An easy-to-use and modular Python library for the Job Shop Scheduling Problem (JSSP) with a special focus on graph representations.
It provides intuitive data structures to represent instances and solutions, as well as solvers and visualization tools.
See the this Google Colab notebook for a quick start guide!
You can install the library from PyPI:
pip install job-shop-lib
You can create a Job Shop Instance by defining the jobs and operations. An operation is defined by the machine(s) it is processed on and the duration (processing time).
from job_shop_lib import JobShopInstance, Operation
job_1 = [Operation(machines=0, duration=1), Operation(1, 1), Operation(2, 7)]
job_2 = [Operation(1, 5), Operation(2, 1), Operation(0, 1)]
job_3 = [Operation(2, 1), Operation(0, 3), Operation(1, 2)]
jobs = [job_1, job_2, job_3]
instance = JobShopInstance(
jobs,
name="Example",
# Any extra parameters are stored inside the
# metadata attribute as a dictionary:
lower_bound=7,
)
You can load a benchmark instance from the library:
from job_shop_lib.benchmarking import load_benchmark_instance
ft06 = load_benchmark_instance("ft06")
The module benchmarks
contains functions to load the instances from the file and return them as JobShopInstance
objects without having to download them
manually.
The contributions to this benchmark dataset are as follows:
-
abz5-9
: by Adams et al. (1988). -
ft06
,ft10
,ft20
: by Fisher and Thompson (1963). -
la01-40
: by Lawrence (1984) -
orb01-10
: by Applegate and Cook (1991). -
swb01-20
: by Storer et al. (1992). -
yn1-4
: by Yamada and Nakano (1992). -
ta01-80
: by Taillard (1993).
The metadata from these instances has been updated using data from: https://github.com/thomasWeise/jsspInstancesAndResults
>>> ft06.metadata
{'optimum': 55,
'upper_bound': 55,
'lower_bound': 55,
'reference': "J.F. Muth, G.L. Thompson. 'Industrial scheduling.', Englewood Cliffs, NJ, Prentice-Hall, 1963."}
You can also generate a random instance with the BasicGenerator
class.
from job_shop_lib.generators import BasicGenerator
generator = BasicGenerator(
duration_range=(5, 10), seed=42, num_jobs=5, num_machines=5
)
random_instance = generator.generate()
This class can also work as an iterator to generate multiple instances:
generator = BasicGenerator(iteration_limit=100, seed=42)
instances = []
for instance in generator:
instances.append(instance)
# Or simply:
instances = list(generator)
Every solver is a Callable
that receives a JobShopInstance
and returns a Schedule
object.
import matplotlib.pyplot as plt
from job_shop_lib.cp_sat import ORToolsSolver
from job_shop_lib.visualization import plot_gantt_chart
solver = ORToolsSolver(max_time_in_seconds=10)
ft06_schedule = solver(ft06)
fig, ax = plot_gantt_chart(ft06_schedule)
plt.show()
A dispatching rule is a heuristic guideline used to prioritize and sequence jobs on various machines. Supported dispatching rules are:
class DispatchingRule(str, Enum):
SHORTEST_PROCESSING_TIME = "shortest_processing_time"
FIRST_COME_FIRST_SERVED = "first_come_first_served"
MOST_WORK_REMAINING = "most_work_remaining"
MOST_OPERATION_REMAINING = "most_operation_remaining"
RANDOM = "random"
We can visualize the solution with a DispatchingRuleSolver
as a gif:
from job_shop_lib.visualization import create_gif, plot_gantt_chart_wrapper
from job_shop_lib.dispatching import DispatchingRuleSolver, DispatchingRule
plt.style.use("ggplot")
mwkr_solver = DispatchingRuleSolver("most_work_remaining")
plot_function = plot_gantt_chart_wrapper(
title="Solution with Most Work Remaining Rule"
)
create_gif(
gif_path="ft06_optimized.gif",
instance=ft06,
solver=mwkr_solver,
plot_function=plot_function,
fps=4,
)
The dashed red line represents the current time step, which is computed as the earliest time when the next operation can start.
Tip
You can change the style of the gantt chart with plt.style.use("name-of-the-style")
.
Personally, I consider the ggplot
style to be the cleanest.
One of the main purposes of this library is to provide an easy way to encode instances as graphs. This can be very useful, not only for visualization purposes but also for developing Graph Neural Network-based algorithms.
A graph is represented by the JobShopGraph
class, which internally stores a networkx.DiGraph
object.
The disjunctive graph is created by first adding nodes representing each operation in the jobs, along with two special nodes: a source
Additionally, the graph includes disjunctive edges between operations that use the same machine but belong to different jobs. These edges are bidirectional, indicating that either of the connected operations can be performed first. The disjunctive edges thus represent the scheduling choices available: the order in which operations sharing a machine can be processed. Solving the Job Shop Scheduling problem involves choosing a direction for each disjunctive edge such that the overall processing time is minimized.
from job_shop_lib.visualization import plot_disjunctive_graph
fig = plot_disjunctive_graph(instance)
plt.show()
Warning
This plot function requires having the optional dependency PyGraphViz installed.
The JobShopGraph
class provides easy access to the nodes, for example, to get all the nodes of a specific type:
from job_shop_lib.graphs import build_disjunctive_graph
disjunctive_graph = build_disjunctive_graph(instance)
>>> disjunctive_graph.nodes_by_type
defaultdict(list,
{<NodeType.OPERATION: 1>: [Node(node_type=OPERATION, value=O(m=0, d=1, j=0, p=0), id=0),
Node(node_type=OPERATION, value=O(m=1, d=1, j=0, p=1), id=1),
Node(node_type=OPERATION, value=O(m=2, d=7, j=0, p=2), id=2),
Node(node_type=OPERATION, value=O(m=1, d=5, j=1, p=0), id=3),
Node(node_type=OPERATION, value=O(m=2, d=1, j=1, p=1), id=4),
Node(node_type=OPERATION, value=O(m=0, d=1, j=1, p=2), id=5),
Node(node_type=OPERATION, value=O(m=2, d=1, j=2, p=0), id=6),
Node(node_type=OPERATION, value=O(m=0, d=3, j=2, p=1), id=7),
Node(node_type=OPERATION, value=O(m=1, d=2, j=2, p=2), id=8)],
<NodeType.SOURCE: 5>: [Node(node_type=SOURCE, value=None, id=9)],
<NodeType.SINK: 6>: [Node(node_type=SINK, value=None, id=10)]})
Other attributes include:
nodes
: A list of all nodes in the graph.nodes_by_machine
: A nested list mapping each machine to its associated operation nodes, aiding in machine-specific analysis.nodes_by_job
: Similar tonodes_by_machine
, but maps jobs to their operation nodes, useful for job-specific traversal.
Introduced in the paper "ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning" by Park et al. (2021), the Agent-Task Graph is a graph that represents the scheduling problem as a multi-agent reinforcement learning problem.
In contrast to the disjunctive graph, instead of connecting operations that share the same resources directly by disjunctive edges, operation nodes are connected with machine ones.
All machine nodes are connected between them, and all operation nodes from the same job are connected by non-directed edges too.
from job_shop_lib.graphs import (
build_complete_agent_task_graph,
build_agent_task_graph_with_jobs,
build_agent_task_graph,
)
from job_shop_lib.visualization import plot_agent_task_graph
complete_agent_task_graph = build_complete_agent_task_graph(instance)
fig = plot_agent_task_graph(complete_agent_task_graph)
plt.show()
For more details, check the examples folder.
-
Clone the repository.
-
Install poetry if you don't have it already:
pip install poetry==1.7
- Create the virtual environment:
poetry shell
- Install dependencies:
poetry install --with notebooks --with test --with lint --all-extras
or equivalently:
make poetry_install_all
If you don't want to use Poetry, you can install the library directly from the source code:
git clone https://github.com/Pabloo22/job_shop_lib.git
cd job_shop_lib
pip install -e .
This project is licensed under the MIT License - see the LICENSE file for details.
-
J. Adams, E. Balas, and D. Zawack, "The shifting bottleneck procedure for job shop scheduling," Management Science, vol. 34, no. 3, pp. 391–401, 1988.
-
J.F. Muth and G.L. Thompson, Industrial scheduling. Englewood Cliffs, NJ: Prentice-Hall, 1963.
-
S. Lawrence, "Resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques (Supplement)," Carnegie-Mellon University, Graduate School of Industrial Administration, Pittsburgh, Pennsylvania, 1984.
-
D. Applegate and W. Cook, "A computational study of job-shop scheduling," ORSA Journal on Computer, vol. 3, no. 2, pp. 149–156, 1991.
-
R.H. Storer, S.D. Wu, and R. Vaccari, "New search spaces for sequencing problems with applications to job-shop scheduling," Management Science, vol. 38, no. 10, pp. 1495–1509, 1992.
-
T. Yamada and R. Nakano, "A genetic algorithm applicable to large-scale job-shop problems," in Proceedings of the Second International Workshop on Parallel Problem Solving from Nature (PPSN'2), Brussels, Belgium, pp. 281–290, 1992.
-
E. Taillard, "Benchmarks for basic scheduling problems," European Journal of Operational Research, vol. 64, no. 2, pp. 278–285, 1993.
-
Park, Junyoung, Sanjar Bakhtiyar, and Jinkyoo Park. "ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning." arXiv preprint arXiv:2106.03051, 2021.