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Create the model and optimise the revenue for the plant over ten days operation after an appropriate warm-up period.

Gears are manufactured in a separate plant and arrive via a 20 m conveyor according to a distribution defined in the excel file supplied. This conveyor feeds an assembly area where gearboxes are assembled with two gears and two bearings (always available). The conveyor is of a belt type, the gears have a footprint of 80 by 80 by 100 mm and can be spaced on the conveyor 100 mm apart. The maximum capacity is 180 parts and speed 5 m/min.

The gearboxes are drawn from stock as castings and are loaded to a CNC workstation for additional machining and boring. An operator loads each casting to the workstation (0.5 – 1.0 minutes) and unloads them to a bin (maximum capacity 50) or a sample inspection station. Unloading takes 0.5 – 0.9 minutes and the machining cycle takes 2.3 minutes.

Every 20th gearbox machined is passed to an inspection station, the others to a buffer. The inspection operation cycle time is triangularly distributed with a minimum of 0.8 minutes, a maximum of 2.2 minutes with a mode of 1.1 minutes. This is carried out by the operator(s). Previous tests have shown that after 20 samples inspected there is a 78% chance that the critical dimensions (bores for bearings) are at the upper tolerance limit. When this occurs, the tools in the CNC workstation need to be replaced and the machine cleaned. This operation time is triangularly distributed with a minimum of 2.5, maximum 3.7 and mode of 3.2 minutes. The failed gearbox is scrapped.

Machined gearboxes are assembled with two gears and two bearings by an operator. This takes a mean of 3 minutes with a standard deviation of 0.7 minutes, but no less than 2.2 minutes and no greater than 5. The assemblies are put into a buffer for testing.

The test bench runs the gearboxes for 10 minutes and can initially take up to five at a time. An operator loads and unloads them to the test points. Loading and unloading both take from 0.2 to 0.4 minutes for each gearbox . There is a 93% chance of passing the gearbox for shipping; the failures are sent directly to a disassembly station where an operator will remove the gears and replace on the conveyor 5 m from its end. The gearbox castings and bearings are discarded. Disassembly takes a mean of 2.3 minutes with standard deviation 0.4, but no less than 1.7 and no greater than 3.5 minutes.

The plant is staffed by a number of operators (to be decided) who are trained in all activities.

You have purchased the CNC workstation for £72K, which needs to be repaid in three years. The plant operates on an eight-hour shift. Operators have 15 minute break in the morning and afternoon and one hour for lunch.

Additionally you have a budget of £15K to set up the remainder of the plant. This expenditure must be repaid within one year.


Assembly stations with appropriate tooling:


Bins for machined gearboxes:


Test bench space per gearbox:


Fixed cost for hiring an operator (advertising, training etc.):


Operator hourly rate including on-costs:


Optimise the plant to achieve maximum revenue with budget. You may need to optimise the plant for the priorities of the machines first. The expected revenue for each completed and working gearbox is £80.

Investigate what effect a breakdown of each of the CNC workstation and the conveyor for 90 minutes has on the daily production rate.

Write a report describing your model and present results on a series of tests on the model. In your conclusions, present an evaluation of the model in the light of all assumptions made and what limitations they place on the model.


The work is based on the manufacturing gear simulation which is for the setup of the separate plant and to handle the 20m conveyor which is as per the distribution defined for the excel file.  The conveyer tends to handle the gearboxes with the assembling process that is for the gears as well as the bearings. The maximised capacity for the discrete even simulation process is 180 parts with the speed of 5m /min. (Zeigler et al., 2000). The gear boxes have been set with the stocks as the castings as well as with the workstations that is able to handle the machining as well as the boring. The operator easily loads the forms with the workstations to cast and unload to a particular bin with maximised capacity. The gearbox machine has been set with the inspection station for the bugger where the operation cycle is set for the distribution along with handling and distributing the maximum of 2.5, mode of 3.2 minutes. The machined gearbox is generally set with the two gears that is for the operator. The test benches are able to run for the time of approximately 10 minutes. The operator loads and unload the testing points depending upon the different forms of the gearbox. The chance of the setup includes the forms where there are gearbox for the shipping and the failures are then directly sent to the disassembly stations. (Sahu et al, 2016). It will remove the gears and replace the conveyor from the end. The simulation technology and the optimisation is set with the gearbox manufacturing plant which is then simulated by the conducting of different task. The simulation tool is based on analysing the characteristics as well as the different sets that is able to take hold of the dynamic systems. With the discrete problems, the methodology is for the stochastic approximation which is then set with the gradient approach. The search is for the continuous event simulation with the charges that can be taken place in the system at the time of the discrete event simulation. The focus is also on the continuous variable problems where the process is based on Lanner Group Inc., which is based on the production and the manufacturing process, mainly to analyse and get the information. (Shukla et al., 2017). The gearbox manufacturing plant has been set with the software as per the conditions that could easily calculate the revenue with the budget from the details given. The consideration is based on the methodology where the relationship is set with the gradient research. The simulation software is used for the development with the CNC machines that are for the production as well as the defective products that lead to the disassembling area. The loading and the unloading of the parts of the machine are through the operator where there is uniform distribution of the cycle time. (Petti et al., 2016). The capacity is set with the two gears and the bearings of the operator which takes the time of 3 minutes approximately. The assembly is set to test mainly the buffer as well as the testing bench. It is mainly to handle the operations where the loading generally takes 0.2 to 0.4 minutes for the process with unloading the gear boxes as well.

Costs and Budget

The basis are upon the plant which could easily be modelled through the use of the Petri net that has been set with the stochastic handling. The tool has been created with the concurrent model for the distributed systems which includes the places, transitions and the arc. Here have been indication of the circles as well as the rectangles, arrows for the system to handle the change of state. It also allow the model for handling the complexity. The model was for the machines, buffers and the parts where the buffers have been used for the bins as well as the testing functions. (Prajapat et al, 2016). It helps in specifying the actions where the gears arrived and then they are supplied to the assembly through the set of conveyor at a particular fixed speed. The work focus on the optimisation of the plants to achieve and handling the budget revenue. For this, there is also a major priority to handle the machines along with the working which has been set in the gearbox forms. The investigation is for the effect where the CNC station holds the conveyor for the time of approximately 90 minutes with the production rate that is able to handle the test series. The Witness model has been used for the discrete elements, logical and the graphical elements that have been set with power and free setup. There are different modules which are for the entities that are for the parts or the work in progress. (Iannone et al, 2016). The logical elements and the modules are also for the additional facilities that is mainly for handling the manufacturing for the nearby models or the raw material. It includes the simulation in the SHIP, SCRAP etc. A proper buffer is important for having capacity that could easily be filled into the parts and then emptied as per the process of the activity. (Pitt et al., 2016). The machines in witness are also set with the classified forms of the activities where there are input rules, cycle times and the output rules, where the labour could easily be set with the multi-cycle and the multi station. The discrete events and the continuous modelling is with the process to address the wide range of the problems which are efficient to address. The continuous elements also enable the modelling of the processes through the fluids which are set through the pipes where there is a higher volume of the parts through the processing of the speed.(Alrabghi et al., 2016).

The power of witness could easily be compared to the PNDES where the machines have been pre-defined for the labour with easy implementation as well. The buffers are found to be well defined where the parts are also for the separate elements, as well as for the statistical analysis. The activities for the search is in the discrete event simulation. The witness machine type is able to handle the single machine with the processes that are for the particular part as well. The assembly machine as well as the production machines are able to take the number of the parts and the outputs mainly for the same or the different parts depending upon the requirement. The multi-station machine is for the operation with the linking. There are different forms of the numbers for the part positions which are stations and they are mainly to handle the different cycle times for the stations where one can easily specify for the different input or the output quantities. (Packianather et al., 2016).


The implementation has been in the Witness Software which is interactive and interpretive for the simulator software to handle the discrete events and the events which are continuous for modelling. The model will easily be built from the scratch to show that there is an easy usage and it powerful as well. The engine is also capable to model the different applications where the OLE is also compatible with the real time system objects. The combination of the continuous flows with the discrete events holds the models that could easily be addressed with the wide range of the effective approaches. (Rybicka et al., 2016). The elements also enable the processes which includes the flow of the fluid mainly through the pipes or the tanks where the situation has been able the higher volumes for the different parts of the process. The designing also include the modelling with the 3D visualisation that is easily able to deliver the virtual reality performance. With a proper connectivity and the database, a proper setup of the simple and the powerful logic coding helps in developing the compartmentalised modular blocks. (Gosavi et al., 2016).

The simulation of the results is mainly through the system modelling process which could be important for the idea with different characteristics. One can easily find the manual calculations that set the budget in the required limit of the 15000 pounds, where the maximised revenue is for the use of the assemblies as well as for the benches with the 2CNC machines. (Wilsone t al., 2016). The conveyor belt has also been used, where the budget of 13700 is used. The use of the witness model is mainly to optimise the forecast as well as handling the proceedings mainly to improve and maximise the income. It will forecast for the liabilities to change as well as handling the performance in the real world where the values could be evaluation with the higher or the lower forms of the system forecasting. The breakdown works on the analysis, to understand the impact on the system. The plant is affected mainly due to the units that are broken down mainly by the conveyor belt. (Rybicka et al., 2016). The system requirements for the Witness software is mainly to provide with the minimised requirements where:

  1. There is a processor of Intel or AMD which is set with the high speed recommendations.
  2. The Windows Versions are set for the different platform.
  3. The memory is 2GB of RAM which is more than 4GB.
  4. The hard drive and the screen includes the pixel screen resolutions.
  5. The graphics generally need the acceleration for the better performance. Hence, for this, the recommendation is mainly on using the 3D environment which includes the premium performances.
  6. The USB includes the software that can easily be used through the support portal.

It also includes the loading and the unloading from the gearbox where there is a chance to pass for the shipping as well. The failures are sent to the disassembly function where the operator will easily be able to remove the gear as well as replace on the conveyor mainly from its end. (Barlas et al., 2016). The cost and the budget for the CNC workstation is $72K where there is a repayment in the time of 3 years. The plant also operates and work on the shift of the 8 hours. There is an additional setup of the $15K value for the complete plant that is remaining. (Vaatainen et al., 2016). The cost for this is the assembly station which is mainly for the tooling purpose.


The cost function for the plant is mainly based on the direct or the indirect cost where:

The cost of the grinder is £12000 over the time of 3 years. Here, £4000/year is £2/hr

Hence, the operator £1500 over the time of 1 year setup with £15.00/hr


Additional bin=£200 over 1 year


The revenue from the sprindle is £S

Simulation Time is T hrs

Actual Revenue is R

R = NShip(Spindle) X S – 2T X NQty(Grinder) – 0.1T X NQty(Bin) – 15.75T * NQty(Operator)

It is in the maximum form.

There are different types of the conveyor which includes the indexed fixed, indexed queuing, continuous fixed and the continuous queuing. The category of the movement is modelled with the type of the conveyor as well. The motion has been modelled by the time for the conveyor where there is a need to move to a particular distance with the indexed and the continuous conveyor. It depends on the length which is defined in the form of units as well as the speed is also defined in the units with time. There are different types which are modelled by the parts with fixed distance, along with the belt or the chain conveyor. The queuing conveyor like the roller conveyor is able to keep the parts moving mainly towards the end. The direct and the indirect costs could easily process into direct cost with the direct material, and labour. (Sheng et al., 2017). The indirect are for the tooling, machine rate as well as the administrative burden for quality control. Witness is able to offer the support to the multi core processing of the model execution as well as allowing the users to run the replications with delivering the results as well as the insight clarity for a better outcome. The simulation is based on the description of the events and the frameworks that allow the time of the event at specific intervals. (Galasso et al., 2016). The events are instantaneous where the activities also extend over the time and have been modelled as the event sequence.


To reduce the costs, there have been certain costs, usages, as well as the wastage that will directly lead to the material costs as well as reducing the tooling costs as well. The reduction in the different number of the operations will lead to the reduced time of cycle and will also reduce the labour or the machine cost. (Prajapat et al., 2017). The machine rates could easily be calculated with the Witness model. The simulation of the events is set in the state where there have been specifications of the intervals with the single threaded form that holds the intervals as well as the synchronisation in between the current states. In this, the events are also based on the interval based modes with the priority queue that is sorted at the time of events. (Soofastaei et al., 2016). The model has been designed with the maximised revenue which could be for a periodic time for a particular budget of 13700 pounds. It also indicates that the plant can easily incur for the revenue which is based on the forecasting as well as to handle the amount of the income. The requirements are based on the plants where the detailed analysis is set with the breakdown in the plant in a week. It also shows that there have been different numbers of the sufferers. (Ozcan et al., 2016).When the machines or the units are broken down, mainly the CNC and the conveyor belt, the complete plant is affected and then it is also subjected to the blockage of the different machines. The results include that there is no requirement of the human, external or the supply factors that play an important role to work on the industries as well as to focus on the simulation forecasting factors that leads to the poor performance.   


Zeigler, B.P., Praehofer, H. and Kim, T.G., 2000. Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic press.

Sahu, A. and Pradhan, S.K., 2016, September. Quantitative analysis and optimization of production line based on multiple evaluation criteria using discrete event simulation: A review. In Automatic Control and Dynamic Optimization Techniques (ICACDOT), International Conference on (pp. 612-617). IEEE.

Shukla, N., Keast, J.E. and Ceglarek, D., 2017. Role activity diagram-based discrete event simulation model for healthcare service delivery processes. International Journal of Systems Science: Operations & Logistics, 4(1), pp.68-83.

Petti, A., Hutabarat, W., Oyekan, J., Turner, C., Tiwari, A., Prajapat, N. and Gan, X.P., 2016. Impact of model fidelity in factory layout assessment using immersive discrete event simulation.

Prajapat, N., Waller, T., Young, J. and Tiwari, A., 2016. Layout Optimization of a Repair Facility Using Discrete Event Simulation. Procedia CIRP, 56, pp.574-579.

Iannone, R., Miranda, S., Prisco, L., Riemma, S. and Sarno, D., 2016. Proposal for a flexible discrete event simulation model for assessing the daily operation decisions in a Ro–Ro terminal. Simulation Modelling Practice and Theory, 61, pp.28-46.

Alrabghi, A. and Tiwari, A., 2016. A novel approach for modelling complex maintenance systems using discrete event simulation. Reliability Engineering & System Safety, 154, pp.160-170.

Packianather, M.S., Davies, A., AlZarooni, M.A., Soman, S. and White, J., 2016, July. Manufacturing process flow improvements using simulation and sustainable manufacturing. In World Automation Congress (WAC), 2016 (pp. 1-6). IEEE.

Rybicka, J., Tiwari, A. and Enticott, S., 2016. Testing a Flexible Manufacturing System Facility Production Capacity through Discrete Event Simulation: Automotive Case Study. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 10(4), pp.690-694.

Wilson, J., Arokiam, A., Belaidi, H. and Ladbrook, J., 2016. A simple energy usage toolkit from manufacturing simulation data. Journal of Cleaner Production, 122, pp.266-276.

Barlas, P. and Heavey, C., 2016. Automation of input data to discrete event simulation for manufacturing: A review. International Journal of Modeling, Simulation, and Scientific Computing, 7(01), p.1630001.

Sheng, L.W., Basri, E.I. and Kamaruddin, S., 2017. Analysis on the Effect of Shop Floor Parameters on the Effectiveness of Preventive Maintenance through Discrete Event Simulation. PERTANIKA JOURNAL OF SOCIAL SCIENCE AND HUMANITIES, 25(1), pp.1-16.

Galasso, F., Ducq, Y., Lauras, M., Gourc, D. and Camara, M., 2016. A method to select a successful interoperability solution through a simulation approach. Journal of Intelligent Manufacturing, 27(1), pp.217-229.

Prajapat, N. and Tiwari, A., 2017. A review of assembly optimisation applications using discrete event simulation. International Journal of Computer Integrated Manufacturing, 30(2-3), pp.215-228.

Gosavi, A., Cudney, E.A., Murray, S.L. and Masek, C.M., 2016. Analysis of Clinic Layouts and Patient-Centered Procedural Innovations Using Discrete-Event Simulation. Engineering Management Journal, 28(3), pp.134-144.

Soofastaei, A., Aminossadati, S.M., Kizil, M.S. and Knights, P., 2016. A discrete-event model to simulate the effect of truck bunching due to payload variance on cycle time, hauled mine materials and fuel consumption. International Journal of Mining Science and Technology, 26(5), pp.745-752.’

Pitt, M., Monks, T., Crowe, S. and Vasilakis, C., 2016. Systems modelling and simulation in health service design, delivery and decision making. BMJ quality & safety, 25(1), pp.38-45.

Ozcan, Y.A., Tànfani, E. and Testi, A., 2016. Improving the performance of surgery-based clinical pathways: a simulation-optimization approach. Health care management science, pp.1-15.

Väätäinen, K., Anttila, P. and Annevelink, E., 2016. D3. 4+ D3. 6: Annex 3 Logistical case study in Finland. S2Biom.

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