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Heuristic Search in LegalTech: Dynamic Allocation of Legal Cases to Legal Staff

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

Abstract

We investigate an allocation problem inspired by the process of assigning legal cases (matters) to staff in law firms. Addressing this problem is important as it can prevent issues around unbalanced workloads and over-recruitment, thus decreasing costs. This initial study on the topic frames the problem as a combinatorial dynamic single-objective problem (minimising tardiness) with constraints modelling staff-client relationships, staff capacities, and earliest start dates of matters. The paper motivates the allocation problem and puts it in context with the literature. Further contributions include: (i) a formal problem definition, (ii) the proposal and validation of a feature-rich problem generator to create realistic test cases, (iii) an initial analysis of the performance of selected heuristics (a greedy approach, a nature-inspired approach, and random search) on different test instances, and finally (iv) a discussion on directions for future research.

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Notes

  1. 1.

    Implementations are presented in https://github.com/mayoayodele/MAPSolver.

  2. 2.

    Code of the generator is available at https://github.com/mayoayodele/MatterAllocationProblemGenerator.

  3. 3.

    The test instances can be found at https://github.com/mayoayodele/MatterAllocationProblemGenerator/tree/master/Data.

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Authors and Affiliations

  1. The University of Manchester, Manchester, UK

    Mayowa Ayodele, Richard Allmendinger & K. Nadia Papamichail

Authors
  1. Mayowa Ayodele
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  2. Richard Allmendinger
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  3. K. Nadia Papamichail
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Corresponding author

Correspondence to Mayowa Ayodele .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Ayodele, M., Allmendinger, R., Papamichail, K.N. (2020). Heuristic Search in LegalTech: Dynamic Allocation of Legal Cases to Legal Staff. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

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