DEVELOPMENT OF A SOFTWARE FOR OPTIMIZING DIGGINGLOADING AND HAULING COSTS USING LINEAR PROGRAMMING

• Main Author: Jundi Fathurrahman, M.
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/61923
• Institution: Institut Teknologi Bandung

One component of production costs in mining operations is the cost of loading and transporting. These costs need to be minimized to maximize profits by optimizing using linear programming. To facilitate the optimization, a software is made to optimize loading-loading and hauling costs as well as analysis of variables that affect the optimization results. Software construction is done using the python programming language. The design of the graphical user interface (GUI) uses additional modules, namely PyQT5 and QT Designer also to perform optimizations used by the PuLP module. After the software has been successfully developed, a cost optimization simulation is carried out between the A dump truck with capacity 11 m3 and the A Shovel with capacity 11 m3. The same optimization results were obtained with the Microsoft Excel Problem Solver, which optimum production target for route 1 is 25.000 ton/day with 6 units of hauling equipment needs and for Route 2 is 27.000 ton/day with 7 units of hauling equipment needs. Optimization simulations were also carried out on optimization case studies with 2 scenarios. Scenario 1 uses a pair of B dump trucks with capacity 90,4 ton while scenario 2 uses C dump truck with capacity 136 ton with both scenario using BC shovels with capacity 12 m3. Sensitivity analysis was conducted to determine the relationship between productivity variables and equipment costs on the optimization results. The variables obtained that affect the optimization results are productivity variables (equipment cycle time and job efficiency) and equipment costs variables (ownership costs and operating costs). The productivity variable affects the achievement of the optimum production target. The productivity variable and equipment costs affects the achievement of the optimum production target. Job efficiency is inversely proportional (negative) to production costs, meanwhile cycle time, ownership costs and operating costs are directly proportional (positive). If it can be sorted from smallest-largest change, the average variable increase every 10% will increase production costs at owning cost of 0,035 $/Ton, operating cost of 0,06 $/Ton and then followed by cycle time of 0,1 $/Ton; while the increase in work efficiency will reduce production costs by 0,105 $/Ton.