Optimization Linear Programming in the MineCus Material Haulage

7/27/2019 Optimization Linear Programming in the MineCus Material Haulage

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Optimization & Linear

Programming in the Mines

Material Haulage

Andree RttigGerente GeneralModular Mining Systems Chile


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The contents of these materials are protected by federal and international intellectual

property laws. No portion of these materials may be reprinted, republished, modified,

reproduced, sold or distributed in any form without the express written consent of

Modular Mining Systems, Inc. Modular, IntelliMine, DISPATCH, MineCare,

ProVision, ShiftBoss, RoadMap, MasterLink, PowerView, and ModularReady are

trademarks and/or registered trademarks and the sole and exclusive property of Modular

Mining Systems, Inc. These materials may contain third party copyright and/or trademark

materials, the use of which may not always have been specifically authorized by theintellectual property owner. All copyrights and/or trademarks contained in these materials

are the sole and exclusive property of their respective owners.

These materials, including third party information, are provided for information purposes

only. Actual specifications may vary from those documented in these materials. Consult

your local Modular Mining office for further details.

Copyright 2012 Modular Mining Systems, Inc. All rights reserved.

Legal Notice


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Introduction

Dispatch Optimization Strategy

Fleet Management Case Studies Cost Based Optimization

Outline


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Problem

How can we maximize the production by

avoiding:

1. Shovels without trucks

2. Trucks waiting at shovels

and by respecting:3. Operational restrictions


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Source: Wikipedia

Matrix form of the simplex algorithm

George Bernard Dantzig (Nov 8, 1914 May 13, 2005)

was an American mathematician, and the ProfessorEmeritus of Transportation Sciences and Professor of

Operations Research and of Computer Science at

Stanford.

George Dantzig is known as the father of linear

programming and as the inventor of the simplex

method, an algorithm for solving linear programming

problems.

Father of Linear Programming


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Elements of the Dispatching Problem

In the design and development of a real-time dispatching system, accurate

information on the following, among others, must be taken into consideration.

mine topography and haul route network profile

shovel locations and dig rates dump site locations and capacities

material quality and grade information on ore deposits

vehicle-shovel matching restrictions

This information is utilized by algorithms embedded in DISPATCH to address

key optimization issues; their outputs integrate for the generation of optimal

truck assignments.


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Introduction



Outline


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A Dynamic Optimization Strategy

Mining operations are dynamic, resulting in non-static resource levels and

performance quantities. DISPATCH is cognizant of this.


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BP, LP & DPLinear Programming B.P. Options


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BP, LP & DPDISPATCH System Selected Paths


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Dispatch Optimization Strategy LP Model

The LP model creates a theoretical master plan for maximizing overall truck

productivity in the mine, it contains optimized production circuits that

indicate:

Which dumping points should provide haulage resources (empty trucks)

to shovels, the specific truck types, and the feed rates at which theresources should cover the shovels.

Which shovels should provide haulage resources (loaded trucks) to

which dumping points and the feed rates at which the resources should

cover the dumping points

LP decides which circuits to create by assigning a truck productivity weight

LP calculates number of trucks required for each LP Path.


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Solution TechniquesThe Plan: Linear Programming and the Simplex Method

DISPATCH uses a simplex-

based procedure

The Simplex Method

exploits Dantzigs Corner

Point Theorem: an optimal

solution is located at anextreme point of the

feasible region of the

problem.

Other methods with very

good theoretical andcomplexity properties in

particular, interior-point

methods have been

developed.

Source: Wikipedia


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Dynamic Program (DP) attempts to enforce, in real time, the production

circuits in the theoretical LP solution.

DP builds a Truck and Shovel need lists. DP works to achieve balance and

synchronization while meeting the LP flow rates.

Truck-shovel pairing evaluations are made not only with respect to thetruck requiring immediate assignment, but also for trucks in the system that

are projected to require assignments within a time frame.

DP also builds future scenarios when generating assignments. Also

considering:

Locks, bars, fuel levels

Dispatch Optimization Strategy DP Model


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Introduction



Outline


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Productivity Reported by our Customers


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Case Study Kleinkopje Mine (AKK)

In 2008, Anglo installed the Modular Intellimine System at theKleinkopje mine.

As well as installing DISPATCH they used Change Management

Consulting Service offered by Modular to ensure a successful

installation.

The broke up the project into two phases

Phase I: productivity analysis following the installation but prior to

decommissioning of any legacy support systems to create a baselinecomparison.

Phase II: maximizing mine productivity within production requirements. This

included looking at real-time KPI data and optimizing asset utilization.


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Case Study Results

After all the project there were a series of

benefits that were realized:

1. Based on TKPH, AKK achieved a total

improvement in productivity of 15% through

the usage of DISPATCH

2. The labor-intensive process of manual load

recording at the crusher was eliminated

3. The previous issue of trucks running out of

fuel was virtually eliminated

4. A more effective fleet monitoring system

gave Pit Superintendents a better planning

strategy

5. Truck cycle times were reduced over 40%

0

10

20

30

40

50

60

July 25 Aug 2, 08 Dec 8-13,08 Jan 5-10, 09 Feb 2-9,09 Mar 26- Apr 2, 09

Bin(sec)

Date

Cycle Time (min)

40.7% TimeReduced


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Introduction


Fleet Management Case Studies

Cost Based Optimization

Outline


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FUEL

TIRES

PARTS &

MAINTENANCE

LABOROPERATIONAL

COST - PER - TON

TRUCK COSTS

Cost-Based Optimization


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Our Customers


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Tucson,

Arizona,

USA

Lima,

Peru

Santiago,

Chile

Belo

Horizonte,

MG, Brasil

Recife, PE,

Brasil

Bryanston,

Republic of

South Africa

Pune,

India

Balikpapan,

Kaltim,

Indonesia

Tuggerah,

NSW,

Australia

Beijing,

China

Moscow,

Russia

Port

Coquitlam,

BC, Canada

Global and Local


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Preguntas?

Dr.-Ing. Andree Rttig

Gerente Generaltel. +56 2 591 3000

e-mail: [email protected]

http://www.mmsi.com

Optimization Linear Programming in the MineCus Material Haulage

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