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Search About us Eudoxus of Cnidus Products and Services Planning Scheduling Deckling Sheet Size Selection Model Tools we use Aspen Mimi 1. Elements of the Mimi/E Language 1.1 Mimi/E as a Declarative Language 1.2 Entities and Data Types 1.3 Instantiation and Assignment of Variables 1.4 How Mimi Compares Numbers 1.5 Statements 1.6 Rules, Rule Sets and Predicates 1.7 Calling Mechanism for Predicates 2. How the Mimi Interpreter Works 2.1 Example Rule and its Procedural Analogue 2.2 Branch Structure of a Rule 2.3 Traversing the Tree 2.4 Backtracking 2.5 Automating Traversal of the Tree 2.6 IN and While Loops 2.7 OR Blocks 3. Writing Procedural Code using Mimi 3.1 Preventing Backtracking 3.2 Writing Code for a Series of Tests 3.3 General Principles for Writing Loops 3.4 Jumping Out of a Loop 4. Advanced Programming Topics 4.1 Use of the Internal Set Index 4.2 Fail-safe Coding 4.3 Static and Dynamic References 4.4 Mixed Case Strings and Embedded Blanks in Rule Variables 4.5 Setting Up &Variables 4.6 Using &Variables 4.7 Replacing Mimi Commands with Your Own 4.8 Recursion Visual Basic C# Simul8 Xpress MP What is Optimization? MP in Action Why Mathematical Programming is Useful AN MP Approach to Strategic Planning Solving the Travelling Milk Collector's Problem A Comparative Survey of MP Software Wishing you a merry Christmas (with MP) How to Choose an LP Code Planning and Scheduling BP's Oil Refineries The Many Faces of Duality Data Envelopment Analysis How to Build a Mathematical Programming Model Farm Management by MP If you can't Win an Election ... Change the Voting Rules MP Modelling at Hoskyns But my Problem Isn't Linear! Aluminium Smelter Benefits from MP Consultancy Regulating Electricity - Preventing Another Own GOAL Non-Linear Optimization Using Extensions to LP Simulation and Optimization Join Forces to Schedule London's Water What Can Integer Programming Do? Using Idle PCs to Solve Scheduling Problems How to Minimize Capital Gains Tax Optimizing the Supply of Bulk Gases Mathematical Programming in the Oil Industry How to Succeed with Integer Programming Aircraft Swapping by Constraint Logic Programming New Directions in Integer Programming Prize-Winning Planning at Harris Semiconductors Data Envelopment Analysis and its Use in Banking Planning and Scheduling in Oil Refineries Modelling Telecommunications Networks How to Make Integer Programming Go Faster Modelling Oil Refineries using Linear Programming Representing Time in Refinery Models Optimizing a Black Art Making Best Use of the World's Favourite Runways The Well-Tempered Warehouse MIMI Brings OR Tools Together Pricing Mobile Phone Tariffs A Practical Perspective on Combinatorial Optimization XPRESS Accelerates Towards Hard Scheduling Problems Lecture Notes What is Mathematical Programming? Practical Linear Programming? 1. Introduction 2. Categories of Modelling Software 3. Practical Example: Oil Blending Model 4. Many-Component Blends 5. Minimising the Cost of the Blend 6. Limits on the Availability of Components 7. Blending by Weight and by Volume 8. Conclusions An Algebraic Approach to Formulation 1. Introduction 2. The Simple Oil Blending Model Revisited 3. Abstracting the Structure of the Problem 4. A Technique for Model Documentation 5. Guidelines for Formulating Models 6. The Transportation Problem 7. A Multi-Mix Blending Problem 8. A Processing Problem 9. Tackling Infeasibility Practical Interpretation of LP Results 1. Introduction 2. Geometrical Description of LP Solution 3. Algebraic Description of LP Solution 4. Sensitivity Analysis 4.1 The Oil Blending Model 4.2 Drawing Inferences from the Solution 4.3 Two-variable Problem 4.4 Reduced Cost 4.5 Alternative Optimal Solutions 4.6 Shadow Prices 5. Ranging Information 5.1 Introduction 5.2 Objective Ranging 5.3 Right Hand Side Ranging 5.4 Ranging the Simple Oil Blending Model Practical Integer Programming 1. Introduction 2. Is IP a Suitable Technique? 3. Discrete Variables 3.1 Integer Variables 3.2 Binary Variables 3.3 Using a Binary Variable 4. Semi-continuous Variables 5. Sets of Decision Variables 6. Special Ordered Sets of Type 2 7. How Integer Programming Codes Work 8. Guiding the Search 8.1 Decisions which the IP Code Takes 8.2 Special Ordered Sets 8.3 Priorities 8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation 9. Advanced Integer Programming 9.1 Travelling Salesman Problem 9.2 Cutting-Stock Problems 9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example Site Map Copyright Privacy
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About us
Eudoxus of Cnidus
Products and Services
Planning Scheduling Deckling Sheet Size Selection Model
Planning
Scheduling
Deckling
Sheet Size Selection Model
Tools we use
Aspen Mimi 1. Elements of the Mimi/E Language 1.1 Mimi/E as a Declarative Language 1.2 Entities and Data Types 1.3 Instantiation and Assignment of Variables 1.4 How Mimi Compares Numbers 1.5 Statements 1.6 Rules, Rule Sets and Predicates 1.7 Calling Mechanism for Predicates 2. How the Mimi Interpreter Works 2.1 Example Rule and its Procedural Analogue 2.2 Branch Structure of a Rule 2.3 Traversing the Tree 2.4 Backtracking 2.5 Automating Traversal of the Tree 2.6 IN and While Loops 2.7 OR Blocks 3. Writing Procedural Code using Mimi 3.1 Preventing Backtracking 3.2 Writing Code for a Series of Tests 3.3 General Principles for Writing Loops 3.4 Jumping Out of a Loop 4. Advanced Programming Topics 4.1 Use of the Internal Set Index 4.2 Fail-safe Coding 4.3 Static and Dynamic References 4.4 Mixed Case Strings and Embedded Blanks in Rule Variables 4.5 Setting Up &Variables 4.6 Using &Variables 4.7 Replacing Mimi Commands with Your Own 4.8 Recursion Visual Basic C# Simul8 Xpress MP
Aspen Mimi
1. Elements of the Mimi/E Language 1.1 Mimi/E as a Declarative Language 1.2 Entities and Data Types 1.3 Instantiation and Assignment of Variables 1.4 How Mimi Compares Numbers 1.5 Statements 1.6 Rules, Rule Sets and Predicates 1.7 Calling Mechanism for Predicates 2. How the Mimi Interpreter Works 2.1 Example Rule and its Procedural Analogue 2.2 Branch Structure of a Rule 2.3 Traversing the Tree 2.4 Backtracking 2.5 Automating Traversal of the Tree 2.6 IN and While Loops 2.7 OR Blocks 3. Writing Procedural Code using Mimi 3.1 Preventing Backtracking 3.2 Writing Code for a Series of Tests 3.3 General Principles for Writing Loops 3.4 Jumping Out of a Loop 4. Advanced Programming Topics 4.1 Use of the Internal Set Index 4.2 Fail-safe Coding 4.3 Static and Dynamic References 4.4 Mixed Case Strings and Embedded Blanks in Rule Variables 4.5 Setting Up &Variables 4.6 Using &Variables 4.7 Replacing Mimi Commands with Your Own 4.8 Recursion
1. Elements of the Mimi/E Language
1.1 Mimi/E as a Declarative Language 1.2 Entities and Data Types 1.3 Instantiation and Assignment of Variables 1.4 How Mimi Compares Numbers 1.5 Statements 1.6 Rules, Rule Sets and Predicates 1.7 Calling Mechanism for Predicates
1.1 Mimi/E as a Declarative Language
1.2 Entities and Data Types
1.3 Instantiation and Assignment of Variables
1.4 How Mimi Compares Numbers
1.5 Statements
1.6 Rules, Rule Sets and Predicates
1.7 Calling Mechanism for Predicates
2. How the Mimi Interpreter Works
2.1 Example Rule and its Procedural Analogue 2.2 Branch Structure of a Rule 2.3 Traversing the Tree 2.4 Backtracking 2.5 Automating Traversal of the Tree 2.6 IN and While Loops 2.7 OR Blocks
2.1 Example Rule and its Procedural Analogue
2.2 Branch Structure of a Rule
2.3 Traversing the Tree
2.4 Backtracking
2.5 Automating Traversal of the Tree
2.6 IN and While Loops
2.7 OR Blocks
3. Writing Procedural Code using Mimi
3.1 Preventing Backtracking 3.2 Writing Code for a Series of Tests 3.3 General Principles for Writing Loops 3.4 Jumping Out of a Loop
3.1 Preventing Backtracking
3.2 Writing Code for a Series of Tests
3.3 General Principles for Writing Loops
3.4 Jumping Out of a Loop
4. Advanced Programming Topics
4.1 Use of the Internal Set Index 4.2 Fail-safe Coding 4.3 Static and Dynamic References 4.4 Mixed Case Strings and Embedded Blanks in Rule Variables 4.5 Setting Up &Variables 4.6 Using &Variables 4.7 Replacing Mimi Commands with Your Own 4.8 Recursion
4.1 Use of the Internal Set Index
4.2 Fail-safe Coding
4.3 Static and Dynamic References
4.4 Mixed Case Strings and Embedded Blanks in Rule Variables
4.5 Setting Up &Variables
4.6 Using &Variables
4.7 Replacing Mimi Commands with Your Own
4.8 Recursion
Visual Basic
C#
Simul8
Xpress MP
What is Optimization?
MP in Action
Why Mathematical Programming is Useful AN MP Approach to Strategic Planning Solving the Travelling Milk Collector's Problem A Comparative Survey of MP Software Wishing you a merry Christmas (with MP) How to Choose an LP Code Planning and Scheduling BP's Oil Refineries The Many Faces of Duality Data Envelopment Analysis How to Build a Mathematical Programming Model Farm Management by MP If you can't Win an Election ... Change the Voting Rules MP Modelling at Hoskyns But my Problem Isn't Linear! Aluminium Smelter Benefits from MP Consultancy Regulating Electricity - Preventing Another Own GOAL Non-Linear Optimization Using Extensions to LP Simulation and Optimization Join Forces to Schedule London's Water What Can Integer Programming Do? Using Idle PCs to Solve Scheduling Problems How to Minimize Capital Gains Tax Optimizing the Supply of Bulk Gases Mathematical Programming in the Oil Industry How to Succeed with Integer Programming Aircraft Swapping by Constraint Logic Programming New Directions in Integer Programming Prize-Winning Planning at Harris Semiconductors Data Envelopment Analysis and its Use in Banking Planning and Scheduling in Oil Refineries Modelling Telecommunications Networks How to Make Integer Programming Go Faster Modelling Oil Refineries using Linear Programming Representing Time in Refinery Models Optimizing a Black Art Making Best Use of the World's Favourite Runways The Well-Tempered Warehouse MIMI Brings OR Tools Together Pricing Mobile Phone Tariffs A Practical Perspective on Combinatorial Optimization XPRESS Accelerates Towards Hard Scheduling Problems
Why Mathematical Programming is Useful
AN MP Approach to Strategic Planning
Solving the Travelling Milk Collector's Problem
A Comparative Survey of MP Software
Wishing you a merry Christmas (with MP)
How to Choose an LP Code
Planning and Scheduling BP's Oil Refineries
The Many Faces of Duality
Data Envelopment Analysis
How to Build a Mathematical Programming Model
Farm Management by MP
If you can't Win an Election ... Change the Voting Rules
MP Modelling at Hoskyns
But my Problem Isn't Linear!
Aluminium Smelter Benefits from MP Consultancy
Regulating Electricity - Preventing Another Own GOAL
Non-Linear Optimization Using Extensions to LP
Simulation and Optimization Join Forces to Schedule London's Water
What Can Integer Programming Do?
Using Idle PCs to Solve Scheduling Problems
How to Minimize Capital Gains Tax
Optimizing the Supply of Bulk Gases
Mathematical Programming in the Oil Industry
How to Succeed with Integer Programming
Aircraft Swapping by Constraint Logic Programming
New Directions in Integer Programming
Prize-Winning Planning at Harris Semiconductors
Data Envelopment Analysis and its Use in Banking
Planning and Scheduling in Oil Refineries
Modelling Telecommunications Networks
How to Make Integer Programming Go Faster
Modelling Oil Refineries using Linear Programming
Representing Time in Refinery Models
Optimizing a Black Art
Making Best Use of the World's Favourite Runways
The Well-Tempered Warehouse
MIMI Brings OR Tools Together
Pricing Mobile Phone Tariffs
A Practical Perspective on Combinatorial Optimization
XPRESS Accelerates Towards Hard Scheduling Problems
Lecture Notes
What is Mathematical Programming? Practical Linear Programming? 1. Introduction 2. Categories of Modelling Software 3. Practical Example: Oil Blending Model 4. Many-Component Blends 5. Minimising the Cost of the Blend 6. Limits on the Availability of Components 7. Blending by Weight and by Volume 8. Conclusions An Algebraic Approach to Formulation 1. Introduction 2. The Simple Oil Blending Model Revisited 3. Abstracting the Structure of the Problem 4. A Technique for Model Documentation 5. Guidelines for Formulating Models 6. The Transportation Problem 7. A Multi-Mix Blending Problem 8. A Processing Problem 9. Tackling Infeasibility Practical Interpretation of LP Results 1. Introduction 2. Geometrical Description of LP Solution 3. Algebraic Description of LP Solution 4. Sensitivity Analysis 4.1 The Oil Blending Model 4.2 Drawing Inferences from the Solution 4.3 Two-variable Problem 4.4 Reduced Cost 4.5 Alternative Optimal Solutions 4.6 Shadow Prices 5. Ranging Information 5.1 Introduction 5.2 Objective Ranging 5.3 Right Hand Side Ranging 5.4 Ranging the Simple Oil Blending Model Practical Integer Programming 1. Introduction 2. Is IP a Suitable Technique? 3. Discrete Variables 3.1 Integer Variables 3.2 Binary Variables 3.3 Using a Binary Variable 4. Semi-continuous Variables 5. Sets of Decision Variables 6. Special Ordered Sets of Type 2 7. How Integer Programming Codes Work 8. Guiding the Search 8.1 Decisions which the IP Code Takes 8.2 Special Ordered Sets 8.3 Priorities 8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation 9. Advanced Integer Programming 9.1 Travelling Salesman Problem 9.2 Cutting-Stock Problems 9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
What is Mathematical Programming?
Practical Linear Programming?
1. Introduction 2. Categories of Modelling Software 3. Practical Example: Oil Blending Model 4. Many-Component Blends 5. Minimising the Cost of the Blend 6. Limits on the Availability of Components 7. Blending by Weight and by Volume 8. Conclusions
1. Introduction
2. Categories of Modelling Software
3. Practical Example: Oil Blending Model
4. Many-Component Blends
5. Minimising the Cost of the Blend
6. Limits on the Availability of Components
7. Blending by Weight and by Volume
8. Conclusions
An Algebraic Approach to Formulation
1. Introduction 2. The Simple Oil Blending Model Revisited 3. Abstracting the Structure of the Problem 4. A Technique for Model Documentation 5. Guidelines for Formulating Models 6. The Transportation Problem 7. A Multi-Mix Blending Problem 8. A Processing Problem 9. Tackling Infeasibility
2. The Simple Oil Blending Model Revisited
3. Abstracting the Structure of the Problem
4. A Technique for Model Documentation
5. Guidelines for Formulating Models
6. The Transportation Problem
7. A Multi-Mix Blending Problem
8. A Processing Problem
9. Tackling Infeasibility
Practical Interpretation of LP Results
1. Introduction 2. Geometrical Description of LP Solution 3. Algebraic Description of LP Solution 4. Sensitivity Analysis 4.1 The Oil Blending Model 4.2 Drawing Inferences from the Solution 4.3 Two-variable Problem 4.4 Reduced Cost 4.5 Alternative Optimal Solutions 4.6 Shadow Prices 5. Ranging Information 5.1 Introduction 5.2 Objective Ranging 5.3 Right Hand Side Ranging 5.4 Ranging the Simple Oil Blending Model
2. Geometrical Description of LP Solution
3. Algebraic Description of LP Solution
4. Sensitivity Analysis
4.1 The Oil Blending Model 4.2 Drawing Inferences from the Solution 4.3 Two-variable Problem 4.4 Reduced Cost 4.5 Alternative Optimal Solutions 4.6 Shadow Prices
4.1 The Oil Blending Model
4.2 Drawing Inferences from the Solution
4.3 Two-variable Problem
4.4 Reduced Cost
4.5 Alternative Optimal Solutions
4.6 Shadow Prices
5. Ranging Information
5.1 Introduction 5.2 Objective Ranging 5.3 Right Hand Side Ranging 5.4 Ranging the Simple Oil Blending Model
5.1 Introduction
5.2 Objective Ranging
5.3 Right Hand Side Ranging
5.4 Ranging the Simple Oil Blending Model
Practical Integer Programming
1. Introduction 2. Is IP a Suitable Technique? 3. Discrete Variables 3.1 Integer Variables 3.2 Binary Variables 3.3 Using a Binary Variable 4. Semi-continuous Variables 5. Sets of Decision Variables 6. Special Ordered Sets of Type 2 7. How Integer Programming Codes Work 8. Guiding the Search 8.1 Decisions which the IP Code Takes 8.2 Special Ordered Sets 8.3 Priorities 8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation 9. Advanced Integer Programming 9.1 Travelling Salesman Problem 9.2 Cutting-Stock Problems 9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
2. Is IP a Suitable Technique?
3. Discrete Variables
3.1 Integer Variables 3.2 Binary Variables 3.3 Using a Binary Variable
3.2 Binary Variables 3.3 Using a Binary Variable
3.3 Using a Binary Variable
4. Semi-continuous Variables
5. Sets of Decision Variables
6. Special Ordered Sets of Type 2
7. How Integer Programming Codes Work
8. Guiding the Search
8.1 Decisions which the IP Code Takes 8.2 Special Ordered Sets 8.3 Priorities 8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation
8.2 Special Ordered Sets 8.3 Priorities 8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation
8.3 Priorities
8.4 Controlling the Shape of the Search 8.5 Targets and Cutoff 8.6 Assisting the Estimation
8.5 Targets and Cutoff 8.6 Assisting the Estimation
8.6 Assisting the Estimation
9. Advanced Integer Programming
9.1 Travelling Salesman Problem 9.2 Cutting-Stock Problems 9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
9.2 Cutting-Stock Problems 9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
9.3 Aircrew Scheduling 9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
9.4 Intersections of Decisions 9.5 A Machine Scheduling Example
9.5 A Machine Scheduling Example
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