cost and effort estimation

This study addresses task sequencing, resource allocation, and multi-constraint optimization during project planning, aiming to jointly minimize makespan and cost. We propose a sequential concession-based iterative task hierarchical decomposition method incorporating an “ideal point” concept to enable decision-makers to dynamically trade off time–cost preferences. We introduce the first modeling framework for synchronous task execution, integrated with Boolean programming to compute minimum-cost feasible schedules. Furthermore, we develop a hybrid qualitative–quantitative multi-objective decision support model that rigorously derives computable lower bounds for both makespan and cost, and generates tunable Pareto-optimal management plans. The model has been validated across educational, research, and industrial production scenarios, demonstrating significant improvements in plan robustness and execution efficiency.

Accurate project duration and cost forecasting remains challenging in resource-constrained, highly interdependent task networks. This paper proposes a graph neural network (GNN)-based joint temporal–cost prediction framework. We construct a heterogeneous activity–resource dynamic graph that explicitly encodes task dependencies and resource allocation constraints. To overcome the static assumptions of traditional Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT), we design a resource-aware heterogeneous graph representation learning approach, enabling bottleneck identification and interpretable critical path analysis. The framework integrates GraphSAGE for structural encoding and Temporal Graph Networks (TGN) for modeling evolutionary dynamics. Evaluated on synthetic and benchmark project datasets, our method achieves 23–31% lower absolute error and an R² of 0.91 compared to conventional approaches, demonstrating substantial improvements in predictive accuracy and adaptability to dynamic scheduling scenarios.

This paper addresses the disconnection between resource scheduling and system modeling in complex large-scale projects. We establish, for the first time, a systematic linkage among the Resource-Constrained Project Scheduling Problem (RCPSP), Model-Based Systems Engineering (MBSE), and Heterogeneous Functional Graph Theory (HFGT). A traceable transformation mechanism is proposed—from RCPSP activity networks to SysML activity diagrams, heterogeneous functional graphs, and operational networks—reformulating RCPSP as a minimum-cost flow problem within a heterogeneous functional network. This enables unified modeling and optimization of both renewable and non-renewable resources. The method preserves classical scheduling performance while yielding interpretable, semantically rich project state representations. As a result, it significantly enhances dynamic monitoring capabilities for complex systems and strengthens enterprise-level decision support.

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

Model-driven software refactoring suffers from excessive computational overhead in multi-objective optimization, particularly under resource constraints. Method: This paper systematically investigates the impact of search budget—especially time constraints—on the performance of multi-objective evolutionary algorithms (MOEAs) for automated architectural refactoring. Leveraging software architecture models, we integrate automated refactoring search with Pareto front quality assessment using Hypervolume (HV) and Inverted Generational Distance (IGD). Contribution/Results: We empirically reveal significant differences across MOEAs in solution-set distribution, structural diversity, and front quality under budget limitations—marking the first such analysis in this domain. We propose a time-aware algorithm selection guideline, quantifying the nonlinear degradation of solution quality with increasing time budget and explicitly delineating each algorithm’s effective applicability boundary. The findings enhance the practicality and industrial deployability of automated refactoring in resource-constrained environments.

Traditional software cost estimation models are ill-suited for AI agent–driven development paradigms, as they fail to account for novel, non-deterministic cost factors such as LLM invocation, human-in-the-loop collaboration, and infrastructure overhead. This work proposes ACEM, the first cost estimation framework tailored to agent-based software engineering, which decomposes total cost into three components: LLM usage cost, human-in-the-loop (HITL) supervision cost, and infrastructure cost. The model incorporates correction factors, context-aware multipliers, and a HITL intensity score to capture dynamic aspects of agent-assisted development. By employing a symbolic constant structure, ACEM enables mapping to conventional size metrics—such as use case points and story points—and supports calibration and prediction using historical project data, thereby establishing a formal and empirically grounded foundation for cost estimation in AI agent–centric software development.

研究通过FWBench工具评估了语言模型在成本限制下选择和使用时间序列预测进行决策的能力，测试了包括小型语言模型在内的十种配置。

本文提出内部外部性概念，解释了组织内部机制如何导致努力相互抵消，产生结构性低效，并建议先解决结构问题再优化资源配置。

This study addresses the allocation of multiple heterogeneous resource types in hierarchical organizations, where certain resources can be transformed into one another. The problem is formulated as a market equilibrium model incorporating structural constraints inherent to the hierarchy. To solve it efficiently, the authors propose a novel two-stage approximation algorithm: first solving a tractable instance that respects the hierarchical structure, then iteratively refining the solution to handle general cases. This work introduces, for the first time, a two-stage approximation framework to hierarchical resource allocation with conversion capabilities, establishing both the guaranteed existence of feasible equilibria and computational efficiency. Experiments on real-world Google TPU/GPU allocation datasets demonstrate the algorithm’s effectiveness and rapid convergence.

研究通过对比实验探讨了在API合同中明确高推理努力与省略该条款对成本和准确性的影响，发现明确高努力会增加成本但未显著提高准确性。