Disassembly sequencing using genetic algorithm

Disassembly sequencing using genetic algorithm

Aug. 29, 2017

Presented by Yooney Cho

International Journal of Advanced Manufacturing Technology (2006) 30: 497-506

Contents

1. Introduction

2. Literature review

3. Overview of genetic algorithm

4. Numerical example

5. Conclusions

Introduction

• Reuse

• Recycle

• Remanufacturing

• Collection

• Disassembly

• Sorting

• Cleaning

• Testing

• Reworking

• Reassembly

 EOL(End Of Life) processing

Literature Review

Author00 Year Solution approach

Taleb and Gupta 1997 Proposed algorithms for scheduling the disassembly of discrete and well defined product structures

Lambert and Gupta 2002 Addressed the problem of demand driven disassembly using a tree network model

Keung et al. 2001 Applied a multi-objective GA approach to a tool selection model.

Overall objective of the model was to minimize the processing time.

Loughlin and Ranjithan 1997

Proposed a GA method, so called neighborhood constraint method and concluded that the GA performed better in multi-objective problems compared to single objective problems.

Lazzerini and Marcelloni 2000

Used GA in scheduling assembly processes. They Employed a

modified partially matched crossover(PMX) and mutation operations to obtain the near optimal sequence. The precedence relationships were not considered in this model.

Bierwirth and Mattfeld 1999

Proposed a method to overcome this problem by introducing the precedence preservative crossover technique for scheduling problems.

 Literature Review

Literature Review

 Disassembly problem (Tree network model, goal programming…)

Quick, cost effective, reasonable solutions

 Genetic Algorithm

Lazzerini and Marcelloni [24] - Not considered precedence relationships Seo et al. [26] - *Penalizing the string to eliminate infeasible strings

 Regular GA is not suitable for considering precedence relationships

Bierwirth et al. [27], Bierwirth and Mattfeld [28]

– introduced *PPX to a single objective job shop scheduling problem

– This method preserves the precedence relationships during the crossover of GA

Disassembly sequencing using Genetic Algorithm (PPX method)

*PPX: Precedence Preservative Crossover

 Literature Review

Overview of genetic algorithm

 Problem description

 Objective function

 Minimizing the make-span

 Decision Variables

 The disassembly sequence of the components

 Assumption

 1) The direction of each operation is independent of the adjacent components.

 2) Neither non-destructive nor destructive disassembly destroys the adjacent components.

 3) Complete disassembly is required.

 each component will be separated from each other.

Overview of genetic algorithm

Step1 : Initial population

 Randomly created, feasible

 The structure of chromosome consists of five components (sequence, direction, method, demand, material)

Step2 : Fitness evaluation

 Make-span

Step3 : Selection

 Half of the best-fit chromosomes are selected for the next generation

Step4 : Offspring generation

 Precedence preservative crossover

 Reciprocal exchange mutation

Step5 : Stop criterion

 When the maximum number of generations are exceeded or no further improvement is obtained

 Solution approach

Overview of genetic algorithm

 Nomenclature

j: Index for component Ch: Index for chromosome gn: Index for generation pop: Index for population

Seq: Index for disassembly sequence(1-9)

n: number of components in the EOL product ncr: Number of chromosomes in the population C: conversion constant

chl: chromosome length rnd: Random number(1-9)

ct_j,seq: penalty for direction change for disassembling component j in sequence seq dt_j,seq: Time required to disassemble component j in sequence seq

T_J,seq: Cumulative disassembly time after component j in sequence seq is disassembled de_j,seq :type of demand for component j in sequence seq

ma_j,seq:Material type of component j in sequence seq

mt_j,seq:Penalty for disassembly method change for disassembling component j in sequence seq

F(ch,gn) : Fitness value for chromosome ch in generation gn MS(ch,gen): Total make-span of chromosome ch in generation gen MT: Total penalty for disassembly method change

CT: Total penalty for direction change

Overview of genetic algorithm

 Components structure

 Precedence relationships

Component 1 or 2 must be disassembled prior to any other components

Component 6 must be disassembled prior to Components 4 and 5

Component 7 must be disassembled prior to components 6 and 3

product

1 2

0 8 7 9

6 3

4 5

1

2 0 8

7 9

6 3

4 5

Overview of genetic algorithm

 Example

 5 sections

 Sequence

 Direction

 Method

 Demand

 Material

 Chromosomes(=string)

Overview of genetic algorithm

Component 1 or 2 >>any other components Component 6 >>Components 4 and 5

Component 7 >>components 6 and 3

처음부터 10개가 완벽히 feasible solution이 나오기는 어렵기 때문 에, Infeasible solutions 들은 제외 시켰을 가능성이 있음.

 Initial population

 Example

 Repetitive Random selection by using Table 1(input data)

 Feasible solutions(all precedence relationships, constraints are satisfied)

 ncr(Number of chromosomes)=10

 Ascending order(fitness)

Overview of genetic algorithm

 Crossover

 Precedence Preservative Crossover(PPX) method

 Example

Randomly filled vector 1: drawn from parent1 2: drawn from parent2 Pass on the precedence relationship.

no new relationships are introduced.

Overview of genetic algorithm

 Crossover

 Precedence Preservative Crossover(PPX) method

 Example (Child

, detail procedure)

2 8 7 1 0 6 3 9 5 4

Chromosome 1 (Parent 1)

Chromosome 2 (Parent 2)

1 2 7 6 3 4 0 8 5 9

Mask for child 1

2 2 2 1 1 2 2 2 1 2 1 2 7 8 0 6 3 4 9 5

Child 1

Overview of genetic algorithm

 Mutation

 Mutate with a given probability

 Precedence relationships are preserved

Not selected for mutation Selected for mutation

Preserve solutions in the current population

Exchange component 1 and 2 Randomly selected chromosomes

Overview of genetic algorithm

 Fitness evaluation

☞ Fitness function (Minimize)

(=Total time of disassembly) (=Make Span)

Basic disassembly time (DT)

Penalty time Tool change time (MT) Direction change time (CT)

• Do not penalize last disassembly sequence component

• Do not penalize “Recycling pair”

*Recycling pair: two adjacent components made of same material and if they are both demanded for recycling.

☞ Fitness value = C – MS, C>MS

(Maximize)

Overview of genetic algorithm

 Fitness evaluation (detail)

Ex: +x to +x is 0

Ex: N to N is 0, N to D is 1

Sequence position 0~8

n: product내의 component 수=0~9=10개

Sequence position 9

J: component

Overview of genetic algorithm

 Fitness evaluation (detail)

Component j의 sequence 에서의 demand type

= 0:no demand, 1: reuse, 2: recycle

Component j의 sequence 에서의 Material type

= A:Al, S: steel, P: plastic

T

=(T

)+dt 가 옳다고 생각함.

*T : Cumulative disassembly time after component j in sequence is disassembled

Sequence-1 Sequence Sequence+1

Al steel steel

Recycle Recycle

Reuse

T

T

Overview of genetic algorithm

 Fitness value calculation

 Example

MS=10s+2s+25s=37s, Let C=100.

Overview of genetic algorithm

 Termination

 When the maximum number of generations are exceeded

 Ex: 100 generations

 No further improvement is obtained

 Ex: If average fitness value of new generation is lower or equal to

previous generation

Overview of genetic algorithm

 Genetic algorithm model steps

Initial population

Fitness calculation

Ascending order(fit)

Select first half of the population Ex: 10개

Ex: 5개

Crossover probability

PPX operation

Generate children

Ex: 5개 Clone the

selection Ex: 5개

Ex: 10개

Mutation probability Not selected for

the mutation

New generation Ex: 10개

Ex: 10개

Ex: 6개

Ex: 10개

Selected for the mutation Clone the

chromosomes

Operate Mutation

Ex: 4개

Ex: 6개 Ex: 4개

Ex: 10개

Numerical example

 Input data

Numerical example

After 6 generation

 Result

Numerical example

 Comparison

 Genetic Algorithm(GA) method

 Fitness value=71

 Conversion constant (c)=100

 Total make-span=100-71=29 s

 Code: ANSI-C

 Exhaustive search (ES) method (= Brute-force search)

 Fitness value=71

 Conversion constant (c)=100

 Total make-span=100-71=29 s

 Code: ANSI-C

 Obtained total 12 sequences

*ES method : Also known as Brute force search, an approach which explore the entire search space, testing every possible candidate solution

Conclusions

 Discussion

 My opinion

 Need more description of sequence dependency on demand and material.

 Need more description of selection method and feasibility of each step.

 Need to mention the computing time in conclusion.

 Future work

 Study an application of Genetic Algorithm for (flexible) job-shop scheduling problem

.