two Meta-Heuristic Algorithms for Flexible Flow Shop Scheduling Problem with Robotic Transportation and Release Time, Applied Soft Computing, 40, 319–330.
15.
GHADIRI NEJAD M., KOVACS G., VIZVARI B., BARENJI R.V., 2018, An Optimization Model for Cyclic Scheduling Problem in Flexible Robotic Cells, The International Journal of Advanced Manufacturing Technology, 95/9, 3863–3873.
16.
LI X., YANG X., ZHAO Y., TENG Y., DONG Y., 2020, Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell, IEEE Access, 8, 147015–147028.
17.
ZHOU B., LI M., 2017, Scheduling Method of Robotic Cells with Robot-Collaborated Process and Residency Constraints, International Journal of Computer Integrated Manufacturing, 30/11, 1164–1178.
18.
REDDY N.S., RAMAMURTHY D., RAO K.P., LALITHA M.P., 2021, Practical Simultaneous Scheduling of Machines, Agvs, Tool Transporter and Tools in a Multi Machine FMS Using Symbiotic Organisms Search Algorithm, International Journal of Computer Integrated Manufacturing, 34/2, 153–174.
19.
MILLER E., KAUPP T., SCHMITT J., 2022, Cascaded Scheduling for Highly Autonomous Production Cells with AGVs, Global Conference on Sustainable Manufacturing, 383–390.
20.
FERENCZI B.T., KOCZY L., LILIK F., 2022, Fuzzy Signature Based Model in Material Handling Management, Computational Intelligence and Mathematics for Tackling Complex Problems, 4, 169–179.
21.
CHAWLA V., CHANDA A., ANGRA S., 2018, Multi-Load Agvs Scheduling by Application of Modified Memetic Particle Swarm Optimization Algorithm, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 1–13.
22.
ZHANG S., WANG S., 2018, Flexible Assembly Job-Shop Scheduling with Sequence-Dependent Setup Times and Part Sharing in a Dynamic Environment: Constraint Programming Model, Mixed-Integer Programming Model, and Dispatching Rules, IEEE Transactions on Engineering Management, 65/3, 487–504.
23.
NOVAS J.M., 2019, Production Scheduling and lot Streaming at Flexible Job-Shops Environments Using Constraint Programming, Computers & Industrial Engineering, 136, 252–264.
24.
MENG L., ZHANG C., REN Y., ZHANG B., LV C., 2020, Mixed-Integer Linear Programming and Constraint Programming Formulations for Solving Distributed Flexible Job Shop Scheduling Problem, Computers & Industrial Engineering, 142, 106347.
25.
LUNARDI W.T., BIRGIN E.G., LABORIE P., RONCONI D.P., VOOS H., 2020, Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, 123, 105020.
26.
ROSSI F., VAN BEEK P., WALSH T., 2006, Handbook of Constraint Programming, Elsevier.
27.
YOON H.J., 2010, Scheduling for Deadlock Avoidance Operation in Robotic Manufacturing Cells, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224/2, 329–340.
28.
COFFMAN J.R.E.G., ELPHIK M.J., SHOSHANI A., 1971, System Deadlocks, Computing Surveys, 3/2, 67–78.
29.
BANASZAK Z.A., KROGH B.H., 1990, Deadlock Avoidance in Flexible Manufacturing Systems with Concurrently Competing Process Flows, IEEE Transactions on robotics and automation, 6/6, 724–734.
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
