{ "info": { "author": "David Torres", "author_email": "d.torressanchez@lancs.ac.uk", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "cspy\n====\n\nA collection of algorithms for the (resource) Constrained Shortest Path\n(CSP) problem.\n\nThe CSP problem was popularised by `Inrich 2005`_. \nIt was initially introduced as a subproblem for the bus\ndriver scheduling problem, and has since then widely studied in a\nvariety of different settings including: the vehicle routing problem\nwith time windows (VRPTW), the technician routing and scheduling\nproblem, the capacitated arc-routing problem, on-demand transportation\nsystems, and, airport ground movement; among others.\n\nMore generally, in the applied column generation framework, particularly\nin the scheduling related literature, the CSP problem is commonly\nemployed to generate columns.\n\nTherefore, this library is of interest to the operational research\ncommunity, students and academics alike, that wish to solve an instance\nof the CSP problem.\n\nAlgorithms\n----------\n\nCurrently, the algorithms implemented include:\n\n- Monodirectional forward labeling algorithm;\n- Monodirectional backward labeling algorithm;\n- Bidirectional labeling algorithm with static halfway point;\n- Bidirectional labeling algorithm with dynamic halfway point `Tilk et al 2017`_;\n- Heuristic Tabu search;\n- Greedy elimination procedure;\n- Greedy Randomised Adaptive Search Procedure (GRASP). Adapted from\n `Ferone et al 2019`_;\n- Particle Swarm Optimization with combined Local and Global Expanding Neighborhood Topology (PSOLGET) (`Marinakis et al 2017`_).\n\nFeatures\n--------\n\n- Generic resource extension functions (`Inrich 2005`_) (not restricted to additive resources);\n- Generic resource consumptions (not restricted to non-negative values).\n\nPrerequisites\n-------------\n\nConceptual background and input formatting is discussed in the\n`docs`_.\n\nUsage Examples\n--------------\n\nPlease see the individual algorithms API Documentation for specific\nexamples and more details:\n\n- ``BiDirectional``: `Bidirectional and monodirectional algorithms`_\n- ``Tabu`` `Heuristic Tabu Search`_\n- ``GreedyElim`` `Greedy Elimination Procedure`_\n- ``GRASP`` `GRASP`_\n- ``PSOLGENT`` `PSOLGENT`_ \n\n\nPlease see individual algorithm documentation for examples.\n\n.. _Bidirectional and monodirectional algorithms: https://cspy.readthedocs.io/en/latest/api/cspy.BiDirectional.html\n.. _Heuristic Tabu Search: https://cspy.readthedocs.io/en/latest/api/cspy.Tabu.html\n.. _Greedy Elimination Procedure: https://cspy.readthedocs.io/en/latest/api/cspy.GreedyElim.html\n.. _Particle Swarm Optimization with combined Local and Global Expanding Neighborhood Topology: https://cspy.readthedocs.io/en/latest/api/cspy.PSOLGENT.html\n.. _GRASP: https://cspy.readthedocs.io/en/latest/api/cspy.GRASP.html\n.. _PSOLGENT: https://cspy.readthedocs.io/en/latest/api/cspy.PSOLGENT.html\n\nContributing\n------------\n\nFeel free to contribute to this project either by either working trough\nsome of issues flagged as help wanted, or raise a new issue with any\nbugs/improvements.\n\nIf you have a question or need help, feel free to raise an\n`issue`_ explaining it.\n\n\n.. _Tilk et al 2017: https://www.sciencedirect.com/science/article/pii/S0377221717302035\n.. _Inrich 2005: 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