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Optimization Algorithms forFlexible Production Scheduling
Zdenek.Hanzalek@cvut.czthanks to : P. Sucha, I. Modos, Z.Baumelt
Czech Institute of Informatics, Robotics and CyberneticsFaculty of Electrical Engineering
Czech Technical University in Prague
Supply Chain Management and Scheduling
Sales Material Production Distribution
• Supply chain is driven by the customer demand• Production needs to be very flexible • Scheduling and rescheduling is needed to achieve efficiency
Application:Flexible planning and scheduling in
printing company
Planning and Scheduling
Planning layer - typically in ERP• Works with cumulative capacities in
buckets of time (like day-week) • Loads operations into buckets • No account of sequencing,
precedences, set-up time
Scheduling layer• Exact start time of each operation• Completion time of job is known• Operations allocated to machines• Takes into account set-up times,
types of resources, transport times• Brings savings and order
Flexibility
Mike Tyson: „Everybody has a plan - until he gets hit.“
Manufacturing is hit very often:
• Machine breakdown
• Material unavailability
• Order with high priority
• Sick-leave of personnel
Solution
Integration withMES&Rescheduling
Resource productivity and efficiency
Information Systems
ERP• Orders
• Material
• Know-how
CPS• Events
Flexible Scheduling
AlgorithmsProcesses
• Optimization algorithm coupled with existing ERP
• Recalculation of the production plan every 30 minutes
• Objective is a maximal use of the most expensive machines
• Sales personnel adjust timing/price of orders up to the available capacities
• Uptime increase from 30% to 40% due to the reduction of changeover times
• Fully automated
Machine‐Uptime in Printing Company
Sales
• inserts jobs
Planner
• checks availability of
resources
• adapts global schedules
Users and their roles
Foreman
• sees a schedules/status
Worker
• sees a sequence of operations
Warehouseman
• bill of materials to be ready
Fabrio – internet service
Histogram of Up‐timeBefore and After
0102030405060708090
1000 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
2014 avg 41,3%2012 avg 30,5%
• Today – increasing number of small orders • Replacement of sales personnel by Web2Print
Case Study:Robust Scheduling with Energy
Consumption Limits in Glass Hardening
Robust Scheduling withEnergy Consumption Limits
• Energy-aware scheduling – consumption limitsper 15 mins
• Uncertainty of execution – disruptions, materiál, …
• Robustness of theschedule is a necessity to avoid panalties
• Development of exact and heuristic alorithms
Case Study:Energy efficiency of Skoda Gear
Box Production Line
Production line for automatic gear box consists of:1. machine tools center2. hardening line3. assembly
Energy Efficiency of Hardening Line
• hardening is the most energy consuming stage
• CURRENT SITUATION: even if a hardening furnaces is not used it is not switched-off due to the luck of the schedule
• SOLUTION: The aim is to switch between operational and standby modes of hardening furnaces
• relation between production scheduling and cost of energy
Case Study:Robotic Welding Cells in
Car Manufacturing
Parameter and Code Generation in Digital Factory
• Generation of parameters and real robot programs
• Cycle‐time and energy• Order of operations• Robot trajectories ‐movements, spot welding
• Shared zones ‐ robots must avoid collisions
Tecnomatix Process SimulateReal Experiments by Blumenbecker
Robot Energy Consumption
1. selection of stationary positions
2. power save modes
3. trajectory selection
4. speed of movement
5. order of operations
We consider the following categories of saving:
Tasks and Precedence Constraints● graph representing two robots
● schedule
Personnel scheduling
Department of Control Engineering
Set of employees with qualificationsSet of shiftsSet of constraintsMultiobjective criterion
Goal - to assign required shifts to employees with respect to given constraints
labour codecollective agreement
…all shifts assignment, coverageminimal time gaps between shifts,maximal length of blocks, balancing
…
Verification of real-time systems
• timed automata, temporallogics
• cooperative-preemptivescheduling
• case studies in automotive sector
• modelling of fault tolerant systems
• distributed applications based on CAN
Finishing timeP-high
CP-highin [1,2]
Finishing timeP-low
CP-lowin [1,2]
0 1 2 3 40
1
2
• Waszniowski, L. - Hanzálek, Z.: Formal Verification of Multitasking Applications Based on Timed Automata Model, Real-Time Systems, Volume 38, Number 1, January 2008
• Waszniowski, L. - Krákora, J. - Hanzálek, Z.: Case Study on Distributed and Fault Tolerant System Modelling Based on TimedAutomata. Journal of Systems and Software, 2009
Message Scheduling for Profinet IO IRT
Hanzálek, Burget, Šůcha, P.: IEEE Trans. on Industrial Informatics, 2010.
Tree topology• switch integrated in each node• data are forwarded according to
a static communication schedule
Formulated as PS|temp|Cmax
Parallel Optimization Algorithms
Graphics Processing Unit• Collaboration with
NVIDA (NDA, visits in Palo Alto)
• solve combinatorial problems on GPUs
Tabu Search algorithm• 10.5/42.7 times faster
than the optimized parallel/sequential algorithm for the Central Processing Unit (CPU)
Bukata, Šůcha, Hanzálek: Journal of Parallel and Distributed Computing 2015
Journal papers in 2016
1. Bukata, L. - Šůcha, P. - Hanzálek, Z. - Burget, P.: Energy Optimization of RoboticCells, IEEE Transactions on Industrial Informatics, 2016.
2. Módos, I. - Šůcha, P. - Václavík, R. - Smejkal, J. - Hanzálek, Z.: Adaptive online scheduling of tasks with anytime property on heterogeneous resources, Computers and Operations Research, December 2016, Volume 76, Pages 95–117, Elsevier.
3. Václavík, R. - Šůcha, P. - Hanzálek, Z.: Roster evaluation based on classifiers for thenurse rostering problem, Journal of Heuristics, October 2016, Volume 22, Issue 5, Springer.
4. Dvořák, J. - Hanzálek, Z.: Using Two Independent Channels with Gateway for FlexRayStatic Segment Scheduling, IEEE Transactions on Industrial Informatics, October 2016.
5. Hanzálek, Z. - Šůcha, P.: Time Symmetry of Resource Constrained Project Schedulingwith General Temporal Constraints, Annals of Operations Research, Springer.
6. Minaeva, A - Šůcha, P. - Akesson, B. - Hanzálek, Z.: Scalable and EfficientConfiguration of Time-Division, Journal of Systems and Software, March 2016, Volume113, Elsevier.
7. Hanzálek, Z. - Tunys T. - Šůcha, P.: An Analysis of the Non-preemptive Mixed-criticalityMatch-up Scheduling Problem, Journal of Scheduling, October 2016, Volume 19, Issue5, pp 601–607, Springer.
8. Bäumelt, Z. - Dvořák, J. - Šůcha, P. - Hanzálek, Z.: A Novel Approach for NurseRerostering based on a Parallel Algorithm, European Journal of Operational Research, June 2016, Volume 251, Issue 2, Elsevier.
Awards in 2016
Excellent Research Results in Technical Sciences • evaluation was based on impact of the paper measured in terms of
contracts and citations • our group got 2 results out of 42 awarded in Technical Sciences in the
Czech Republic
Hanzálek, Z. - Burget, P. - Šůcha, P.: Profinet IO IRT Message Scheduling with TemporalConstraints. IEEE Transactions on IndustrialInformatics, August 2010.
Hanzálek, Z. - Jurčík, P.: Energy efficientscheduling for IEEE 802.15.4/ZigBee. IEEE Transactions on Industrial Informatics. 2010.
Projects and Contracts in 2016
1. HERCULES - High-Performance Embedded Real-timeArchitectures for Low-Power Many-Core Systems, EuropeanCommission 688860. Horizon 2020.
2. FOREST - Flexible Scheduling and Optimization Algorithms for Distributed Real-time Embedded Systems - GACR.
3. SALTT - Scheduling Algorithms for Time-Triggered Systems, Office of Naval Research Global N62909-15-1-N094, US Navy.
4. CAK 3 - Centre for Applied Cybernetics - TACR.5. eRobot – Energy efficiency of robotic welding cells – Ministry of
Industry and Trade.