Innovation is driven by passion. At Syngenta, it’s a passion to help farmers grow crops successfully year after year, increasing productivity, producing higher-quality crops and improving the sustainability of agriculture. Syngenta’s scientists are focused on accelerating innovation in plant science. Their goal → deliver consistent, reliable and high yield to farmers despite ever-changing environments due to variable weather conditions. Plant breeders work to maximize the amount of food we gain from crops by breeding plants with the most resilient, highest-yielding genetics, and then providing the seeds from those efforts to farmers around the world. With the advent of COVID-19, securing the world’s food supply has become even more critical. Planet Earth adds nearly 200,000 new mouths to feed every day. Yet our world is running out of cropland; land needed to produce food. We’ll add 2 billion more people by the year 2050, but we’re currently using our arable land and water 50 percent faster than the planet can sustain. At the same time, the crops farmers plant face an unprecedented set of obstacles due to increasingly challenging growing conditions driven by climate change. How will we be able to grow enough food to meet world demand?

THE CHALLENGE

Figure 1: Process diagram

Figure 2: Illustration of research question

Objective: Minimize the difference between the weekly harvest quantity and the capacity for each harvesting week . For each harvesting week and location: Min: weeklyharvestTotal - locationCapacity

. Capacity Constraint: For scenario 1, Site 0 has a capacity of 7000 ears and Site 1 has a capacity of 6000 ears. For scenario 2, there is not a predefined capacity. The participant is asked to determine the lowest capacity required.



Each week runs from Sunday – Saturday.

There are two scenarios for a given population’s harvest quantity. The two scenarios roughly emulate normal distributions: N(250,100) and N(350,150), respectively.

DELIVERABLES

DOWNLOAD SUBMISSION TEMPLATE



Submissions must be in MS-Word or LaTeX format using the appropriate submission template. You can download the submission template here (.zip) Creation of a planting schedule that:

1. Plants all populations within their available time window,

2. Ensures maximum capacity is not exceeded, and

3. Provides consistent weekly harvest quantity. Additionally, observing the standards for academic publication, entries should include a written report with the following: Quantitative results to justify your modeling techniques

A clear description of the methodology and theory

References or citations as appropriate Additionally, observing the standards for academic publication, entries should include a written report with the following:

EVALUATION CRITERIA

Quantitative evaluation metrics: The maximum and median difference between the weekly harvest quantity and the capacity among all harvesting weeks. Total number of harvest weeks – fewer is preferred. Recommendation of the lowest capacity required for both locations while still considering the other evaluation metrics. Note that, both locations will be evaluated equally.

Simplicity and intuitiveness of the solution

Clarity in the explanation

The quality and clarity of the finalist’s presentation at the 2021 INFORMS Conference on Business Analytics and Operations Research

DATASETS

Dataset #1: This dataset describes the input variables for an optimization model as well as the number of growing degree units (GDUs) in Celsius needed for harvest. Succinctly, GDUs are a measure of heat accumulation and are used to estimate specific stages of a plant’s growth cycle. In our dataset, for a given population the “required_gdus” is the number of heat units required in order for the corn population to be ready for harvesting. Dataset #2: This dataset describes the growing degree units in Celsius accumulated for each day for sites 0 and 1 over the last 10 years. Note that due to the formula for calculating GDUs, year-to-year, GDUs will be different. The participant will need to determine the best way to make use of this historical dataset. Dataset #3 (Output): This dataset is used for evaluation of the optimization model. This is where the planting date will be entered. Dataset #4 (Output): This dataset is used for evaluation of the optimization model. This is where the weekly harvest quantity and recommended capacity for scenario 2 will be entered.

Dataset #1 Description

Variable Description Population Seed population identifier site Planting site either 0 or 1 original_planting_date Actual planting date of the population early_planting_date Earliest the population could have been planted late_planting_date Latest the population could have been planted required_gdus Number of growing degree units needed for harvest scenario_1_harvest_quantity Harvest quantity (number of ears) for each population in scenario 1. The value in this column must be used as the harvest quantity, not just a percentage of this value. scenario_2_harvest_quantity Harvest quantity (number of ears) for each population in scenario 2. The value in this column must be used as the harvest quantity, not just a percentage of this value.



Dataset #2 Description

Variable Description date Calendar date site_0 GDUs accumulated for each calendar day at site_0 site_1 GDUs accumulated for each calendar day at site_1



Dataset #3 Description (Planting Schedule Output):

Variable Description population Population of seed scenario Scenario indicator site Planting site either 0 or 1 planting_date Planting date for the given population – to be completed by participant



Dataset #4 Description (harvest Quantity Output):

Variable Description scenario Scenario indicator site Planting site either 0 or 1 week Week index starting from the first week of January 2020. harvest_quantity Harvest quantity for the given week – to be completed by participant capacity Capacity for scenario 1 – to be completed by participant for scenario 2

Figure 3: Optimization model representation

TIMELINE

January 20, 2021

Deadline for Submissions



TBA

Finalists Announced



TBA

Finalist presentations.

Winners announced.





PRIZES

FIRST PLACE

$5,000



SECOND PLACE

$2,500



THIRD PLACE

$1,000



2020 WINNERS ANNOUNCED

WEBINARS

We’ve proven that data-driven strategies can help our industry breed more efficient, better seeds that require fewer resources and are adaptable to more diverse and variable environments. Developing models and analytical approaches that identify patterns and insights in our experimental data can help breeders more accurately choose seeds that increase the productivity of the crops we plant within shortened breeding cycles, and ultimately, help address the growing global food demand.Commercial corn is processed into multiple food and industrial products and is widely known as one of the world’s most important crops. However, it typically requires many years of in-field testing to deliver new products to market. Recently, innovative and novel technologies have shortened the time required to develop new corn hybrids—new products that can deliver higher-yielding, better-adapted seed options for growers at a faster pace. These promising technologies decrease the amount of time needed to create the parents of commercial hybrids. Commercial hybrids are created by crossing two parents together, so by reducing the amount of time to create these parents, scientists can deliver novel products to growers years faster. By continuously optimizing our product development system with these promising technologies, scientists can ensure increased crop yields for global food security.With the increased rate of producing parental lines comes new challenges—increased output (the number of harvested ears) can cause storage capacity limitations. Our year-round breeding process could be improved by optimizing planting schedules to achieve a consistent output – a weekly harvest quantity (number of ears).Erratic weekly harvest quantities create logistical and productivity issues. How can we optimally schedule the planting of our seeds to ensure that when ears are harvested, facilities are not over capacity, and that there is a consistent number of ears each week? This issue is the basis for the 2021 Syngenta Crop Challenge in Analytics. Figure 1 provides diagram for this process.Can an optimal scheduling model be created to ensurethat are below the maximum capacity? Figure 2 illustrates a representation of this problem.The objective is scheduling the planting date for each population to ensure the capacity constraints are met and that there is consistent harvest quantity. The following is the desired objective function.In summary, we desire an optimization model to schedule when planting should occur for a specific seed population so that when the ears are harvested, we are not over holding capacity.Additional NotesThe entries will be evaluated based on:You are provided with the following datasets described below.These tables provide the meaning of each variable in the four datasets.Two Q&A webinars will be available, the first one in October and the second in early November, that all participants may attend. Archives will be available to view HERE