Pertemuan 1 (OR, an interesting field).pptx

7/29/2019 Pertemuan 1 (OR, an interesting field).pptx

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Operations Research and

Optimization: an interesting fieldRuhul A Sarker

School of Engineering and ITUNSW Canberra, Australia

Presented with a little modification by:

Samsul Amar


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Background

PhD (IE/OR) 1991, Daltech, DalhousieUniveristy, Canada

M.Engg (IPE) 1984 & B.Sc. Eng. (ME)1982

Deputy Head of School (Research),School of Engineering & IT, University ofNew South Wales, Australia


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OR in the News (Sept. 2009):J apanese PM has PhD in OR

On August 31, when the Democratic Party ofJapan defeated the long-ruling LiberalDemocrats, the party leader, Yukio Hatoyama,became the presumptive prime minister.

Dr. Yukio Hatoyama earned his PhD inOperations Research at Stanford University inthe 1970s. His PhD advisor was the late GeraldLieberman.

His fellow operations researchers at INFORMS(INstitute ForOperations Research andManagement Science) wish him luck andsuccess as he applies his analytical expertise tosolving his country's economic challenges.


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John Malone, chairman of Liberty Media, cablemagnate, and number 488 on Forbesmagazinesbillionaires listing (with $2 billion). Got his Ph.D. from

Johns Hopkins. His paper with Manny Bellmore on thetraveling salesman problem continues to bereferenced a lot.

Bill Gates of Microsoft, coauthored a paper with

Christos Papadimitriou on a sorting algorithm, thoughhe did not receive a Ph.D. (not counting his fourhonorary ones).

Virgil Carter, quarterback for the NFLs CincinattiBengals, wrote papers on football and OR forOperations Research and Management Science in the1970s (but did not get a Ph.D.: he is an MBA from

Northwestern).
http://en.wikipedia.org/wiki/John_Malonehttp://www.forbes.com/lists/2007/10/07billionaires_John-Malone_ZP8G.htmlhttp://scholar.google.com/scholar?q=bellmore+malone&hl=en&lr=&btnG=Searchhttp://en.wikipedia.org/wiki/Bill_Gateshttp://www.cs.berkeley.edu/~christos/papers/Bounds%20For%20Sorting%20By%20Prefix%20Reversal.pdfhttp://www.cs.berkeley.edu/~christos/papers/Bounds%20For%20Sorting%20By%20Prefix%20Reversal.pdfhttp://www.bengalszone.com/article.php?sid=541http://links.jstor.org/sici?sici=0030-364X(197103/04)19:2%3C541:OROF%3E2.0.CO;2-Ehttp://links.jstor.org/sici?sici=0025-1909(197812)24:16%3C1758:OSOFD%3E2.0.CO;2-Mhttp://links.jstor.org/sici?sici=0025-1909(197812)24:16%3C1758:OSOFD%3E2.0.CO;2-Mhttp://links.jstor.org/sici?sici=0030-364X(197103/04)19:2%3C541:OROF%3E2.0.CO;2-Ehttp://www.bengalszone.com/article.php?sid=541http://www.cs.berkeley.edu/~christos/papers/Bounds%20For%20Sorting%20By%20Prefix%20Reversal.pdfhttp://www.cs.berkeley.edu/~christos/papers/Bounds%20For%20Sorting%20By%20Prefix%20Reversal.pdfhttp://en.wikipedia.org/wiki/Bill_Gateshttp://scholar.google.com/scholar?q=bellmore+malone&hl=en&lr=&btnG=Searchhttp://www.forbes.com/lists/2007/10/07billionaires_John-Malone_ZP8G.htmlhttp://en.wikipedia.org/wiki/John_Malone


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Hedy Lamarr, actress and famous beauty, invented amethod for hopping frequencies in wirelesscommunication.

Dan Gimaldi, actor who played the twin Parisi brotherson the Sopranos, has a masters in OR and a Ph.D. indata processing. He has a position in the Departmentof Mathematics and Computer Science at

Kingsborough Community College. Anyone know whathe works on?

Gomory himself is a good example: terrific OR

credentials but even better known as President of theSloan Foundation.
http://en.wikipedia.org/wiki/Hedy_Lamarrhttp://en.wikipedia.org/wiki/Dan_Grimaldihttp://www.kingsborough.edu/academicDepartments/math/index.htmlhttp://www.kingsborough.edu/academicDepartments/math/index.htmlhttp://en.wikipedia.org/wiki/Ralph_Gomoryhttp://scholar.google.com/scholar?q=gomory&hl=en&lr=&btnG=Searchhttp://scholar.google.com/scholar?q=gomory&hl=en&lr=&btnG=Searchhttp://scholar.google.com/scholar?q=gomory&hl=en&lr=&btnG=Searchhttp://scholar.google.com/scholar?q=gomory&hl=en&lr=&btnG=Searchhttp://en.wikipedia.org/wiki/Ralph_Gomoryhttp://www.kingsborough.edu/academicDepartments/math/index.htmlhttp://www.kingsborough.edu/academicDepartments/math/index.htmlhttp://en.wikipedia.org/wiki/Dan_Grimaldihttp://en.wikipedia.org/wiki/Dan_Grimaldihttp://en.wikipedia.org/wiki/Hedy_Lamarrhttp://en.wikipedia.org/wiki/Hedy_Lamarrhttp://en.wikipedia.org/wiki/Hedy_Lamarr


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Carlos Slim (The richest man on the world):

Do you think your engineering training has helped

you to evaluate business opportunities and risk?

Carlos Slim: Well, I think talking about curiosity...When you love life, you want to know more about life. I thinkengineering is important to give you the tools. And in business, toanalyze, it's important to simplify, to have the essential points, theessential issues, the fundamentals, the basics, and take outeverything that is secondary, to have less variables to study or tolook at. Because when you have a lot of variables, and you don'tmake a distinction between the ones that are essential and thesecondary ones and parameters, you have confusion. I will say

that engineering is important, but also when I was finishing my(academic) career, I make my thesis about linear programming.That's operational research. That's to optimize functions, optimizeresults of things, and the study ofmodels that take you to the

optimization of the solutions.


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Have you carried out that theory of optimizing

solutions throughout your business ventures?

Carlos Slim: I think so. When I was studying that 45years ago, the parametric linear programming was veryinteresting, because the problems in the models werethe parameters. That's very difficult to define precisely.

The parametrics give you the difference between whichsolution is optimal. One teacher said that an engineer issomeone who makes with one dollar what others thatare not engineers make with two dollars.


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Outline

An opening storey

Introduction to OR

Our research in OR

Traditional OR

Evolutionary Optimisation

Discussions

8 9 10 11 12 13 14 15 16 17 18

11

14

17

175000

185000

195000

205000

215000

225000

Totalcos

Value ofn

Valueofm


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An Opening StoryTortoise vs Hare: Who is faster?

Once upon a time a tortoise and a hare had anargument about who was faster.

Settle the argument with a race.

Agreed on a route and started off the race.

The hare was ahead.

The hare sat under the tree and soon fell asleep.


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Tortoise vs Hare: Who is faster?

The tortoise plodding on overtook him and soonfinished the race, emerging as the undisputedchamp.

The hare woke up and realized that hed lost therace.

The moral - Slow and steady wins the race.

A story that weve all grown up with.


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Tortoise vs Hare: Who is faster?

Who is really faster Hare or Tortoise?


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Tortoise vs Hare:The Storey Goes on (1)

The hare was disappointed at losing the race.

He lost the race only because he had been

overconfident, careless and lax. If he had not taken things for granted, theres no

way the tortoise could have beaten him.

So he challenged the tortoise to another race. The tortoise agreed.


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This time, the hare went all out and ran withoutstopping from start to finish.

He won by several miles. The moral - Fast and consistent will alwaysbeat the slow and steady.

Its good to be slow and steady; but its better

to be fast and reliable.


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There is no way the tortoise can beat the hare ina race the way it was currently formatted.

The tortoise challenged the hare to another

race, but on a slightly different route. The hare agreed.

They started off.

In keeping with his self-made commitment to beconsistently fast, the hare took off and ran at topspeed until he came to a broad river.


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The finishing line was a couple of kilometres onthe other side of the river.

The hare sat there wondering what to do.

In the meantime the tortoise trundled along, gotinto the river, swam to the opposite bank,continued walking and finished the race.

The moral - First identify your corecompetency and then change the playingfield to suit your core competency.


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The hare and the tortoise, by this time, hadbecome pretty good friends.

Both realised that the last race could have been

run much better. So they decided to do the last race again, but to

run as a team this time.

They started off, and this time the hare carried

the tortoise till the riverbank. There, the tortoise took over and swam across

with the hare on his back.


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On the opposite bank, the hare again carried thetortoise and they reached the finishing linetogether.

They both felt a greater sense of satisfactionthan theyd felt earlier.

The moral - Its good to be individually brilliant andto have strong core competencies; but unless youre

able to work in a team and harness each others corecompetencies, youll always perform below parbecause there will always be situations at whichyoull do poorly and someone else does well.


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Tortoise vs Hare:Who is the Winner?

Our core question:

Faster or Winner?

Who is the winner & why? How can we make sure that the faster will be the

winner?

Performance measures

Environments /constraints


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Operations Research

The science of better and responsiblemanagement

Interdisciplinary: Mathematics, Statistics,Computing, Business/Management, EngineeringManagement

Useful for all levels of decision making Operations Research (OR)

Hard OR: Quantitative Soft OR: Qualitative

Operations Research (OR) Optimisation Simulation


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Operations Research:Problem Solving Cycle


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Decision Problem & Mathematical Model:A Simplified Example

Blue Ridge Hot Tubs produces two types of hottubs: Aqua-Spas & Hydro-Luxes.

There are 200 pumps, 1566 hours of labor,and 2880 feet of tubing available.

Aqua-Spa Hydro-Lux

Pumps 1 1Labor 9 hours 6 hours

Tubing 12 feet 16 feetUnit Profit $350 $300


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Model for Blue Ridge Hot Tubs

Decision Variables:X1=number of Aqua-Spas to produceX2=number of Hydro-Luxes to produce

MAX: 350X1 + 300X2S.T.: 1X1 + 1X2


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Optimisation Problem Classification

Differentiable

orNon-differentiable

Convex

or

Non-convex

Linear

or

Nonlinear

Level 1: General

problem

Level 3: Problem

classification

Level 4: Variable

classification

Level 5: Function

classification

Problem

Unconstrained Constrained

Continuous Integer/Discrete

Single Objective Multiple Objectives

Mixed

Level 2:

Objective

classification


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Our Research

Applied Operations research

Defining decision situations as OR problems

Modelling

Solution approaches

Evolutionary Optimisation

Development of algorithms

Solving practical problems


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Applications(1)

Agriculture Crop planning

Mining Coal mining: Planning & Scheduling

Oil mining: Production planning Manufacturing

Production planning Job-shop scheduling

Machine layout & job scheduling Climate change

Planned human relocation


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Applications(2) Logistics and Supply Chain

Inventory management Transportation Defence logistics Joint maintenance-inventory systems Joint production-inventory systems

Supply chain modelling & analysis Supply chain under disruption

Rostering Doctors in Hospital Maintenance crews for Airlines

Other Forex exchange Rate Prediction Share market forecast Sales/demand forecast


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Crop Planning(1)

National or regional problem

n crops (usually 100+)

Limited land

Different soil characteristics

Different type: single-, double-, or triple-crop

Combination of crops

Certain regions /sub-regions suitable for certain crops

Different input requirements

Different yield rates


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Crop Planning(2)

Local demand must be fulfilled

Import/export restrictions

Budget limitation

Manpower & other resource limitation Decision problem:

Determine the areas of land to be used for differentcrops

Maximise return or profit not violating the constraints Can be formulated as a single or multiobjective

LP or NLP model.


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Crop Planning(3)

New way of solving crop planning problem

Better solution than existing practices

Key Publications:

Sarker, R. and Ray, T. (2009) An Improved Evolutionary Algorithmfor Solving Multi-objective Crop Planning Models, Computers andElectronics in Agriculture, 68, pp191-199. (ISI Impact factor 1.273).

Sarker, R. and Quaddus, M. (2002) Modelling a Nationwide CropPlanning Problem Using a Multiple Criteria Decision Making Tool,

Int. J. of Computers and Industrial Engineering, 42/2-4, pp541-553.

(ISI Impact Factor 1.057) Sarker, R., Talukder, S. and Haque, A. (1997) Determination of

Optimum Crop-Mix for Crop Cultivation in Bangladesh,AppliedMathematical Modelling, 21, pp621-632. (ISI Impact Factor 0.931)


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Fleet Mix Problem(DSTO/Army)

MISG 2002

Presentation in Canberra

Future fleet-mix: spend billions Seed funding from 2002-2003


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Optimisation Problem

Minimising the number of vehicles

Subject to

Performing all (or given % of) the tasks on time

Mobility criteria Material type & mix

Vehicle type & capacity

Weight & volume

Driving hours & rest/break

Passengers & others


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Difficulties

Timeliness is a crucial factor

Unmanageable problem size in terms of the

number of variables and constraints

Impossible to solve using the existingoptimisation techniques and current computing

resources


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Heuristic Approach

To find workable solutions within areasonable computational effort

Our approach: Minimise the number of vehicles required by

maximising the capacity utilisation ofindividual vehicle and overall vehicle

effectiveness while satisfying all theconstraints


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Planned HumanRelocation(1)

Due to climate change or surface mining A number of regions to be evacuated

Fully .. not suitable for living

Partially .. new opportunity Relocated to a number of other regions Basis of relocation

Job/business opportunity in new locations

Individual & family preference Logistics for relocations Resources available for settlement

Budget limitation


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Approach Integrated AHP & GP

Key Publications Zahir, Z. and Sarker, R. (2008) Optimizing Multi-Objective

Decisions for Planned Relocation of People in Surface Miningfor a Densely Populated Environment, 2008 Conference of the

Administrative Sciences Association of Canada(ASAC2008),Halifax, Canada, Received Best Paper Award.

Zahir, S. and Sarker, R. (2009) An Interactive DSS forImplementing Sustainable Relocation Strategies for

Adaptation to Climate Change: A Multi-Objective OptimizationApproach, International Journal of Mathematics in OperationsResearch, 1(3), pp326-350.


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Media Coverage Wall Street Journal

Science Daily

United Press International (UPI)

COP15: UN Climate Change Conference, 2009 Times of the Internet

TerraDaily

MarketWatch

EcoEarth

Eurocean Softpedia

Oneindia

ScienceBlog

Planned Human


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Coal Mining (1)

A number of operating mines with differentproduction capacity

Quality of coals varies - mine to mine & period toperiod

Customer demands and contract specifications Price of coal is a function of quality parameters Raw coal quality can be upgraded through

mechanical process and blending with better

quality coal Upgradation capacity Environmental factor for supplied coals


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Coal Mining (2)

Decision Problem Determine the yearly production plan by

maximising the total revenue

Subject to Production capacity

Quality specification

Demand fulfilment

Environmental condition Employment level

Resource limitation

Wash-plant

Mi 1


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Coal Mining (3)

Developed algorithms /heuristics Better than current practices Key Publications:

Sarker, R. (2009) Alternative Mathematical Programming Models: ACase for Coal Blending Decision Process, Optimization: Structure andApplications, Springer Series in Optimization and its Application,

Volume 32, pp383-399. Sarker, R. and Gunn, E. (1997) A Simple SLP Algorithm for Solving a

Class of Nonlinear Programs, European Journal of OperationalResearch, 101/1, pp140-154. (ISI Impact Factor 1.627)

Sarker, R. and Gunn, E. (1995) Determination of a Coal PreparationStrategy Using a Computer Based Enumeration Method, IndianJournal of Engineering and Material Sciences, 2, pp150-156. (ISIImpact Factor 0.197)

Sarker, R. and Gunn, E. (1994) Coal Bank Scheduling Using aMathematical Programming Model,Applied Mathematical Modelling,18, pp672-678. (ISI Impact Factor 0.931)

BlendingProcess

Mine-1

Mine-2

Period-1

BlendingProcess

Mine-1

Mine-2

Period-2

BlendingProcess

Wash-plant

Mine-1

Mine-2

Period-3

Wash-plant

Mine-n

Washedcoal inv.

Washedcoal inv.

Washedcoal inv.

Pileor

Customer

Mine-n

Mine-n

Pileor

Customer

PileorCustomer


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Supply Chain &Inventory Management (1)

Inventory is usually optimised for individual sub-systems

The solution is sub-optimal

Our research: Two stage production-inventory system underdifferent conditions

Joint maintenance-spare inventory plan Multi-stage inventory system under transportation

restriction Material flow analysis in supply chain Supply chain design & redesign


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Better solutions than single system approach Better solutions than existing practices Key publications:

Zahir, S. and Sarker, R. (2010) Optimizing Multi-Objective LocationDecisions in a Supply Chain using an AHP-Enhanced Goal

Programming Model, International Journal of Logistics Systems andManagement, Accepted, publication scheduled for 2010. Sarker, R. and Zahir, S. (2008) Supply Chain Expansion using AHP,

ILP and Scenario-Planning,The Journal of American Academy ofBusiness, 12 (3), pp21-29.

Khan, L. and Sarker, R. (2002) An Optimal Batch Size for a JITManufacturing System, International Journal of Computers and

Industrial Engineering, 42/2-4, pp127-136. (ISI Impact Factor 1.057) Sarker, R. and Khan, L. (2001) Optimum Batch Size under Periodic

Delivery Policy, International Journal of Systems Sciences, 32(9),pp1089 1099. (ISI Impact Factor 0.634)


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Sarker, R., Runarsson, T. and Newton (2001) A Constrained Multiple RawMaterials Manufacturing Batch Sizing Problem,International Transaction inOperational Research, 8(2), pp121-138.

Sarker, R. and Haque, A. (2000) Optimization of Maintenance and SpareProvisioning Policy Using Simulation,Applied Mathematical Modelling, 24(10),pp751-760. (ISI Impact Factor 0.931)

Sarker, R., (2000) A Note on Production Capacity Planning and Control in Multi-

Stage Manufacturing, Journal of the Operational Research Society, 51(5), pp639-640. (ISI Impact Factor 0.839) Sarker, R. and Khan, L. (1999) An Optimal Batch Size for a Production System

Operating under a Periodic Delivery Policy, Computers and Industrial Engineering:An International Journal, 37(4), pp711-730. (ISI Impact Factor 1.057)

Sarker, R., Karim, M. and Haque, A. (1995) An Optimum Batch Size for aProduction System Operating under a Continuous Supply/Demand, InternationalJournal of Industrial Engineering- Theory, Applications and Practice, 2(3), pp189-

198. Zahir, S. and Sarker, R. (1991) Joint Economic Ordering Policies of Multiple

Wholesalers and Single Manufacturer with Price-Dependent Demand Functions,Journal of the Operational Research Society, 42(2), pp157-164. (ISI Impact Factor0.839)


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Gas Lift Optimisation(1)

Underground oil reservoir

Multiple wells for oil extraction through gas injection

The total daily use of gas is limited.

Oil extracted from each well is a nonlinear function of thegas injected into a well and varies between wells.

The problem is to identify the optimal amount of gas thatneeds to be injected into each well to maximize the

amount of oil extracted subject to the constraints.


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Formulated as an integer programming problem

Solved using an evolutionary algorithm

Our results show significant improvement overthe existing practices.

The multiobjective formulation is useful as iteliminates the need to solve such problems on a

daily basis.


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Shell International tested our algorithm

World OilMagazine published a feature article on thiswork in April 2008

Key Publications: Ray, T. and Sarker, R. (2007) Genetic Algorithm for Solving a

Gas Lift Optimization Problem, Journal of Petroleum Scienceand Engineering, Elsevier, 59, pp84-96. (ISI Impact Factor0.824, A* Journal in Engineering)

Ray, T. and Sarker, R. (2007) Optimal Oil Production Planningusing an Evolutionary Approach, In Evolutionary Scheduling,Studies in Computational Intelligence, Springer-Verlag, pp273-292.


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J ob-Shop Scheduling

Number of jobs on a fixed number ofmachines

Makespan minimisation

Developed three heuristics to enhance theperformance of GAs

Process interruption

Machine breakdown and unavailability

Re-optimisation


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An example of a 6 jobs 6 machines problem


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Key Publications

S. M. K. Hasan, R. Sarker, D. Essam (2010) Genetic Algorithm for Job-Shop Scheduling with Machine Unavailability and Breakdown,International Journal of Production Research, 49(16), pp.4999-5015.

Hasan, S. M. K., Sarker, R., Essam, D. and Cornforth, D. (2009) MemeticAlgorithms for Solving Job-Shop Scheduling Problems, MemeticComputing, Springer Journal, 1(1), pp69-83.

S M K Hasan, R. Sarker, D. Essam, I. Kaceem (2011) A DSS for JobScheduling under Process Interruptions, Journal of Flexible Services andManufacturing, Springer, 23(2), pp.137-155.

Hasan, S. M. K., Sarker, R., Essam, D. and Cornforth, D. (2009) AGenetic Algorithm with Priority Rules for Solving Job-Shop SchedulingProblems, Natural Intelligence for Scheduling, Planning and PackingProblems, R. Chiong (ed.), Springer-Verlag series in Studies inComputational Intelligence, Accepted

S. M. K. Hasan, R. Sarker and D. Cornforth (2008) GA with Priority Rulesfor Solving Job-Shop Scheduling Problems, IEEE Congress onEvolutionary Computation (IEEE-CEC) within World Congress onComputational Intelligence (WCCI), 1-6 June, 2008, Hong Kong, pp1913-1920.

M hi L d


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Machine Layout andJ ob Scheduling

Combined machine layout and job assignment problem

Arranging Nmachines to Mlocations (/cells), M>= N

Assigning O operations of each product to Nmachines

Any layout type (linear, rectangular, circular, ) A machine can perform one to many operations

The number of machines of any particular type is notrestricted

A job may visit a subset of the machines and may visitone or more machines more than once

Any number of products or product type

M hi L d6000


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Machine Layout andJ ob Scheduling

Determine the machine layout and

The job scheduling

Minimise the system cost production cost

material handling cost

Bi-objective problem Minimize system cost

Minimize the machine load imbalance

1000

2000

3000

4000

5000

50833 53167 59833 62000

Cost

MachineLoadImbalance


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Key Publications

Ray, T. and Sarker, R. (2008) EA for solving combined machinelayout and job assignment problems, Journal of Industrial andManagement Optimization, 4(3), pp631-646 (ISI Impact Factor1.181)

Ray, T. and Sarker, R. (2007) Optimal Oil Production Planningusing an Evolutionary Approach, In P. Cowling, KC Tan and K.Dahal (eds.), Evolutionary Scheduling, Studies in ComputationalIntelligence, Springer-Verlag, pp273-292.

Sarker, R., T. Ray and J. B. da Fonseca (2007) An EvolutionaryAlgorithm for Machine Layout and Job Assignment Problems,IEEE Congress on Evolutionary Computation(IEEE-CEC),

September 25-28, 2007, Singapore, pp3991-3997.

I t M d l


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Inventory Modelswith GA

Inventory models

Multiple products

Capacity constraints Transport limitation

Nonlinear constrained model

Multimodal model


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Key Publications

Sarker, R. and Newton, C. (2002) Genetic Algorithm for SolvingEconomic Lot Size Scheduling Problem, International Journal ofComputers and Industrial Engineering, 42(2-4), pp189-198. (ISIImpact Factor 1.057)

Sarker, R., Maulloo, A., and Rahman, M. (2005) Evolutionary

Algorithm for Solving a Practical Multi-modal Problem: A CaseStudy, Complexity International, Volume 11, pp162-170.

Sarker, R., Runarsson, T. and Newton, C. (2001) GeneticAlgorithms for Solving A Class of Constrained Nonlinear IntegerPrograms,International Transaction in Operational Research, 8(1),pp61-74.

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Pertemuan 1 (OR, an interesting field).pptx