Paper: arXiv 2507.01968

Authors: Chris Duckworth, Zlatko Zlatev, James Sciberras, Peter Hallett, Enrico Gerding

Abstract

Purpose: Financial service companies manage huge volumes of data which requires timely error identification and resolution. The associated tasks to resolve these errors frequently put financial analyst workforces under significant pressure leading to resourcing challenges and increased business risk. To address this challenge, we introduce a formal task allocation model which considers both business orientated goals and analyst well-being. Methodology: We use a Genetic Algorithm (GA) to optimise our formal model to allocate and schedule tasks to analysts. The proposed solution is able to allocate tasks to analysts with appropriate skills and experience, while taking into account staff well-being objectives. Findings: We demonstrate our GA model outperforms baseline heuristics, current working practice, and is applicable to a range of single and multi-objective real-world scenarios. We discuss the potential for metaheuristics (such as GAs) to efficiently find sufficiently good allocations which can provide recommendations for financial service managers in-the-loop. Originality: A key gap in existing allocation and scheduling models, is fully considering worker well-being. This paper presents an allocation model which explicitly optimises for well-being while still improving on current working practice for efficiency.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a formal probabilistic model for multi-objective task allocation in financial operations, explicitly incorporating worker well-being alongside operational risk. While the mathematical formulations rely on standard metaheuristic techniques (Genetic Algorithms), the empirical validation using realistic asset management workforce data is well-executed.

Research Flowchart

  flowchart TD
    A[Research Goal: Balance Business Goals & Analyst Well-being in Task Allocation] --> B[Inputs: Tasks, Analyst Skills, Well-being Objectives & Business Constraints]
    B --> C[Model Formulation: Formal Multi-objective Task Allocation Model]
    C --> D[Optimization Engine: Run Genetic Algorithm]
    D --> E[Evaluation: Compare GA Performance against Baseline Heuristics & Working Practice]
    E --> F[Findings: GA Outperforms Baselines & Provides Manager-in-the-Loop Recommendations]

Cited References (61)

  1. PyGAD: An Intuitive Genetic Algorithm Python Library (2021) — link
  2. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence (1992)
  3. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems (2001)
  4. Efficient task allocation approach using genetic algorithm for cloud environment (2019)
  5. Maximizing reliability of distributed computing system with task allocation using simple genetic algorithm (2001)
  6. Multi-UAV task allocation based on improved genetic algorithm (2021)
  7. Genetic algorithm for task allocation in UAV cooperative control (2003)
  8. Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding (2018)
and 53 more
  1. A genetic algorithm for subtask allocation within human and robot coordinated assembly (2012)
  2. Genetic algorithm-based task allocation in multiple modes of human–robot collaboration systems with two cobots (2022)
  3. Task scheduling method for HRC workplaces based on capabilities and execution time assumptions for robots (2020)
  4. Multi-robot auctions for allocation of tasks with temporal constraints (2015)
  5. Adaptive mutation in genetic algorithms (2000)
  6. An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments (2020)
  7. Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department (2007)
  8. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments (2018)
  9. The bargaining problem (1950)
  10. Microeconomic theory (1995)
  11. Bargaining and fairness (2014)
  12. A note on two problems in connexion with graphs (2022)
  13. Staff scheduling and rostering: A review of applications, methods and models (2004) — link
  14. Personnel scheduling: A literature review (2013)
  15. Job assignment based on brain demands and human resource strategies (2017)
  16. A genetic algorithm based approach for the simultaneous optimisation of workforce skill sets and team allocation (2016)
  17. Assignment and allocation optimization of partially multiskilled workforce (2002)
  18. An intelligent model for workforce allocation taking into consideration the operator skills (2021)
  19. Optimizing workforce allocation under uncertain activity duration (2023)
  20. The job demands-resources model of burnout. (2001)
  21. When job resources function as a stress buffer: A resource allocation perspective of the job demands-resources model (2022)
  22. Optimization of job allocation in construction organizations to maximize workers’ career development opportunities (2019)
  23. Job Openings and Labor Turnover Summary, US Bureau of Labor Statistics (2022) — link
  24. Companies Need To Know The Dollar Cost Of Employee Turnover (2018) — link
  25. Working Conditions Survey (2022) — link
  26. Financial Analysts - Work Environment, US Bureau of Labor Statistics (2023) — link
  27. Job demands–resources theory: Taking stock and looking forward. (2017)
  28. WSO 2021 Investment Banking Work-Conditions Survey (2021) — link
  29. Selection policies for a multifunctional workforce (2014)
  30. Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms (2007)
  31. The trade off between diversity and quality for multi-objective workforce scheduling (2006)
  32. What is the analytic hierarchy process? (1988)
  33. Multicriteria decision-aid (1992)
  34. Multiple attribute decision making: methods and applications (2011)
  35. Note—A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making) (1985)
  36. Decisions with multiple objectives: preferences and value trade-offs (1993)
  37. Multi-criteria and multi-objective decision making for land allocation using GIS (1998)
  38. Multi-criteria decision-making methods to address water allocation problems: A systematic review (2021)
  39. An extensive review of computational intelligence-based optimization algorithms: trends and applications (2020)
  40. Rough sets (1982)
  41. Fuzzy set theory—and its applications (2011)
  42. Evolution strategies–a comprehensive introduction (2002)
  43. Artificial intelligence a modern approach (2010)
  44. Ant system: optimization by a colony of cooperating agents (1996)
  45. Nature-inspired metaheuristic algorithms (2010)
  46. Particle swarm optimization (1995)
  47. An improved adaptive genetic algorithm (2022)
  48. The use of evolutionary algorithms for optimization in the modern entrepreneurial economy: interdisciplinary perspective (2019)
  49. A Systematic Approach to Predicting NAV Errors (2024) — link
  50. Preventing the “huge consequences” of NAV oversight error (2021) — link
  51. Evaluation of factors influencing job satisfaction (2014)
  52. Job satisfaction of recent graduates in financial services (2003)
  53. Reinforcement learning in dynamic task scheduling: A review (2020)

0 of 61 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.