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# Modeling Supply Chains

In this example we will model the effects of the supply chain on substructure fabrication on
jacket-pile installations. For this example we will consider the following three cases:

1. Jacket installations with unlimited port storage.
2. Insufficient jacket fabrication to match installation rates.
3. Increased jacket fabrication to keep pace with installation rates.

```{code-cell} ipython3
from copy import deepcopy
from pathlib import Path

import pandas as pd
import matplotlib.pyplot as plt

from ORBIT import ProjectManager, load_config
from ORBIT.phases.install import MonopileInstallation, JacketInstallation

# Set the example path for use in the docs and standalone examples usage
here = Path(".").resolve()
match here.stem:
    case "examples":
        example_path = here
    case "topical_guides":
        example_path = here.parents[1] / "examples"
    case "ORBIT":
        example_path = here / "examples"
    case _:
        msg = "Please manually change `example_path` if running in a custom location."
        raise FileNotFoundError(msg)

weather = pd.read_csv(
    example_path / "data/example_weather.csv", parse_dates=["datetime"]
).set_index("datetime")
```

## Preparing The Cases

Here, we will load the
[`examples/configs/example_fixed_project.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/example_fixed_project.yaml)
configuration and modify it to run a jacket installation process. Please note that there is no
jacket design model in ORBIT, so we must provide the basic jacket parameterizations for the
design result.

```{code-cell} ipython3
base_jacket_config = load_config(example_path / "configs/example_fixed_project.yaml")
base_jacket_config["jacket"] = {
    "diameter": 10,
    "height": 100,
    "length": 100,
    "mass": 100,
    "deck_space": 100,
    "unit_cost": 1e6
}

slow_prod_config = deepcopy(base_jacket_config)
slow_prod_config["jacket_supply_chain"] = {
    "enabled": True,
    "substructure_delivery_time": 500,
    "num_substructures_delivered": 2,
}

increased_prod_config = deepcopy(base_jacket_config)
increased_prod_config["jacket_supply_chain"] = {
    "enabled": True,
    "substructure_delivery_time": 250,
    "num_substructures_delivered": 2,
}

case1_project = JacketInstallation(base_jacket_config, weather=weather)
case2_project = JacketInstallation(slow_prod_config, weather=weather)
case3_project = JacketInstallation(increased_prod_config, weather=weather)

case1_project.run()
case2_project.run()
case3_project.run()
```

## Comparing Results

### Installation Time

```{code-cell} ipython3
print(f"Case 1 Installation Time: {case1_project.total_phase_time / 24:.1f} days")
print(f"Case 2 Installation Time: {case2_project.total_phase_time / 24:.1f} days")
print(f"Case 3 Installation Time: {case3_project.total_phase_time / 24:.1f} days")
```

### Installation Timing

Below, we plot the timing of jacket deliveries and their subsequent installations without
considering vessel logistic delays. Note the inclement weather around the 400th day of
Case 2's installation simulation.

```{code-cell} ipython3
case2_df = pd.DataFrame(case2_project.env.actions)
case2_deliveries = case2_df.loc[case2_df["action"].str.contains("Delivered"), ["action", "time"]]
case2_deliveries["number"] = case2_deliveries["action"].str.split(" ").str[1].astype(int)

case2_installs = case2_df.loc[case2_df["action"].str.contains("Grout Jacket"), ["action", "time"]]
case2_installs["number"] = 1

case2_deliveries.time /= 24.0
case2_installs.time /= 24.0


case3_df = pd.DataFrame(case3_project.env.actions)
case3_deliveries = case3_df.loc[case3_df["action"].str.contains("Delivered"), ["action", "time"]]
case3_deliveries["number"] = case3_deliveries["action"].str.split(" ").str[1].astype(int)

case3_installs = case3_df.loc[case3_df["action"].str.contains("Grout Jacket"), ["action", "time"]]
case3_installs["number"] = 1

case3_deliveries.time /= 24.0
case3_installs.time /= 24.0
```

```{code-cell} ipython3
fig = plt.figure(figsize=(8,4), dpi=200)
ax = fig.add_subplot(111)

ax.scatter(
    case2_deliveries["time"],
    case2_deliveries["number"].cumsum(),
    marker="o",
    c="tab:blue",
    label="Substructure Delivered - Slow Fabrication"
)
ax.scatter(
    case2_installs["time"],
    case2_installs["number"].cumsum(),
    marker="x",
    c="tab:blue",
    label="Completed Installation - Slow Fabrication"
)

ax.scatter(
    case3_deliveries["time"],
    case3_deliveries["number"].cumsum(),
    marker="o",
    c="tab:orange",
    label="Substructure Delivered - Increased Fabrication"
)
ax.scatter(
    case3_installs["time"],
    case3_installs["number"].cumsum(),
    marker="x",
    c="tab:orange",
    label="Completed Installation - Increased Fabrication"
)

ax.set_xlim(0, ax.get_xlim()[1])
ax.set_ylim(0, 60)

ax.set_xlabel("Simulation Time (Days)")
ax.set_ylabel("Substructures")

ax.legend()
ax.grid()
fig.tight_layout()
```

### Port Storage

```{code-cell} ipython3
fig = plt.figure(figsize=(8,4), dpi=200)
ax = fig.add_subplot(111)

case2_installs_neg = case2_installs.copy()
case2_installs_neg["number"] *= -1
case2_total = pd.concat([case2_deliveries, case2_installs_neg]).sort_values("time")
case2_total["storage"] = case2_total["number"].cumsum()

case3_installs_neg = case3_installs.copy()
case3_installs_neg["number"] *= -1
case3_total = pd.concat([case3_deliveries, case3_installs_neg]).sort_values("time")
case3_total["storage"] = case3_total["number"].cumsum()

ax.plot(
  case2_total["time"],
  case2_total["storage"],
  label="Storage Required - Slow Fabrication"
)
ax.plot(
  case3_total["time"],
  case3_total["storage"],
  label="Storage Required - Increased Fabrication"
)

ax.set_xlim(0, ax.get_xlim()[1])
# ax.set_ylim(0, 5)

ax.axhline(4, ls="--", lw=0.5, c="k", label="Theoretical Port Storage Limit")

ax.set_xlabel("Simulation Time (h)")
ax.set_ylabel("Substructures")

ax.legend()
fig.show()
```