A genetic algorithm cube for multi-criteria multi-resource fixed cost allocation

Purpose Allocating common costs across projects is a challenging problem in management accounting. Analytical techniques for allocating multiple costs are limited in availability. This study aims to propose an analytical method for allocating multiple fixed costs across multiple managerial criteria.

Design/methodology/approach This paper investigates different sequential and data envelopment analysis models to identify conditions and propose solution procedures for multi-criteria multi-resource (MCMR) fixed-cost allocation (FCA) problems.

Findings The paper shows that certain multi-resource, single-criterion problems can be solved as independent, multi-stage, single-resource, fixed-cost allocation problems. However, for MCMR-FCA problems with conflicting criteria, a three-dimensional genetic algorithm cube data structure is necessary to solve them.

Practical implications This paper presents a range of analytical and heuristic methods for allocating multiple costs and resources across projects based on a broad set of managerial criteria.

Originality/value To the best of the authors’ knowledge, this study is a pioneering work on allocating multiple fixed costs to multiple projects under multiple managerial criteria.

The version of record is available at https://doi.org/10.1108/JM2-11-2025-0645. The full citation is as follows: [A genetic algorithm cube for multi-criteria multi-resource fixed cost allocation. Journal of Modelling in Management p1-24 (2026)]. 'This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com'