[Feature] Update generation module to support creating instance with arrival times, due dates, and deadlines

.flake8

@@ -1,5 +1,5 @@
 [flake8]
-extend-ignore = E203
+extend-ignore = E203, RST303
 # Tell flake8-rst-docstrings about Sphinx directives/roles
 rst-directives = autosummary,seealso,deprecated,doctest
 rst-roles = class,meth,func,attr

README.md

@@ -20,13 +20,13 @@ It follows a modular design, allowing users to easily extend the library with ne
 We support multiple solvers, including:
 - **Constraint Programming**: Based on OR-Tools' CP-SAT solver. It supports **release dates, deadlines, and due dates.** See the ["Solving the Problem" tutorial](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/02-Solving-the-Problem.ipynb) for an example.
 - **Dispatching Rules**: A set of predefined rules and the ability to create custom ones. They support arbitrary **setup times, machine breakdowns, release dates, deadlines, and due dates**. See the [following example](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/examples/03-Dispatching-Rules.ipynb). You can also create videos or GIFs of the scheduling process. For creating GIFs or videos, see the [Save Gif example](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/examples/04-Save-Gif.ipynb).
-- **Metaheuristics**: Currently, we have a **simulated annealing** implementation that supports **release dates, deadlines, and due dates**. We also support arbitrary neighborhood search strategies, including swapping operations in the critical path as described in the paper "Job Shop Scheduling by Simulated Annealing" by van Laarhoven et al. (1992); and energy functions. See our [simulated annealing tutorial](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/03-Simulated-Annealing.ipynb).
+- **Metaheuristics**: Currently, we have a **simulated annealing** implementation that supports **release dates, deadlines, and due dates**. We also support arbitrary neighborhood search strategies, including swapping operations in the critical path as described in the paper "Job Shop Scheduling by Simulated Annealing" by van Laarhoven et al. (1992); and energy functions. See our [simulated annealing tutorial](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/04-Simulated-Annealing.ipynb).
 - **Reinforcement Learning**: Two Gymnasium environments for solving the problem with **graph neural networks** (GNNs) or any other method. The environments support **setup times, release dates, deadlines, and due dates.** We're currently building a tutorial on how to use them.
 
 We also provide useful utilities, data structures, and visualization functions:
 - **Intuitive Data Structures**: Easily create, manage, and manipulate job shop instances and solutions with user-friendly data structures. See [Getting Started](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/00-Getting-Started.ipynb) and [How Solutions are Represented](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/01-How-Solutions-are-Represented.ipynb).
 - **Benchmark Instances**: Load well-known benchmark instances directly from the library without manual downloading. See [Load Benchmark Instances](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/examples/05-Load-Benchmark-Instances.ipynb).
-- **Random Instance Generation**: Create random instances with customizable sizes and properties. See [`generation`](https://job-shop-lib.readthedocs.io/en/stable/api/job_shop_lib.generation.html#module-job_shop_lib.generation) module.
+- **Random Instance Generation**: Create random instances with customizable sizes and properties. See [`this tutorial`](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/03-Generating-New-Instances.ipynb).
 - **Gantt Charts**: Visualize final schedules and how they are created iteratively by dispatching rule solvers or sequences of scheduling decisions with GIFs or videos.
 - **Graph Representations**: Represent and visualize instances as disjunctive graphs or agent-task graphs (introduced in the ScheduleNet paper). Build your own custom graphs with the `JobShopGraph` class. See the [Disjunctive Graphs](https://github.com/Pabloo22/job_shop_lib/blob/main/docs/source/tutorial/04-Disjunctive-Graphs.ipynb) and [Resource Task Graphs](https://job-shop-lib.readthedocs.io/en/stable/examples/07-Resource-Task-Graph.html) examples.
 

docs/source/tutorial.rst

@@ -7,4 +7,5 @@ Tutorial
     tutorial/00-Getting-Started
     tutorial/01-How-Solutions-are-Represented
     tutorial/02-Solving-the-Problem
-    tutorial/03-Simulated-Annealing
+    tutorial/03-Generating-New-Problems
+    tutorial/04-Simulated-Annealing