Surface Mine Production Scheduling: 5-Day Professional Training Course

Course Overview

The Surface Mine Production Scheduling training program is an intensive 5-day course designed for mine planners, mining engineers, and technical professionals responsible for optimizing open pit production schedules. This comprehensive training delivers practical expertise in strategic planning, short-term scheduling, NPV optimization, and constraint management using industry-leading software platforms including Minemax Scheduler, Datamine, Deswik, and MineSight.

Participants master advanced scheduling techniques that maximize Net Present Value (NPV) while balancing mining, processing, and marketing constraints. From life-of-mine strategic schedules through weekly operational plans, this course covers the complete scheduling hierarchy with emphasis on practical optimization, blending strategies, equipment allocation, and cash flow management.

Target Audience: Mine planners, production schedulers, mining engineers, technical services managers, operations managers, and professionals responsible for optimizing surface mine production.

Prerequisites: Engineering degree or equivalent; understanding of open pit mining operations, basic economics (NPV concepts), and familiarity with mine planning software.

Day 1: Strategic Mine Scheduling Fundamentals

Morning: Scheduling Framework and Economic Principles

Establishing foundational understanding of mine production scheduling philosophy, temporal hierarchy, and economic drivers that guide optimization decisions.

Learning Outcomes:

• Scheduling hierarchy: strategic (life-of-mine), tactical (1-5 years), operational (weekly/monthly)

• NPV maximization principles: discount rates, time value of money, cash flow optimization

• Understanding mining value chain: extraction, haulage, processing, marketing

• Relationship between pit optimization, pushback design, and production scheduling

• Key performance indicators: tonnes mined, grade delivered, stripping ratio, equipment utilization

• Balancing early revenue generation with long-term value maximization

• Contract obligations and market constraints in scheduling decisions

Economic Concepts:

• NPV calculation methodology and sensitivity analysis

• Discounted cash flow modeling for mining projects

• Impact of discount rates (typically 8-12%) on schedule optimization

• Cut-off grade strategies: incremental, breakeven, and marginal cut-offs

• Stockpiling economics: opportunity cost versus grade recovery benefits

Afternoon: Block Models and Scheduling Inputs

Understanding data requirements and block model preparation essential for creating optimized production schedules.

Learning Outcomes:

• Block model fundamentals: grade, density, rock type, metallurgical domains

• Orebody characterization and geological variability

• Defining mining blocks: aggregation strategies from resource blocks

• Precedence relationships and mining constraints

• Equipment parameters: production rates, operating costs, availability

• Processing constraints: mill capacity, recovery curves, throughput limitations

• Calculating economic block values for scheduling optimization

Data Preparation:

• Block model validation and quality assurance

• Setting up precedence matrices and spatial relationships

• Defining geotechnical constraints and slope stability requirements

• Configuring equipment fleets and production capacities

• Establishing processing plant constraints and blending requirements

Practical Exercises:

• Analyzing block models for scheduling suitability

• Calculating block economic values with multiple scenarios

• Setting up precedence relationships in scheduling software

Day 2: Long-Term Strategic Scheduling

Morning: Strategic Schedule Development Theory

Understanding mathematical optimization approaches and algorithms that generate life-of-mine schedules maximizing NPV while respecting operational constraints.

Learning Outcomes:

• Mathematical optimization fundamentals: linear programming, mixed-integer programming

• Objective functions: NPV maximization versus alternative objectives

• Constraint types: mining capacity, processing capacity, blending, stockpile management

• Mining sequencing rules: precedence, minimum mining width, access requirements

• Pushback development and phase scheduling strategies

• Waste stripping strategies: pre-stripping versus concurrent stripping

• Grade control and dilution management in scheduling

Optimization Techniques:

• Heuristic methods: greedy algorithms, Lagrangian relaxation

• Exact methods: branch-and-bound, cutting plane algorithms

• Metaheuristic approaches: genetic algorithms, simulated annealing

• Understanding solver technology: CPLEX, Gurobi, and commercial optimizers

• Trade-offs between solution quality and computation time

Afternoon: Strategic Scheduling with Minemax/Datamine

Hands-on application of strategic scheduling software to create optimized long-term production schedules.

Learning Outcomes:

• Software interface navigation and project setup

• Importing block models and defining mining constraints

• Configuring mining fleets, processing plants, and stockpiles

• Setting up blending constraints for grade and quality control

• Running schedule optimization and interpreting results

• Analyzing schedule outputs: production profiles, grade curves, NPV progression

• Sensitivity analysis on key parameters: price, cost, capacity

Software Applications:

• Creating strategic schedules maximizing NPV

• Implementing stockpiling strategies for grade blending

• Evaluating multiple processing scenarios and expansions

• Analyzing equipment requirements and capital expenditure timing

• Comparing alternative mining strategies and pushback sequences

Practical Project:

• Complete strategic schedule for a sample deposit

• Analyzing production tonnage, grade delivery, and stripping ratio profiles

• Evaluating NPV under different economic scenarios

• Presenting schedule recommendations and sensitivity analysis

Day 3: Short-Term and Operational Scheduling

Morning: Short-Term Planning Methodology

Transitioning from strategic frameworks to operational execution through detailed short-term scheduling that balances daily/weekly production targets with long-term strategic objectives.

Learning Outcomes:

• Short-term scheduling objectives: meeting mill feed requirements, maintaining equipment productivity

• Translating strategic schedules into operational plans

• Dig block definition and mining polygon creation

• Equipment allocation and fleet management strategies

• Shift planning and crew allocation

• Grade control integration: blast hole sampling, reconciliation

• Managing variability and uncertainty in short-term execution

Operational Constraints:

• Daily/weekly production targets and contractual obligations

• Equipment availability, maintenance schedules, and downtime

• Blending requirements: multiple sources, stockpile utilization

• Weather considerations and seasonal operational challenges

• Mining rate limitations: face availability, drill-blast cycle times

• Haul road capacity and traffic management

Afternoon: Blending Optimization and Grade Control

Advanced techniques for optimizing ore blending to meet processing specifications while maximizing throughput and recovery.

Learning Outcomes:

• Blending theory: linear versus non-linear blending constraints

• Multi-source blending: pit sources, stockpiles, low-grade ore

• Quality specifications: head grade, contaminants, metallurgical properties

• Stockpile management strategies: grade segregation, reclaim sequencing

• Cut-off grade optimization for multiple destinations

• Real-time grade control and schedule adjustment

• Reconciliation between planned and actual production

Blending Applications:

• Creating blending matrices for multiple ore sources

• Optimizing stockpile build and reclaim strategies

• Managing metallurgical variability through blending

• Implementing penalty functions for specification violations

• Using blending to buffer short-term grade variability

Hands-On Exercises:

• Developing weekly production schedules with blending constraints

• Optimizing multi-source blending to meet mill specifications

• Analyzing trade-offs between grade control and production rate

• Creating operational plans integrating grade control data

Day 4: Equipment Scheduling and Fleet Optimization

Morning: Equipment Fleet Management

Optimizing equipment deployment, allocation, and productivity to achieve production targets cost-effectively while managing capital and operating expenditure.

Learning Outcomes:

• Fleet sizing methodology: matching equipment to production requirements

• Equipment selection: excavators, loaders, haul trucks, auxiliary equipment

• Productivity analysis: cycle times, availability, utilization, efficiency

• Equipment costing: ownership costs, operating costs, maintenance costs

• Haulage optimization: shortest path algorithms, traffic management

• Load-haul modeling and simulation techniques

• Equipment replacement strategies and technology selection

Fleet Optimization:

• Calculating required fleet size for production targets

• Matching excavator and truck capacities for optimal productivity

• Optimizing truck-shovel assignments and dispatch strategies

• Analyzing bottlenecks and equipment constraints

• Evaluating autonomous haulage systems versus conventional operations

• IPCC (in-pit crushing and conveying) versus truck-shovel economics

Afternoon: Schedule Optimization and Scenario Analysis

Advanced optimization techniques and scenario evaluation to support decision-making under uncertainty.

Learning Outcomes:

• Multi-objective optimization: NPV, production smoothing, infrastructure utilization

• Incorporating mining dilution and ore loss into schedules

• Risk analysis: commodity price volatility, geological uncertainty

• Stochastic scheduling approaches and robust optimization

• Scenario planning: base case, optimistic, pessimistic scenarios

• Real options analysis: expansion, deferral, closure decisions

• Schedule flexibility and adaptation strategies

Advanced Applications:

• Conducting comprehensive sensitivity analysis on schedules

• Evaluating multiple commodity price scenarios

• Analyzing impacts of processing plant expansions

• Optimizing phased development strategies

• Incorporating geological uncertainty through stochastic models

Software Demonstrations:

• Running multiple optimization scenarios efficiently

• Comparing schedule alternatives using economic metrics

• Developing decision trees for staged investment

• Creating risk-adjusted production schedules

Day 5: Schedule Implementation and Advanced Topics

Morning: Schedule Execution and Reconciliation

Bridging the gap between planned schedules and operational reality through execution management, monitoring, and continuous improvement.

Learning Outcomes:

• Schedule implementation planning and communication

• KPI tracking: tonnes moved, grade delivered, equipment productivity

• Reconciliation methodology: comparing planned versus actual performance

• Identifying and analyzing schedule variances

• Root cause analysis for production shortfalls

• Continuous improvement and schedule updating cycles

• Integration with mine operations and production control systems

Performance Management:

• Establishing schedule compliance metrics

• Creating dashboards for real-time schedule monitoring

• Variance analysis techniques and reporting

• Corrective action planning and schedule re-optimization

• Learning from execution to improve future schedules

Afternoon: Advanced Topics and Industry Trends

Exploring emerging technologies, optimization methods, and industry best practices shaping the future of mine production scheduling.

Learning Outcomes:

• Artificial intelligence and machine learning in scheduling optimization

• Real-time scheduling: integrating IoT, sensors, and fleet management systems

• Digital twin technology for schedule simulation and validation

• Autonomous operations integration: equipment scheduling for automated fleets

• Sustainability considerations: carbon footprint optimization, ESG goals

• Integrated mine planning: combining scheduling with maintenance, geology, metallurgy

Industry Best Practices:

• Case studies: successful schedule optimization projects with documented NPV improvements

• Software integration: connecting scheduling with ERP, geology, and surveying systems

• Organizational structures for effective scheduling functions

• Change management for implementing optimized schedules

Final Assessment:

• Capstone project presentation: complete scheduling study

• Peer review and feedback session

• Certificate of completion

• Professional development pathways discussion

Course Deliverables

• Educational/trial licenses for scheduling software (subject to vendor agreements)

• Comprehensive training manual with optimization methods and case studies

• Schedule templates and calculation spreadsheets

• Sample block models and project datasets

• Video tutorials for software workflows

• Professional development certificate

• Alumni network for ongoing technical support

Why Choose This Course?

Comprehensive Curriculum: Complete coverage from strategic life-of-mine scheduling through daily operational planning in one integrated program.

Software Proficiency: Hands-on training with industry-standard platforms (Minemax Scheduler, Datamine) used globally.

NPV Focus: Strong emphasis on value optimization, not just feasibility, delivering measurable economic improvements.

Practical Application: 65% hands-on exercises with real datasets ensuring immediately applicable workplace skills.

Expert Instruction: Experienced mine planners and optimization specialists with track records of delivering schedule improvements.

Career Advancement: Production scheduling expertise is highly valued, opening opportunities in mine planning, technical services, and consulting.

Conclusion

The Surface Mine Production Scheduling course delivers essential skills for maximizing mine value through optimized production planning. Master strategic and operational scheduling techniques that directly impact project economics, operational efficiency, and competitive advantage.

Enroll today to transform your scheduling capabilities and unlock hidden value in mining operations through data-driven optimization.

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