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Showing 2 results for Resource Allocation
Mrs. S. Madadi, Dr. F. Hosseinzadeh Lotfi, Dr. M. Rostamy-Malkhalifeh, Dr. M. Fallah Jelodar,
Volume 9, Issue 1 (7-2018)
Abstract
Resource allocation is a problem that commonly appears in organization with a centralized decision making (CDM), who controls the units. The aim of central decision making is to allocate resources in such a way that the organization get the most benefit. Some Data Envelopment Analysis (DEA) researchers presented DEA-based resource allocation models by paying attention to energy saving and environmental pollution reduction. In this paper, we expanded a resource allocation model for 25 branches of an Iranian Tejarat bank, so that determined how much decision making (DM) can save on energy and manpower hours, so that undesirable outputs like non-performing loans are significantly reduced in a way that achieve the minimum reduction of desirable outputs while unchanged the performance of each unit after re-allocation. The result of the implementation of the model shows that in total with a 10% and 23% reduction in staff and costs respectively can result in the 0.09% reduction of deposits and 56% of non-performing loans.
Fatemeh Mohajernia, Jafar Rahmani, Mohsen Mohajernia,
Volume 17, Issue 1 (5-2026)
Abstract
Extant quantitative models in Resource Allocation and Operational Systems often rely on statistical optimization or behavioral frameworks, frequently failing to model and quantify the deterministic structural conflicts that arise when operational and informational constraints are ignored. This paper addresses a critical analytical gap in Industrial Information Integration: the necessity of a deterministic modeling framework to quantify the systemic risks inherent in resource scarcity and misallocation. We introduce a novel analytical framework, derived from the Generalized Pigeonhole Principle (GPP), to quantify the inevitable consequences of resource imbalance within complex organizational systems. The framework develops a three-tiered conceptual model: (1) the Base Principle (quantifying the inevitable Structural Non-Allocation Rate, (N-M)), (2) the Generalized Principle (modeling Inevitable Operational Congestion, λ min), and (3) the Weighted Principle (quantifying Qualitative Mismatch, f). We demonstrate the model's predictive and explanatory power through scenario-based analytical simulations, providing a foundational validation for its applicability across workforce planning and capacity development contexts. The analysis yields testable propositions linking these mathematical inevitabilities directly to systemic consequences (e.g., structural attrition/turnover, intragroup operational conflict). This research provides a new, parsimonious analytical language for Operational Systems Modeling, fundamentally shifting the focus from optimizing individual performance to managing unavoidable structural pressures. The model serves as a robust quantitative decision-support tool for strategic workforce planning and lays the essential groundwork for future dynamic modeling of operational capacity and resource demand.
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