improving harvesting operations · 2020. 2. 20. · other crops harvest transport supply chain e-t...

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An analytics solution for harvesting operations in

an oil palm plantation

Jorge Leal, M.Sc.

Mariana Escallón , M.Sc.

Daniel Castillo-Gómez, M.Sc.

Andrés Medaglia, Ph.D.

Center for the Optimization and Applied Probability (COPA)

Industrial Engineering Department

Carlos Montenegro, Ph.D.

Center for Orinoquia Studies (CEO)

Universidad de los Andes (Colombia)

1

II International Conference on

Agro BigData and Decision

Support Systems in Agriculture12-14 July 2018. Lleida (Spain)

http://copa.uniandes.edu.co/ 2

Agenda

Introduction

Project stages

Results


Agenda

Introduction

Motivation

Context

Problem definition

Literature review


Motivation

*Fedepalma (2018).

Introduction Project stages Results

Palm oil

Economic:

By 2015, valued at USD 66 billion (global)

Major job provider in Colombia (140,000)*


Motivation

54%31%

3%

2% 2%

8%

Palm oil market

Indonesia

Malaysia

Thailand

Colombia

Nigeria

Others (23 countries)


Fedepalma (2015); Euromonitor International (2018).

Palm oil

Economic:




Motivation

Higher productivity in:

• Malaysia – 26%

• Indonesia – 23%


Fedepalma (2015); Euromonitor International (2018).

Palm oil

Economic:



54%31%

3%

2% 2%

8%

Palm oil market

Indonesia

Malaysia

Thailand

Colombia

Nigeria

Others (23 countries)


Motivation

Main uses:

Alimentary: cooking oil, margarine and ice cream

Non-alimentary: biodiesel, soap and cosmetics


Palm oil


Motivation

Main uses:

Alimentary: cooking oil, margarine and ice cream

Non-alimentary: biodiesel, soap and cosmetics

Environmental issues:

Deforestation in Southeast Asia

Plantations in flooded savannas as in Colombian

Orinoquia have less impact in the ecosystem


Palm oil


Agenda

Introduction

Motivation

Context

Problem definition

Literature review


Context



Context - La Ilusión plantation

• 2,090 hectares




• 2,090 hectares

• 87 land plots

• Sowing years 2009, 2010 y 2011

• 200 workers (direct and outsourced)

• 100 km of cableway

• 8 aerial tractors


2009

2010

2011




Planting, fertilization

and maintenance

Harvest Transport Oil extraction





and maintenance


http://copa.uniandes.edu.co/


and maintenance


16



8 20 days

Buffalo

Mechanized

Harvest crew



and maintenance


17



• 30 containers per trip

• 150 kg per container

• 3 - 4.5 tons per trip

8 aerial tractors



and maintenance


18



Cableway network

• Main cable

• Secondary cables

• Tertiary cables

• Stockpiling center


Business problem


Average harvest cycle length: 19.6 days


Business problem

How to visit the plantation to

reduce the harvest cycle length?


HarvestTransport

8 20 days


Business problem


HarvestTransport

8 20 days

Reduce costs

Optimize resource allocation

Reduce uncertainty in task scheduling

How to visit the plantation to

reduce the harvest cycle length?


Agenda

Introduction

Motivation

Context

Problem definition

Literature review


Literature review

23

Oil palm

Harvest Transport

Gen

eral

ana

lysi

sLi

near

optim

izat

ion

• Durán, Sierra & García (2004).

Extraction potential.

• Mosquera, Fontanilla & Donado

(2008). Harvest studies for oil

palm.

• Mosquera et al. (2008).

Harvest comparison, invidual or

group.

• Solah, Hitam & Borhan (2004).

Cableway system for oil palm

FFB evacuation.

• Fontanilla & Castiblanco (2009).

Cableway at the harvesting.

• Fontanilla et al. (2010).

Comparison between FFB

evacuation systems.

Adarme, Fontanilla & Arango (2011).

General logistic model and single

sourcing.

García-Cáceres (2007). Tactical

and operative optimization of

the supply chain in the oil palm

industry.

Supply chain

Fontanilla (2012). Stockpilling center location model for an oil palm

plantation.

Oth

er Ademosun (1982). Location-allocation models for oil palm

production (feasible set approach).

Cableway


Euler, Hoffmann, Fathoni, &

Schwarze (2016). Exploring yield

gaps in oil palm production

systems.

• Corley & Tinker (2016).

The Oil Palm.

• Murphy (2014). Oil palm

crop challenges.


• Plà-Aragonés

(2015). Handbook

of Operations

Research in

Agriculture and the

Agri-Food Industry

Literature review

24

Other crops

Harvest Transport Supply chain

Dis

cret

e-ev

ent

sim

ulat

ion

• Yates (2014). Harvest, transport and processing of

vegetables.

• van der Vorst et al. (2000). Modelling and simulating

multi-echelon food systems.

• van der Vorst et al. (2009). Simulation modeling for

food supply chain redesign.

Heu

ristic

s

Edwards et al. (2015). Tabu search task allocation in agriculture.

• Ali et al. (2009). Infield logistics planning for crop-

harvesting operations.

• Caixeta-Filho (2006). Orange

harvesting scheduling management:

A case study. MIP optimal harvest

for juice extraction and acidity level.

Line

ar

optim

izat

ion

• Amorim et al. (2012). Multi-objective integrated production

and distribution planning of perishable products.

• Jiao, Higgins, & Prestwidge (2005). An integrated

statistical and optimization approach to increasing sugar

production within a mill region.


• Borodin et al. (2014). A

quality risk management

problem: Case of annual

crop harvest scheduling.


Agenda

Introduction

Project stages

Results


Project stages


Harvest cycle

model Q

Transport allocation

model D

Discrete-event

simulation W

Resource allocation

model W

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily

Parameter estimation


Project stages



model D

Resource allocation

model W

Q: Quarterly

W: Weekly

D: Daily

Harvest cycle

model Q


Project stages



model D

Resource allocation

model W

Q: Quarterly

W: Weekly

D: Daily

Harvest cycle

model Q

What day and how

much fruit should be

collected for each

land plot?

Strategical


Project stages



model D

Resource allocation

model W

Q: Quarterly

W: Weekly

D: Daily

How to allocate

harvest crews

throughout the

plantation?

Tactical

Harvest cycle

model Q

What day and how


collected for each

land plot?

Strategical


Project stages


Harvest cycle

model Q


model D

Resource allocation

model W

Q: Quarterly

W: Weekly

D: Daily

What are the aerial

tractor routes for

fruit evacuation?

Operational

How to allocate

harvest crews

throughout the

plantation?

Tactical

What day and how


collected for each

land plot?

Strategical


Project stages


Feasible?

Add constraints

Amount (kg) of fresh fruit

to be harvested each day

Amount (kg) of fresh

fruit to pick in each

land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order


Project stages






land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast


Feasible?

Add constraints

Q: Quarterly

W: Weekly

D: Daily

Aerial

tractor

order


1. We assign a penalization for not visiting the palm on time

FFB state

Ripeness cost function


Unripe Ripe Overripe Rotten

< 8 days

No fruitlets

detachment

8-12 days

< 50% fruitlets

detachment

12-17 days

>50% fruitlets

detachment

17-20 days

Dehydrated

bunch


Ripeness cost function


Oil yield loss

Higher

harvesting

time


Project stages






land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast


Feasible?

Add constraints

Q: Quarterly

W: Weekly

D: Daily

Aerial

tractor

order


Yield forecast


1. Time series fitting and forecast

Methodology

Forecast for average yield zone 9 for short harvest cycle lengths

0

200

400

600

800

1000

1200

Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

2015 2016 2017 2018

kg /

ha

Quarter

Real

Forecast


0

200

400

600

800

1000

1200

Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

2015 2016 2017 2018

kg /

ha

Quarter

Real

Forecast

37

Yield forecast


2. Variability for each land plot

Methodology


ln 𝑦9,𝑄2 , 𝜎2010


Yield forecast


2. Variability for each land plot

Methodology

0

200

400

600

800

1000

1200

Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

2015 2016 2017 2018

kg /

ha

Quarter

Real

Forecast


𝑦52′

Simulation

land plot 52


Yield forecast


1. Quarterly average per zone (Holt-Winters) captures seasonality and long-term trend

2. Monte Carlo simulation (particular value per land plot)

3. Increase in production due to accumulation (harvest cycle length)

• Polynomial regression

Yield increase - land plot 52

𝑓(𝑥) = 𝑦52′ 1 + 𝛽2𝑥

2 + 𝛽3𝑥3

750

900

1050

1200

1350

1500

0 2 4 6 8 10 12

Yie

ld (

kg /

ha)

Days from optimal cycle length


Project stages


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast


Q: Quarterly

W: Weekly

D: Daily

Aerial

tractor

order


Harvest cycle model

When and how much fruit to collect from each land plot?

Planning horizon: 3 months

Objective: Reduce harvest cycle length

Subject to:

• Transport capacity

• Daily labor time

• Daily production capacity


Q


1 2 3 4 5 6 7 8 9 10

42

Harvest cycle model

Aim

X X XLand plot 1

Days


Q


1 2 3 4 5 6 7 8 9 10

43

Harvest cycle model

Aim

Land plot 1

Days


Q

X X X

X X X

X X X

X X X


1 2 3 4 5 6 7 8 9 10

44

Harvest cycle model

Aim

Land plot 1

Days


Q

X X X

X X X

X X X

X X X


1 2 3 4 5 6 7 8 9 10

45

Harvest cycle model

Aim

X X XLand plot 1

Days


Q

X X X

X X XLand plot 2

Land plot 3 X X X


Harvest cycle model

Column generation


Q

Auxiliary

problem

Visit pattern


Harvest cycle model

Column generation


Q

Master

problem

𝑥𝑝𝐻 ≥ 0

Auxiliary

problem

End

𝕨T

𝕒𝑠𝑟𝑠 > 0

Master

problem

𝑥𝑝𝐻 ∈ {0,1}

Sí No


Objective function

max

𝑖∈𝒯

𝑗∈𝒯

𝓁∈ℒ

𝑦𝑖𝑗𝓁𝐻 𝑔 ∙ 𝛿𝑖𝑗𝓁 − 𝜍𝑖𝑗𝓁 − 𝕨

T𝕒𝑠

48

Harvest cycle model

Mathematical model


QAuxiliary problem

𝑟𝑠


Objective function

max

𝑖∈𝒯

𝑗∈𝒯

𝓁∈ℒ


T𝕒𝑠

49

Harvest cycle model

Mathematical model


QAuxiliary problem

Pattern usefulness


Harvest cycle model

Mathematical model


QAuxiliary problem

Objective function

max

𝑖∈𝒯

𝑗∈𝒯

𝓁∈ℒ


T𝕒𝑠

Problem linking


Harvest cycle model

Mathematical model


QAuxiliary problem

Objective function

max

𝑖∈𝒯

𝑗∈𝒯

𝓁∈ℒ


T𝕒𝑠

𝕒𝑠 =

𝑗∈𝒯

𝑦𝑠𝑗𝓁𝐻 ∀𝓁 ∈ ℒ ,

𝑗∈𝒯

𝓁∈ℒ

𝑦𝑖𝑗𝓁𝐻 𝛿𝑖𝑗𝓁

𝑘𝑡𝓁 ∀𝑖 ∈ 𝒯,

𝑗∈𝒯

𝓁∈ℒ

𝑦𝑖𝑗𝓁𝐻 𝛿𝑖𝑗𝓁 ∀𝑖 ∈ 𝒯,

𝕨T: dual variables from master problem constraints

Pattern type

Utilization of aerial tractors

Amount of fruit collected

Problem linking


Harvest cycle model

Mathematical model


QAuxiliary problem

⋮

𝑠 𝑒Lan

d p

lots

1

2

ℒ

⋮

Days


Harvest cycle model

Mathematical model


QAuxiliary problem

⋮

𝑠 𝑒Lan

d p

lots

1

2

ℒ

⋮

Days

1,0 1,3 1,7 1,9


Harvest cycle model

Mathematical model


QAuxiliary problem

⋮

𝑠 𝑒Lan

d p

lots

1

2

ℒ

⋮

Days

1,0 1,3 1,7 1,9

𝛿0,3,1

𝛿3,7,1 𝛿7,9,1


Harvest cycle model

Column generation


Q

Master

problem

Pattern selection

for each land plot


Project stages


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order


Resource allocation model


W How to arrange the land plots programmed in a timetable?

Planning horizon: 1 week

Objective: Minimize yield loss (earliness)

Subject to:

• Available personnel

• Harvesting time for each land plot

• Deadline per land plot




WAim Harvest crew 1

Time interval Day

Start End M T W Th F S Su

06:00 07:00

07:00 08:00

08:00 09:00

09:00 10:00

10:00 11:00

11:00 12:00 Land plot 1

13:00 14:00 Land plot 2

14:00 15:00 Land plot 3

15:00 16:00 Land plot 4

Land plot 5

Land plot 6

Land plot 7

Land plot 8

Land plot 9

Rest/travel time




WAim Harvest crew 1

Time interval Day

Start End M T W Th F S Su

06:00 07:00

07:00 08:00

08:00 09:00

09:00 10:00

10:00 11:00

11:00 12:00 Land plot 1

13:00 14:00 Land plot 2

14:00 15:00 Land plot 3

15:00 16:00 Land plot 4

Land plot 5

Harvest crew 2 Land plot 6

Time interval Day Land plot 7

Start End M T W Th F S Su Land plot 8

06:00 07:00 Land plot 9

07:00 08:00 Rest/travel time

08:00 09:00

09:00 10:00

10:00 11:00

11:00 12:00

13:00 14:00

14:00 15:00

15:00 16:00




WScheduling

Machine-oriented

Harvest crews Land plots to harvest

time

0 5 10 15 20 25

M1 L2 L3

M2 L1 L5

M3 L28 L29

M4 L14 L18

M5 L21 L20


Optimization

61



𝑒∗𝑠∗

1

2

ℳ

1 2 3 ℒ

⋮

⋯

1,1 1,2 1, |ℒ|1,3

2,1 2, |ℒ|

|ℳ|, 1 |ℳ|, 2 |ℳ|, |ℒ||ℳ|, 3

Har

vest

cre

ws

Land plots

⋮ ⋮ ⋮ ⋮

⋯

⋯

⋯

2,3

Arcs 𝐴1 Arcs 𝐴2 Arcs 𝐴3

Network structureSequence

W


Optimization

62



𝑒∗𝑠∗

1

2

ℳ

1 2 3 ℒ

⋮

⋯

1,1 1,2 1, |ℒ|1,3

2,1 2, |ℒ|

|ℳ|, 1 |ℳ|, 2 |ℳ|, |ℒ||ℳ|, 3

Har

vest

cre

ws

Land plots

⋮ ⋮ ⋮ ⋮

⋯

⋯

⋯

2,3

Arcs 𝐴1 Arcs 𝐴2 Arcs 𝐴3

Network structureSequence (example)

W




WMathematical model

time

0 5 10 15 20 25

M1 L2 L3

𝑡2𝑠 𝑡2

𝑐𝑝12 𝑡3𝑠 𝑡3

𝑐𝑝13




WMathematical model

time

0 5 10 15 20 25

M1 L2 L3

𝑡2𝑐 𝑡3

𝑠𝜏23




WMathematical model

time

0 5 10 15 20 25

M1 L2

𝑡2𝑐 𝑑2𝑡2

𝑒


Project stages


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order


Transport allocation model


D

Which should be the daily routes for the aerial tractors?

Planning horizon: one day

Objective: Collect all the estimated production

Subject to:

• Maximum pulling capacity

• Daily operational time




D

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor


Land

plot

Demand

(kg)

Number of

trips

Unattended

demand (kg)

L1 12,300 2.7 3,300

L8 8,500 1.9 4,000

L2 10,200 2.3 1,200

69



D

4,500 Kg

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor


Land

plot

Demand

(kg)

Number of

trips

Unattended

demand (kg)

L1 12,300 2.7 3,300

L8 8,500 1.9 4,000

L2 10,200 2.3 1,200

70



D

4,500 Kg

L8 L1

L2

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor




D

L1

L2

L1L8

Route A Route CRoute B

3 times 2 times

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor




D




D

Route A

SPC

AA AB 14A 11A 8A

L8

8A 11A 14A AB AA

SPC

L8

Route A

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor




D

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor

Route-A

Route-B

Route-C

On hold




D

Route-A

Route-B

Route-C

Local search with

swap moves

Discrete-event

simulation

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor

On hold




D

Route-A

Route-B

Route-C

Discrete-event

simulation

Calculate number

of trips per land

plot

Create route for

unattended

demand

Assign routes to

every aerial

tractor

On hold

New sequence Time calculation

Local search with

swap moves


Project stages


Feasible?

Add constraints



Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order



land plot each day


Discrete-event simulation



Introduction Project stages

80


General

modeling

Results

Node types

Intersection

Land plot

X

X X

X

X

FFB: Fresh fruit bunches


Node types

Intersection

Land plot


81


General

modeling

Results

X

X

X

X

XX




82


General

modeling

Results

Node types

Intersection

Land plot

Aerial tractor

Transits through the network




83


General

modeling

Results

Node types

Intersection

Land plot

Aerial tractor


Stockpiling center




84


General

modeling

Results

Node types

Intersection

Land plot

Aerial tractor


Stockpiling center

Entities

Represent containers of FFB

Transported by aerial tractors




85


General

modeling

Aerial tractors

Loading time

Unloading time (workers in

SPC)

Time between failures

Repair Time

Uncertainty

Scenarios

Number of aerial tractors

Results



Project stages


Feasible?

Add constraints



Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order



land plot each day



87

Modeling

logic


Results

1 2 3 4 5 6 7

Land

plo

ts

X

X

… X …

X

X X

Days

Inputs

Harvest cycle

model



88

Modeling

logic


Results

1 2 3 4 5 6 7

Land

plo

ts

X

X

… X …

X

X X

Days

InputsLand

plot

Day 7

L1 15 ton

L2 -

… -

… 2.5 ton

LX -

.. -

… 20 ton

L86 -

L87 10 ton

Harvest cycle

model



89

Modeling

logic


Results

Containers (daily)


Containers in land plots

Number of trips

Route to land plot


90

Modeling

logic


Results



Number of trips

Route to land plot

Shortest path between

SPC and land plot

How to avoid collisions?


91

Modeling

logic


Results



92

Modeling

logic


Results


Number of trips

Route to land plot


SPC and land plot




93

Modeling

logic


Results


Number of trips

Route to land plot


SPC and land plot



Branch system

Branch: subset of nodes and paths


94


Results



95


Results

X

XX

X

X

XX

X

X27

3736

32

35

34

33

Branch system




96


Results

X

X

X

X44

43

38

Branch system




97


Results

X

X

X

X

46

39

48

Branch system



Branch system


Assumptions

Only one aerial tractor per branch

Aerial tractors may park in the origin of a

branch

Aerial tractors parked do not block the path


98


Results


Branch system


Assumptions



branch



99


Results


Branch system


Assumptions



branch



100


Results



101

Modeling

logic


Results


Number of trips

Route to land plot


Agenda

Introduction

Project stages

Results


Results


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast


Q: Quarterly

W: Weekly

D: Daily

Aerial

tractor

order


Results

Introduction Project stages Trabajo en cursoIntroduction Project stages Results

Average harvest cycle length: 8.3 days8 tractors

Land

plo

ts

Day in the quarter(days)


Results


Tons collected per day

8 tractors

Day

FF

B (

ton)

0

50

100

150

200

20 40 60 80

(days)


Results


8 tractors

Land

plo

ts

Day in the quarter

Average harvest cycle length: 10.9 days

(days)


Results


Tons collected per day

8 tractors

Day

0

50

100

150

20 40 60 80

FF

B (

ton)

(days)


Results

108


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order


Results

110


min

𝑗∈𝐽

𝑤𝑗𝑡𝑗𝑒

Earliness (hours)


Results

112


min

𝑗∈𝐽

𝑡𝑗𝑒

Earliness (hours)


Results

113

min

𝑗∈𝐽

𝑡𝑗𝑒

min

𝑗∈𝐽

𝑤𝑗𝑡𝑗𝑒

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9

Util

izat

ion

Harvest crew

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9

Util

izat

ion

Harvest crew


Project stages


Feasible?

Add constraints





land plot each day

Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order


Results - transport


Tons collected8 tractors

day 7day 1 day 3

SPCSPCSPC


Results - transport


8 tractors

1st trip for aerial tractor i

2nd trip for aerial tractor i

3rd trip for aerial tractor i

4th trip for aerial tractor i

Aerial tractor 1

Aerial tractor 2

Aerial tractor 3

Aerial tractor 4

Aerial tractor 5

Aerial tractor 6

Aerial tractor 7

Aerial tractor 8


Results - transport


Routes for aerial tractor 6

Land plots visited


Project stages


Feasible?

Add constraints



Harvest cycle

model Q


model D

Discrete-event

simulation W

Resource allocation

model W

Feasible?

Add constraints

Ripeness

cost

function

Yield

forecast

Q: Quarterly

W: Weekly

D: Daily


Aerial

tractor

order



land plot each day



-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

5 6 7 8 9 10 11Cha

nge

Amount of aerial tractors

Tiempo jornal Mallas/hora

10 hours 60 containers/hour

Results – uncertainty

Working day Containers/hour



0,99%

0,10%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

5 6 7 8 9 10 11Cha

nge








18,45%

-11,37%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

5 6 7 8 9 10 11Cha

nge








-3,26%

16,04%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

5 6 7 8 9 10 11Cha

nge









60%

65%

70%

75%

80%

85%

90%

5 6 7 8 9 10 11

Util

izat

ion


-20%

-10%

0%

10%

20%

30%

5 6 7 8 9 10 11Cha

nge


Tiempo jornal Mallas/horaWorking day Containers/hour


Conclusions

• We showed the potential of reducing the harvest cycle length from

19.6 days to 8.3 days.

• We highlighted the benefits of combining harvest and transport

planning.

• We measured the impact on the fruit evacuation metrics under failure

scenarios.

• We delivered results in a visual interactive interface to facilitate

decision-making at the plantation.

124


Thank you!

{m.escallon240, d.castillo1879, ja.leal222, andres.medaglia, copa}@uniandes.edu.co

http://copa.uniandes.edu.co

[email protected]

http://ceo.uniandes.edu.co

COPA CEO

http://copa.uniandes.edu.co/https://ceo.uniandes.edu.co/


Future work

• Implement the planning in the plantation.

• Adjust parameters if necessary.

• Incorporate other activities from the plantation.

• Integrate the models in a single computational tool that enables

parameter modifications, model execution and result visualization.

126

improving harvesting operations · 2020. 2. 20. · other crops harvest transport supply chain e-t...

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