PRODUCTION OF CONSTRUCTION OPEARTIONS: AN EXAMPLE ON READY MIX CONCRETE BATCH PLANT

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1 University Of Balamand (UOB) Faculty of Engineering Civil Engineering Department PRODUCTION OF CONSTRUCTION OPEARTIONS: AN EXAMPLE ON READY MIX CONCRETE BATCH PLANT By: Dr. Nabil M. Semaan, Ph.D., P.E. FUTURE CONCRETE 2016 MANAGING CONSTRUCTION SITE 25 August 2016, Beirut, Lebanon

2 2 Dr. Nabil M. Semaan AGENDA PRODUCTIVITY OF CONSTRUCTION OPERATIONS GENERAL CONCEPT FACTORS AFFECTING PRODUCTIVITY APPLICATION TO READY MIX CONCRETE BATCH PLANT DETERMINISTIC PRODUCTION MODEL QUEUYING PRODUCTION MODEL SIMULATION PRODUCTION MODEL

3 3 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS Construction Operations: over 1,145,000 business utilize heavy equipment in contract construction. The ability to win contracts and to perform them at a profit is determined by vital assets: Labor and Equipment. Productivity: Helps in evaluating Cost and Time Durations.

4 4 PRODUCTIVITY OF CONSTRUCTION OPERATIONS Construction Operations: concrete pouring structural steel erection slurry wall construction pile construction Caisson Construction Pipe Laying Brick and Masonry Work Reinforced earth Construction Earthmoving Operations Etc

5 5 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS General Concepts: Productivity = Quantity per hour Productivity = No. of Cycles per Hour * Capacity Cost of Production = Equipment Cost per hour / Productivity Time of Production = Total Quantity / Productivity

6 6 Dr. Nabil M. Semaan PRODUCTIVITY OF CONSTRUCTION OPERATIONS Factors Affecting Productivity: Equipment capacity, power and condition Operator knowledge Ground surface conditions Travel and return distance and speeds Soil classifications, conditions and features Job and management conditions External environmental conditions

7 7 APPLICATION TO RMC BATCH PLANT A concrete batch plant is a well-developed and industrialized plant, where the concrete is combined before transferring it to the site using transit mixer and ready to be placed. (Utranazz, 2008) Evaluating the production of the RMC batch plant is not straight forward.

8 8 RMC BATCH PLANT Dry Mobile Transit Mix Wet Mobile Transit Mix Dry Permanent Transit Mix Wet Permanent Central Mix RMC at Construction Site Water added in Transit Truck RMC at Construction Water added in Mixer Fixed Place Water added in Transit Truck Fixed Place Water added in Mixer

9 9 RMC CENTRAL MIX WET PLANT PROCESS

10 10 Dr. Nabil M. Semaan DETERMINISTIC PRODUCTION MODEL P BPi = V CM CT BP Where, P BPi is the ideal batch plant productivity (m 3 /hr). V CM is the volume capacity of the central mixer (m 3 ), CT BP is the batch plant Cycle Time (hr.) it takes to: Move the raw material to the central mixer, Mix in the central mixer

11 11 Dr. Nabil M. Semaan DETERMINISTIC PRODUCTION MODEL P BPa = P BPi n j=1 f j Where, P BPi is the ideal batch plant productivity (m 3 /hr.) P BPa is the actual batch plant productivity (m 3 /hr.), f i are reduction factors, j = 1 to n, n = total number of reduction factors.

12 12 DETERMINISTIC PRODUCTION MODEL B. P. = P BPa P TR a B.P. is the Balance Point or the required number of transit trucks in order to reach optimum productivity, P BPa is the actual batch plant productivity, P TRa is the actual transit truck productivity

13 13 DETERMINISTIC PRODUCTION MODEL HOLCIM NAHR EL MAOUT Aggregates (gravel and sand) bins: 3 bins, 45 m 3 capacity each; and 2 bins 48m 3 capacity each. Cement silos: 3 silos, 67 m 3 capacity each. Water tank: 4 tanks, 260 m 3 capacity. Admixtures tanks: 6 tanks, 3 m 3 capacity each. Central mixer: 1 mixer, 2 m 3 theoretical capacity.

14 14 Task DETERMINISTIC PRODUCTION MODEL Cycle No. Task Starts at Time [sec] Task Duration [sec] Task Ends at Time [sec] Batching Aggregates Discharge Aggregates Batching Cement Discharge Cement Batching Water Discharge Water Batching Admixture Discharge Admixture Mixing Concrete Discharge Mixer Batching Aggregates Discharge Aggregates Batching Cement Discharge Cement Batching Water Discharge Water Batching Admixture Discharge Admixture Mixing Concrete Discharge Mixer Cycle Time [sec]

15 15 DETERMINISTIC PRODUCTION MODEL CT TR [min] P TRa [m 3 /hr.] P BPa [m 3 /hr.] BP Required No. of Trucks

16 200 Dr. Nabil M. Semaan 16 DETERMINISTIC PRODUCTION MODEL 150 P BPa CT=15 CT=20 CT=30 CT=60 CT=90 CT= No. of Trucks

17 17 QUEUING PRODUCTION MODEL Service rate: μ= 1/Tservice Arrival rate: λ= 1/Tarrival MARKOV MODEL

18 18 QUEUING PRODUCTION MODEL Productivity = μ PI Q L μ is the service rate, L is the length of time, PI is the productivity index, Q is the actual capacity of the mixer.

19 Productivity Index (P.I.) Dr. Nabil M. Semaan 19 QUEUING PRODUCTION MODEL No. of Trucks CT=15min CT=20min CT=30min CT=45min

20 Productivity [m3/hr] QUEUING 18.0 PRODUCTION MODEL Dr. Nabil M. Semaan Truck Cycle Time [min] Truck No.=3 Truck No.=4 Truck No.=5 Truck No.=6 Truck No.=7 Truck No.=8 Truck No.=9 Truck No.=10 Truck No.=11 Truck No.=12 20

21 21 SIMULATION PRODUCTION MODEL

22 22 SIMULATION PRODUCTION MODEL Task SET Probabilistic Duration [sec] FEED AGGR BIN 1 Triangular (42, 53, 64) BLOW CEM SILO 2 Triangular (32, 40, 48) PUMP ADMX TANK 3 Triangular (38, 55, 72) PUMP WATER TANK 4 Triangular (27, 35, 41) MIX CONCR 5 Triangular (21, 30, 39) FILL CONCR TRUCK 6 Triangular (20, 34, 55) TRUCK TRAVEL 7 Triangular (900, 2700, 7200) Concrete Batch Plant Production Productivity Information Total Sim. Time Unit Cycle No. Productivity [Per Time Unit]

23 23 SIMULATION PRODUCTION MODEL

24 24 SIMULATION PRODUCTION MODEL The stochastic batch plant production (after 30 cycles) is equal to: (0.3045*3600) / 20 [m 3 /sec] = m 3 /hr.,

25 SIMULATION PRODUCTION MODEL Type No. Name Average Units Idle Max. Idle Units Times Not Empty % Idle Average Wt. Time Units At End Queue 1 Aggr Avail 0.0 1000 0.0 0.00 0.0 0 Queue 2 Aggr Bin Wt.

25 25 SIMULATION PRODUCTION MODEL Type No. Name Average Units Idle Max. Idle Units Times Not Empty % Idle Average Wt. Time Units At End Queue 1 Aggr Avail Queue 2 Aggr Bin Wt Queue 4 Cement Avail Queue 5 Cem Silo Wt Queue 7 Admx Avail Queue 8 Admx Tank Wt Queue 10 Water Avail Queue 11 Water Tank Wt Queue 13 Aggr Ready Queue 14 Cement Ready Queue 15 Admx Ready Queue 16 Water Ready Queue 17 Mixer Wait Queue 20 Concr Ready Queue 21 Truck Wait

26 26 Dr. Nabil M. Semaan CONCLUSIONS Deterministic Model: Queuing Model: Simulation Model: P BPa = 63 m 3 /hr. P BPa = 23 m 3 /hr. (Max.) P BPa = 54.8 m 3 /hr. (after 30 cycles) Mixer Idle 68% of time 32% efficiency. Simulation Model most comprehensive Points out at bottle necks Queuing Model less accurate lot of assumptions: FIFO Erlang Exponential probability distributions of activities durations

27 27 THANKS FOR YOUR ATTENTION!

If a customer arrives and finds both servers busy, there is a 25% probability that he departs without entering the queue.

If a customer arrives and finds both servers busy, there is a 25% probability that he departs without entering the queue. Dennis L. Bricker Dept of Mechanical & Industrial Engineering The University of