This paper presents a new statistical process control model for the economic–statistical optimization of a Variable-Parameter (Vp) Shewhart control scheme for monitoring processes where multiple assignable causes may occur. The assignable causes affect not only the mean but also the variance of a specific quality characteristic by leading to progressive process deterioration. The contemporary occurrence of the assignable causes and the different corrective action for each assignable cause makes the proposed model more realistic. The operation of the proposed control scheme is stochastically modelled by means of the Markov chain theory. The design parameters vary to get an economic and statistical performance optimization of the control scheme. An extended numerical investigation is utilized to demonstrate the economic and statistical superiority of the proposed model against simpler approaches. An example from aviation industry is presented to illustrate the application of the model.

The zero‐inflated Conway‐Maxwell Poisson (ZICMP) distribution models count data with many zero observations. ZICMP model has been developed assuming that zero observations exist with probability and the number of non‐conformities in a product unit follows the Conway‐Maxwell Poisson (COM‐Poisson) distribution with location parameter and dispersion parameter . This article presents four kinds of cumulative sum (CUSUM) charts for monitoring upward shifts in a ZICMP process. Three CUSUM schemes, namely ‐CUSUM, ‐CUSUM, and ‐CUSUM, have been designed to detect shift only in one parameter assuming that the other two are fixed and one CUSUM scheme, namely ‐CUSUM, has been designed to detect shifts in all the parameters. The performance of the proposed charts has been evaluated in terms of the average run‐length (ARL). Finally, a numerical example is given to demonstrate the application of the proposed charts.

Traditional Operations Research (OR) models incorporate structured numerical inputs, such as demand parameters, costs, emissions, processing times, facility and transport mode capacities, to simulate stochastic behaviors and optimize objective-functions. There is however a vast number of OR-relevant parameters that can be derived from textual and semi-structured sources, such product reviews, social media, news, contracts, procurement documents, ESG reports, policy texts, patents, maintenance records, technical manuals and others. Under this context, the purpose of this study is to develop an artifact and parameter-centered framework for explaining how such textual inputs are transformed into model-ready OR components and incorporated into forecasting, simulation, optimization, logistics, inventory, sustainability, procurement, network analysis, and decision-support models. The main insights derived from the employed framework reveal that: (i) the OR value of textual information lies not in text analysis itself, but in its transformation into validated model-ready artifacts, such as covariates, parameters, constraints, scenarios, rules, weights, graph relations, simulation triggers, and solver inputs; (ii) different textual sources and language-processing methods can generate distinct OR artifacts that enter models through different integration mechanisms, including covariate augmentation, parameter updating, constraint generation, scenario definition, objective-function weighting, graph construction, retrieval support, and solver-code generation; (iii) the same text-derived artifact may play different roles across OR model types, for example a disruption event may update a simulation scenario, increase a lead-time parameter, remove a routing arc, or modify a supplier-risk penalty; and (iv) evaluation must extend beyond NLP accuracy to include artifact validity, parameter validity, model feasibility, mathematical consistency, solver correctness, deployment reliability, and downstream decision usefulness.

This study addresses the aerial firefighting resource allocation problem, formulated as the strategic prepositioning of a heterogeneous aerial firefighting fleet across capacity‐constrained airbases. A novel bi‐objective model is proposed that simultaneously optimizes efficiency (aggregate risk‐weighted accessibility) and equity (minimized regional service disparities). The model employs an enhanced spatial accessibility method with a distance‐decay function to evaluate coverage effectiveness and distribution fairness. A multi‐objective genetic algorithm is implemented to generate Pareto‐approximate solutions, revealing quantifiable trade‐offs. Applied to a national‐scale case study in Greece using 25 years of wildfire data, results demonstrate that the proposed framework provides significantly wider coverage than current practices, thereby extending protection to underserved regions while reducing redundant overlap. Sensitivity analyses confirm robustness across various real‐world conditions, such as different budget levels and aircraft availability. This transferable tool enables policymakers to make data‐driven decisions by quantifying the balance between response effectiveness and social fairness in wildfire‐prone regions.