Axxom case study - questions Angelika Mader University of Twente.

Slides:



Advertisements
Similar presentations
Algorithm Design Methods (I) Fall 2003 CSE, POSTECH.
Advertisements

Algorithm Design Methods Spring 2007 CSE, POSTECH.
SCHEDULING IN THE PHARMACEUTICAL INDUSTRY IEOR 4405 – Production Scheduling Kristinn Magnusson Sigrun Gunnhildardottir.
Axxom: What happened so far. Basic case study  lacquer production scheduling  3 recipes for lacquers,  specifying processing steps,  resources used.
Lecture 6: Job Shop Scheduling Introduction
School of Computer Science
GRAPH BALANCING. Scheduling on Unrelated Machines J1 J2 J3 J4 J5 M1 M2 M3.
The Greedy Method1. 2 Outline and Reading The Greedy Method Technique (§5.1) Fractional Knapsack Problem (§5.1.1) Task Scheduling (§5.1.2) Minimum Spanning.
Genetic Algorithms Contents 1. Basic Concepts 2. Algorithm
1 9. S EQUENCING C ONSTRUCTION T ASKS Objective: To understand the problem of sequencing tasks in a manufacturing system, and the methods of finding optimal.
Progress in Linear Programming Based Branch-and-Bound Algorithms
Spring 2008, King Saud University Bar Chart Dr. Khalid Al-Gahtani 1 What is Activity An activity or task is a given kind and amount of work which: –Consumes.
In this handout Stochastic Dynamic Programming
1 Dynamic Scan Scheduling Specification Bruno Dutertre System Design Laboratory SRI International
THE SINGLE MACHINE EARLY/TARDY PROBLEM* PENG SI OW & THOMAS E. MORTON IE Paper Presentation A. İrfan Mahmutoğulları *Ow, P. S., & Morton, T. E. (1989).
© J. Christopher Beck Lecture 14: Assembly Line Scheduling 2.
Linear Programming Special Cases Alternate Optimal Solutions No Feasible Solution Unbounded Solutions.
1 An Asymptotically Optimal Algorithm for the Max k-Armed Bandit Problem Matthew Streeter & Stephen Smith Carnegie Mellon University NESCAI, April
Support Vector Machines Formulation  Solve the quadratic program for some : min s. t.,, denotes where or membership.  Different error functions and measures.
An Introduction to Black-Box Complexity
An indirect genetic algorithm for a nurse scheduling problem Ya-Tzu, Chiang.
Ant Colony Optimization (ACO): Applications to Scheduling
Metaheuristics The idea: search the solution space directly. No math models, only a set of algorithmic steps, iterative method. Find a feasible solution.
Elements of the Heuristic Approach
Optimal Scheduling for ICU Patients SIDDHANT BHATT STEVE BOYLE ERICA CUNNINGHAM.
Linear programming Lecture (4) and lecture (5). Recall An optimization problem is a decision problem in which we are choosing among several decisions.
Operational Research & ManagementOperations Scheduling Flow Shop Scheduling 1.Flexible Flow Shop 2.Flexible Assembly Systems (unpaced) 3.Paced Assembly.
Embedded System Design Framework for Minimizing Code Size and Guaranteeing Real-Time Requirements Insik Shin, Insup Lee, & Sang Lyul Min CIS, Penn, USACSE,
MILP Approach to the Axxom Case Study Sebastian Panek.
© J. Christopher Beck Lecture 5: Project Planning 2.
Chapter 7 Handling Constraints
Manijeh Keshtgary. Queuing Network: model in which jobs departing from one queue arrive at another queue (or possibly the same queue)  Open and Closed.
1 Simulated Annealing Contents 1. Basic Concepts 2. Algorithm 3. Practical considerations.
AMETIST Review Meeting June 2003 Deliverable Axxom Case Study: Scheduling of Lacquer Production Contributors: Axxom Dortmund Twente Verimag.
UML’03 Stochastic Evaluation of the 1st Axxom Case Study Holger Hermanns Yaroslav S. Usenko (Saarbrücken & Twente) (Twente) with contributions of Henrik.
Resource Constrained Project Scheduling Problem. Overview Resource Constrained Project Scheduling problem Job Shop scheduling problem Ant Colony Optimization.
1 Short Term Scheduling. 2  Planning horizon is short  Multiple unique jobs (tasks) with varying processing times and due dates  Multiple unique jobs.
For Wednesday No reading No homework There will be homework for Friday, as well the program being due – plan ahead.
Search exploring the consequences of possible actions.
CHECKERS: TD(Λ) LEARNING APPLIED FOR DETERMINISTIC GAME Presented By: Presented To: Amna Khan Mis Saleha Raza.
Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks I-Hong Hou.
Search exploring the consequences of possible actions.
Understanding User’s Query Intent with Wikipedia G 여 승 후.
Extending the smart-card personalisation system by the graphical treatment Angelika Mader University of Twente.
EECS 690 Critiques of Utilitarianism. A common objection dismissed: Objection: If there are 101 people, Util. says that 51 of them can do whatever they.
A METIST Review Meeting, Brussels, 13 November 2005 The Axxom Case Study state of the art Ed Brinksma joint work with Gerd Behrmann Martijn Hendriks Angelika.
Scheduling Lacquer Productions with Uppaal AXXOM case study of the Ametist project Angelika Mader Distributed and Embedded Systems Group, University of.
Cybernetix – Cost Driven UPPAAL and Insights Angelika Mader University of Twente Ametist meeting December 2002 Dortmund.
Decision Theoretic Planning. Decisions Under Uncertainty  Some areas of AI (e.g., planning) focus on decision making in domains where the environment.
A local search algorithm with repair procedure for the Roadef 2010 challenge Lauri Ahlroth, André Schumacher, Henri Tokola
D Goforth - COSC 4117, fall OK administrivia  Exam format – take home, open book  Suicide rule for King’s court Illegal moves cannot move last.
Search Control.. Planning is really really hard –Theoretically, practically But people seem ok at it What to do…. –Abstraction –Find “easy” classes of.
DECISION MODELING WITH MICROSOFT EXCEL Chapter 12 Copyright 2001 Prentice Hall Publishers and Ardith E. Baker Multi-Objective Decision Making and Heuristics.
Linear programming Lecture (4) and lecture (5). Recall An optimization problem is a decision problem in which we are choosing among several decisions.
1 Web Search/Thinkin g It may not seem this way, but there are robots being used all around us all the time! What are some examples of everyday.
The GNMS segment operates a global payments network that processes and settles proprietary and non-proprietary card transactions. GNMS acquires merchants.
Finite state machine optimization
Finite state machine optimization
Heuristic Optimization Methods
Breakout Session 3 Alex, Mirco, Vojtech, Juraj, Christoph
Digital Optimization Martynas Vaidelys.
Load Balancing: List Scheduling
Erwin Hans (T&M-OMST) BB-235, tel. 3523, Johann Hurink (TW-STOR)
Introduction to Scheduling Chapter 1
Planning and Scheduling in Manufacturing and Services
Approaches to search Simple search Heuristic search Genetic search
Algorithm Design Methods
Presented By: Darlene Banta
Flexible Assembly Systems
Load Balancing: List Scheduling
Presentation transcript:

Axxom case study - questions Angelika Mader University of Twente

Question 1: TIMING lacquer production follows 3 different recipes. timing behaviour is specified for each recipe. timing dependencies with minimal and maximal offset times end-start start-start end-end production times for processes maximal break times

Question 1: TIMING recent information: maximal offset times need not to be taken into account consequence: problem is much more a job shop scheduling problem than specified what is the right timing behaviour?

No question (for Kim) For the UPPAAL model with arbitrary delay and a few easy heuristics and random depth first search we get schedules in no time

Question 2: COSTS costs as penalty for delay challenge: minimize delay costs there are feasible schedules possible => no delay, no cost-optimisation problem delay costs are so high that they seem much more to be a value tuning an optimizer => what do delay costs mean to us?

Question 3: AVAILABILITY problem: machines break down for a certain amount of time (say 50%) solution: extend the production time of a product requiring this machine (say factor 2) question: is this the right way to deal with this kind of probabilities?