Fault detection process (FDP) and Fault correction process (FCP) are important phases of software development life cycle (SDLC). It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.

Modern hospital operations need more advanced optimization methods due to the factors such as the variation in patient demand, limited resources, and complicated workforce regulations. The initial research suggested a combined Linear and Mixed-Integer Linear Programming (LP/MILP) approach to the joint optimization of patient admissions, bed/OR utilization, and nurse scheduling. The model unified the operational costs and the staff preferences into a single weighted objective, thereby showing the very significant resource utilization and scheduling satisfaction improvements. We have extended the framework from its original version and in this extended work we are going to demonstrate how the nurse scheduling component is fashioned into an actual multi-objective optimization problem. Rather than addressing the problem via a single weighted aggregation, two opposing targets, minimizing overall operational cost and maximizing nurse preference satisfaction, are treated openly. Moreover, we introduce the Adaptive ε-Constraint method that allows us to take advantage of the division between coarse ε sweep and local refinement to produce a well-distributed approximation of the Pareto frontier. The progressive method not only addresses the clustering problem that has appeared in the naive ε sweeps but also creates a continuous and varied set of solutions that are not dominated by any other solution. With the extended model that utilizes synthetic but realistic nurse, demand, and preference data, a variety of feasible scheduling policies with obvious trade-offs between cost and employee satisfaction are provided. The Pareto frontier offers intermediate solutions which are able to achieve large increases in preference satisfaction at the expense of only negligible increments in operational costs when compared to the baseline cost-minimal and preference-maximal schedules. The findings emphasize the usefulness of multi-objective decision support in hospital practice and also prove that through the direct representation of staff preferences, it is possible to have even distributions of working time without losing the effectiveness of operations. On the whole, the extension demonstrates that the original "smart hospital" model is enriched and the decision-making process for the administrators is more flexible with the inclusion of multi-objective optimization, thus resulting in the enhancement of both efficiency and health of the staff.

The generalized assignment problem (GAP) is a unique extended form of the Knapsack problem, which is tremendously practical in optimization fields. For instance, resource allocation, sequencing, supply chain management, etc. This paper tackles the GAP in uncertain environment in which the assignment costs and capacity of agents are fuzzy numbers. Two models are presented for this problem and a novel hybrid algorithm is offered using simulated annealing (SA) method and max-min fuzzy in order to obtain near optimal solution. Computational experiments validate the efficiency of proposed method.

Hospital departments are continuously challenged to allocate their limited resources—beds, nursing staff, and operating room (OR) time—among patients with different clinical priorities on the waiting list. This paper presents a solution to this intricate planning problem by constructing a novel Integer Linear Programming (ILP) model to schedule elective patient admissions optimally. The goal is to increase the total priority score of the patients admitted, where the score indicates clinical urgency and waiting time, while at the same time honoring the restrictions on bed capacity, nursing hours, and OR availability through a multi-day planning horizon. The model uses binary decision variables for patient admission as well as pre-processed parameters to map the individual patient resource consumption over the time they are expected to stay in the hospital. We showcase the model's application through a realistic scenario with synthetic data. The outcomes reveal that the suggested framework not only effectively creates an optimum admission timetable but also considerably enhances the use of vital resources when compared to a first-come-first-served standard. The ILP model is not only potent but also clear and just decision-support tool for hospital management, according to the study's findings. By assigning priority to clinical criteria and making patient access more equitable, it provides a data-guided way to improve operational productivity, as well as to decide the trade-offs that come with the limitations of healthcare facilities in terms of resources. Besides, the model is computationally capable of meeting the needs for the inclusion of extra real-world constraints and adjustments.

Modern healthcare's growing intricacy, which is a result of an aging population and increased use of technology, necessitates the use of very advanced operational management. A solution is provided to the issue of hospital optimization through the use of a unified mathematical programming method that combines two main areas, resource allocation and workforce management. We present a new multi-objective Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) framework made especially for the "smart hospital" setting. The model is able to do several things at once; it is able to come up with the best patient admission scheduling, use the beds and operating rooms in the most efficient way possible, and schedule the nurse staff all while being subjected to the real-world constraints of demand fluctuations, staff preferences, skill mixes, and labor regulations. A detailed case study is presented which clearly shows the effectiveness of the model, revealing a 15% boost in resource utilization, a 12% decrease in operational costs, and increased nurse schedule satisfaction, all whilst still being able to offer high standards of patient care coverage. The findings point out the great opportunity of using integrated optimization models to make healthcare delivery systems more efficient, cheaper, and more adaptive, thus giving hospital administrators a strong decision-support tool.

The Quadratic Assignment Problem (QAP) is still considered to be one of the most difficult and widely used models in combinatorial optimization. The layout of healthcare facilities has been its most significant application area since the 1970s, representing a crucial field of study for increasing operational efficiency, patient safety, and staff flow. The QAP context has been continually altered and supplemented to cover the particular intricacies of the healthcare sector. After Elshafei's groundbreaking paper in 1977, the QAP framework was reinvented and extended to the point where it gained acceptance in the healthcare facility location planning area. This review offers a synthesis of the existing literature from 1977 to 2025 and classifies the research into ten different methodological streams: Exact Solution Methods, Classical Heuristics, Metaheuristics, Hybrid Approaches, Robust Optimization, Fuzzy QAP, Stochastic Programming, Multi-Objective QAP, Special Structure Exploitation, and Parallel & Dis-tributed Computing. The critical assessment of the transition of solution procedures and how the techniques for handling uncertainty have been developed shows how the research has progressed from modeling with one deterministic objective to a sophisticated data-driven approach where multiple objectives are characterized as well as the inherent uncertainties of the system. The analysis indicates the integration and hybridization trend—in the case of algorithms, objectives, and data sources is quite strong—pointing out the future lines of research in areas such as real-time adaptive layouts, deep learning integration, and pandemic-responsive design.