My very own experience in solving optimization problems Alessandro - - PowerPoint PPT Presentation

▶
my very own experience in solving optimization problems
SMART_READER_LITE
LIVE PREVIEW

My very own experience in solving optimization problems Alessandro - - PowerPoint PPT Presentation

Aug 16, 2022 412 likes •845 views

My very own experience in solving optimization problems Alessandro Zanarini - EPFL - 26 March 2019 Automated tools vs Optimization Shift from manual to automated tool is seen as the holy grail Underlying problem can be tough

slide-1
SLIDE 1

My very own experience in solving

  • ptimization problems

Alessandro Zanarini - EPFL - 26 March 2019

slide-2
SLIDE 2

Alessandro Zanarini - 26th March 2019

Automated tools vs Optimization

  • Shift from “manual” to “automated tool” is seen as the holy grail

○ Underlying problem can be tough

  • Optimization seen as cherry on the cake… but the cake is needed first
  • Optimization expert needs to educate the customer about “optimization

potential/capabilities” for managing expectations

  • Very often customers do not know what they want to optimize

○ Possibly conflicting objectives

  • Optimization can unleash considerable potential savings
  • Optimization may threaten jobs. No-optimization may threaten entire companies
slide-3
SLIDE 3

Alessandro Zanarini - 26th March 2019

30%

Optimization development phases

  • 1. Discovery

– Understanding the revenue and costs drivers, size of the problem – Define the problem, its constraints, its objective function(s)

  • 2. Designing and implementing an optimization model/algorithm

– All models are wrong but some are useful (cit. George Box) – Understand necessary assumptions/approximations

  • 3. Integrating with existing IT system / workflow

– Fetching and preparing input to optimization model/algorithm – Feeding back the (sub) optimal solution

  • 4. Testing – Verifying constraint satisfaction, hypothesis, etc…

Business case/model needs to be defined!!! 10% 30% 30%

slide-4
SLIDE 4

Alessandro Zanarini - 26th March 2019

Optimization technologies

An incomplete list for discrete optimization

Mathematical Programming Metaheuristics Constraint Programming Genetic Algorithms Dynamic Programming Greedy / Heuristics Graph Algorithms

slide-5
SLIDE 5

E-bus deployment

  • ptimization
slide-6
SLIDE 6

Alessandro Zanarini - 26th March 2019

Electrical buses - the TOSA case

slide-7
SLIDE 7

Alessandro Zanarini - 26th March 2019

E-bus technologies

D T S S S T S S S S S T Depot charging D T S S S T S S S S S T Terminal charging D T S S S T S S S S S T En-route charging

slide-8
SLIDE 8

Alessandro Zanarini - 26th March 2019

myTOSA

Traffic Simulation Sensitivity / What-if Analysis Bus Simulation Optimization Battery Ageing

speed profile

  • route profile
  • passenger load
  • bus description
  • feeding stations
  • battery models
  • bus route frequency

Energy consumption battery selected deployment solution

  • battery ageing features
  • average bus consumption
slide-9
SLIDE 9

Optimal deployment of control solutions

slide-10
SLIDE 10

Alessandro Zanarini - 26th March 2019

Multirate control systems

System

Controller 1 Feedback y u1 e1 Controller 2 e2 Controller 3 e3 u2 u3 Feedback Feedback r

slide-11
SLIDE 11

Alessandro Zanarini - 26th March 2019

Context

Software Hardware

  • SoC (2 cores + FPGA)
slide-12
SLIDE 12

Alessandro Zanarini - 26th March 2019

Problem Definition

slide-13
SLIDE 13

Alessandro Zanarini - 26th March 2019

Experimental evaluation - CP

slide-14
SLIDE 14

Underground mining fleet

  • ptimization
slide-15
SLIDE 15

Alessandro Zanarini - 26th March 2019

Underground Mine

slide-16
SLIDE 16

Alessandro Zanarini - 26th March 2019

Undeground mining operations

Drilling Charging Concrete Scaling Blasting Ventilation Hauling Bolting

slide-17
SLIDE 17

Alessandro Zanarini - 26th March 2019

Automated Cyclic Scheduling

slide-18
SLIDE 18

Stator Winding Design Optimization

slide-19
SLIDE 19

Alessandro Zanarini - 26th March 2019

Gearless Mill Drives

slide-20
SLIDE 20

Alessandro Zanarini - 26th March 2019

Stator

slide-21
SLIDE 21

Alessandro Zanarini - 26th March 2019

Main Intuition

slide-22
SLIDE 22

Alessandro Zanarini - 26th March 2019

Different approaches

slide-23
SLIDE 23

Optimal Stock Sizing in a Cutting Stock Problem with Stochastic Demands

Case Study 1

slide-24
SLIDE 24

Alessandro Zanarini - 26th March 2019

Production of plastic pieces

slide-25
SLIDE 25

Alessandro Zanarini - 26th March 2019

Initial Input

  • A mold creates a piece with 16 discs
  • Orders in year 2018
slide-26
SLIDE 26

Alessandro Zanarini - 26th March 2019

Discovery Phase

  • What are the cost drivers?

○ Total time of production, waste, total plastic used, overproduction, cutting costs

  • Is there a possibility to build a new mold?
  • Will different molds have the same yield?
  • Will different molds have the same throughput?
  • Are the production requirements constant or they may vary on subsequent years (i.e.

stochastic)?

  • Is the yield of the cutting procedure constant?
  • Size of the problem?
slide-27
SLIDE 27

Alessandro Zanarini - 26th March 2019

Actual Problem

Decision variables

  • Which investment to build a set of molds to use subject to stochastic production

requirements

  • Which cutting patterns to use subject to given production requirements

Objective function

  • Minimize: Waste, Over-production, Number of cuts
slide-28
SLIDE 28

Alessandro Zanarini - 26th March 2019

Models for operational optimization

Item-based formulation (Kantorovich) Pattern-based formulation (Gilmore & Gomory)

Item 2 Item 1 Item 3 Stock 1 Item 4 Stock 2

Pattern 1: Pattern 2: Pattern 3:

Stock size

x 2 x 0

slide-29
SLIDE 29

Alessandro Zanarini - 26th March 2019

High level model for (stochastic) planning

Optimization of the average case Optimization under uncertainty

Choice of Stock size Optimal cut operations @ average case scenario Choice of Stock size Optimal cut operations @ Scenario 1 Optimal cut operations @ Scenario 2 Optimal cut operations @ Scenario n

slide-30
SLIDE 30

Container Terminal Optimization

Case Study 2

slide-31
SLIDE 31

Alessandro Zanarini - 26th March 2019

Container Terminal

slide-32
SLIDE 32

Alessandro Zanarini - 26th March 2019

Container Trade Growth

slide-33
SLIDE 33

Alessandro Zanarini - 26th March 2019

End-loaded terminal operations

import export transhipment

slide-34
SLIDE 34

Alessandro Zanarini - 26th March 2019

Discovery Phase

slide-35
SLIDE 35

Alessandro Zanarini - 26th March 2019

Berth Crane and Allocation

Quay Time Vessel 1 Vessel 2 Vessel 3 Vessel 4

slide-36
SLIDE 36

Alessandro Zanarini - 26th March 2019

Quay Crane Allocation and Scheduling

slide-37
SLIDE 37

Alessandro Zanarini - 26th March 2019

Stowing sequence and allocation

slide-38
SLIDE 38

Alessandro Zanarini - 26th March 2019

Yard Management / Planning

slide-39
SLIDE 39

Alessandro Zanarini - 26th March 2019

Automated Stacking Cranes

slide-40
SLIDE 40

Alessandro Zanarini - 26th March 2019

Horizontal Transportation

slide-41
SLIDE 41

Conclusions

slide-42
SLIDE 42

Alessandro Zanarini - 26th March 2019

Conclusions

  • Real challenge is understanding domain-specific knowledge and translate it into abstractions

and mathematical formulations

  • Getting access to data is key

○ Baseline for comparing optimized solution vs current solution ○ Understanding problem features and size

  • Educate the customer about

○ Optimization potentials (setting expectations right) ○ Trade-off between performance vs quality

  • Fail fast

○ Short feedback cycle with customer ○ Post-processing tool for verifying solution (better if customer developed)

  • Technology mastery is required to understand strengths and weaknesses of each technology

and figure out which technology is suited for which problem