Adaptive Efficiency Optimization in SDLC: An MILP Approach for Balanced and Cost-Effective Resource Allocation

TL;DR

This paper presents a MILP-based method for optimizing human resource allocation in software development, balancing workloads and minimizing costs through adaptive, data-driven task assignment strategies.

Contribution

It introduces a novel MILP model that dynamically incorporates employee efficiency and task complexity for balanced and cost-effective resource allocation.

Findings

Outperforms traditional assignment strategies in fairness and cost efficiency

Demonstrates adaptability through performance-based efficiency updates

Proves scalability and robustness in simulated project scenarios

Abstract

The efficient allocation of human resources is a critical concern in software development and other industries. This paper introduces a rigorous mathematical methodology for task assignment, employing Mixed Integer Linear Programming (MILP) to ensure both balanced workloads and cost minimization. The proposed model systematically integrates individual employee efficiency, task complexity, and performance metrics to reflect real organizational dynamics. The formulation is guided by two principal objectives: firstly, to achieve equitable work load distribution commensurate with employee efficiency, and secondly, to minimize overall project costs by accounting for task difficulty and individual proficiency. Furthermore, the approach incorporates adaptive updates to efficiency parameters based on observed performance, thereby enhancing its practical applicability. Empirical evaluation using simulated datasets demonstrates the superiority of the proposed method over conventional assignment strategies in terms of both workload fairness and cost reduction. The findings underscore the potential of this MILP based framework as a robust, scalable, and adaptable solution for contemporary human resource allocation challenges in project management contexts.