Development of machine allocation & Route Recommendation).pptx

Project Title:
Name of the Guide:
Department of Civil Engineering
Project Synopsis Presentation – 2021 -2022
Name of the Student USN
Group Number
Designation:
DEVELOPMENT OF MACHINE ALLOCATION SYSTEM AND
ROUTE RECOMMENDATION
SN Varuna
17-05-2023 | 1
Ashutosh Adhikari 1AY19MI003
Kencho Gyeltshen 1AY19MI010
Tshering Dorji 1AY19MI016
Acharya Institute of Technology, Acharya Dr. Sarvepalli Radhakrishnan Road, Soladevanahalli,Bengaluru 560107
• 01
Lecturer
Department of Mining Engineering
Department of Mining Engineering
Project Final Presentation 2022-2023
Project Title:
Name of Co-Guide:Kandi Sandeep Reddy
Designation: Assistant Professor

Contents
1. Introduction
2. Background information
3. Problem Statement
4. Objectives
5. Literature Review
6. Methodology
7. Result and Discussion
8. Time Scale
9. Conclusion
10. Future Scope
11. References
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1. Introduction
Mining is a highly capital intensive.
 Truck and shovel operation accounts for approximately 60% of total mining operation.
Optimize the usage of mobile assets and meet the strategic production schedule.
Optimization in mining machine allocation maximizes efficiency and productivity
Best allocation of machines reduces waste and downtime while maximizing output and
profitability
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Goal is to use machines to their fullest potential for efficient and effective mining
operations
Mathematical algorithms or heuristic methods can determine optimal machine allocation
Optimization strategy improves overall performance and profitability of mining companies.
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2. Background Information
The Hungarian method is a mathematical algorithm used to find the optimal assignment of
agents to tasks by constructing a matrix of costs or profits and finding a series of
augmenting paths to minimize the total cost or maximize the total profit, commonly used in
operations research, economics, and engineering.
The Traveling Salesman Problem (TSP) is about finding the shortest route between the
sources and destination.
The number of jobs and the number of machines are assumed to be finite in Scheduling
problems
Time study / work measurement
Man-Hour = Number of people worked × Working time
Productivity = Total Output/Total Input
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3. Problem Statement
 No proper platform for machine allocation system in Mining
 Target output not achieved due to breakdown time
 Loss of time and energy during the operation
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4. Objectives
To reduce Cost, time and energy of machine allocation
To eliminate unnecessary manpower
To recommend haulage routes for machine
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5. Literature Review
 Optimization of Truck-Shovel Allocation Problem in Open-Pit Mines:
Benlaajili, S., Moutauouakkil, F., Chebak, A., & Medromi, H. (2020), Optimizing the dispatching system of haul trucks in open-pit
mines is crucial for enhancing production and efficiency while reducing operating costs. This paper presents a literature review on
the truck-shovel allocation problem and proposes a dispatching model that can be integrated into a mining fleet management system.
The proposed model involves modeling the allocation of shovels as a vehicle routing problem and a mixed integer linear
programming model to determine the optimal number of trips required to transport ore from loading points to dumping sites. The
model also includes the assignment of shovels, which is often ignored in dispatching systems. The proposed models are designed to
handle heterogeneous fleets of trucks and shovels with different capacities. Results from simulation experiments indicate that the
proposed models provide efficient solutions to the truck-shovel allocation problem.
 Mathematical Modeling for Optimal Manpower Allocation
This paper presents a case study of optimal manpower allocation in aircraft line maintenance using mathematical modeling. The
study focuses on determining the optimal number and type of manpower required for each service, considering licensed workers
(Aircraft Maintenance Engineer Licence - AMEL) and non-licensed workers (Aircraft Maintenance Technician - AMT). The model
also takes into account the possibility of transferring manpower among different stations. The objective of the model is to minimize
employee expenses while ensuring sufficient manpower allocation for each service and meeting the AMEL requirement. The model
is solved using the ILOG CPLEX software, and the results show that the proposed manpower allocation can meet the required
manpower need and serve all loads efficiently. This study provides insights for optimal manpower allocation in aircraft line
maintenance, and the proposed mathematical modeling can be applied in other similar industries.
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 A Model and Algorithm:
The optimal workforce assignment for chip handling in computer-based manufacturing has been a challenging task for industrial
management. This paper proposes the Optimal Manpower (OMP) model, which provides a practical solution to this problem. The
OMP model considers balancing machine productivity and human ability in the objective and develops a step-by-step algorithm to
optimize the combination problem to achieve maximum profit. To extend the applicability of the OMP model, a computerized
decision in Visual Basic is developed to efficiently approach the optimal solution of manpower allocation. The versatility of the
OMP model is demonstrated through a numerical study. This paper provides a valuable tool for optimizing the profit of machining
projects and contributes to the applicable scheme of manpower allocation for chip handling in computer-based manufacturing.
 A Study of Modern Express Delivery Services:
Manpower allocation and shift scheduling are crucial factors in many industries, including modern express delivery services. While
much literature has discussed the real-time control issue of hardware systems, modeling and optimizing human resources has
received less attention. In their study, Lau et al. (2010) aim to optimize manpower allocation and shift schedules to maximize overall
throughput and minimize costs. The proposed algorithm includes a human performance model with conventional considerations.
Simulation experiments were conducted to test the algorithm's efficacy, and satisfactory results were obtained. The algorithm
provides a quality solution to manpower allocation and shift scheduling, resulting in improved labor cost and utilization. The study
highlights the importance of considering human performance in workforce allocation and scheduling to enhance overall system
efficiency.
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6. Methodology
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7. Result and Discussion
Mining Solution App icon
Application Interface
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Login page Home Page Sign out Button

 Equipment Details Menu
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Routing Solution
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Equipment allocation menu

8. Time Scale
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Problem Identification
Literature Review
Finding Solutions
Software Development
Name & Icon Design
Software Analysis
Submission
AUG
2022
SEP
2022
OCT
2022
APR
2023
NOV
2022
MAR
2023
DEC
2022
MAY
2023
JAN
2023
JUN
2023
FEB
2023

9. Conclusion
The machine allocation system in mines is crucial for optimizing work outputs.
Using our application, users can view details of equipment used in the mines.
The application can also recommend the best route with the shortest distance and minimum
cost.
Job allocation for particular machines with mobility is possible through the application.
Overall, the application can improve efficiency and productivity in mining operations.
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10. Future Scope
Future scope of machine allocation and route recommendation application in mining is
significant and promising.
The application can optimize operations, increase efficiency, and reduce costs.
Advanced analytics, machine learning, and artificial intelligence can be integrated for better
accuracy.
The application can provide insights into equipment performance and utilization rates.
Integration with other mining-related applications is possible for a comprehensive solution.
Overall, the application has the potential to transform the mining industry by making it
more efficient, productive, and sustainable.
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11. References
1.Akerstedt, T. et al. (2000), Sleepiness and days of recovery, Transportation Research Part F, Vol. 3, pp. 251-261
2.Aykin T. (2000), A Comparative Evaluation of Modeling Approaches to the Labor Shift Scheduling Problem, European Journal of Operational Research, 125, pp. 381-397
3.Baron, S. et al. (1990), Quantitative Modeling of Human Performance in Complex, Dynamic Systems, National Academy Press, Washington
4.Choudhary, R. P. (2015). Optimization of load – Haul – Dump mining system by Oee and match factor for surface mining. International Journal of Applied Engineering and Technology,
5(2), 96–102.
5.J. Little, M. Nehring and E. Topal: ‘A new mixed-integerprogramming model for mine production scheduling optimisationin sublevel stope mining’, Proc. 2008 Australian
MiningTechnology Conf., Brisbane, Australia, September 2008, TheAustralasian Institute of Mining and Metallurgy, 157–172.
6.M. Nehring and E. Topal: ‘Production schedule optimisation inunderground hard rock mining using mixed integer programming’,Proc. Project Evaluation Conf. 2007, Melbourne,
Australia, June2007, The Australasian Institute of Mining and Metallurgy, 169–17
7.N. Runciman, N. Vagenas and T. Corkal: ‘Simulation of haulagetruck loading techniques in an underground mine usingWITNESS’, Simulation, 1997, 68, (5), 291–299).
8.Patel, B., & Prajapati, J. (2012). Evaluation of bucket capacity, digging force calculations and static force analysis of mini hydraulic backhoe excavator. Machine Design, 4(1), 59–66
http://standards.globalspec.com/std/1856598/sae-j296
9.Polanco, A.R. (2014) Notes of surface mining. Moa. Retrieved from http://docplayer.net/22851170-Improvingproductivity-of-mining-machinery-through-totalproductive-
maintenance.html
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