American Journal of Business and Operations Research

This study explores the economic implications of international migration dynamics in Central Asia over the past two decades. It provides an advanced analysis of migration patterns, identifying key destination trends and the economic, demographic, and political factors shaping these movements. Employing 24 years of panel data and econometric analysis using the OLS model, the research examines how variables such as GDP per capita, unemployment rates, inflation, and population growth influence migration flows across the region. Additionally, it assesses the impact of political stability on migration decisions and highlights the role of international organizations and regional cooperation frameworks in managing migration for economic development. The findings offer insights for policymakers aiming to harness migration as a driver of sustainable economic growth and regional integration.

The palm orchards resource is one of the most important economic sources of advantage for the southern and central regions of Iraq. Trees and products obtained from their processing are used in many areas of human life, for example, furniture of various specifications, household and interior items, construction, chemistry, etc. Orchards themselves are also important ecosystems and a necessary element when creating safe living conditions for humans (processing carbon dioxide into oxygen) and plants and animals living in them. Irrational clearing of orchards and trees can not only lead to disruptions in the economic systems of enterprises, but also have a disastrous impact on the health of people living in these territories. In this regard, the purpose of this article is to formalize the system of interaction between objects of the horticultural industry and arable land, which in turn will make it possible to create an effective and rational model of logging at the level of an individual territorial entity. The relationships developed by the authors can be used for a simulation modeling system based on one of the active approaches in development - agent-based modeling. To solve the mentioned problem, general scientific methods such as generalization, systematization, logical-inductive deductions, analysis, scientific synthesis method, etc. were used to determine the specific numerical characteristics of the simulated objects, their parameters and variables. Methods and tools of mathematical statistics were used, tested because of regional statistics of the orchard industry in the Basra region. The results obtained will be useful for forming computer models of orchard management at the regional level, which in turn will allow one to calculate the optimal number of participants in a particular economic direction, and will show the dynamics in which the orchard area will be restored after the removal of orchards. The work carried out by the authors of the study may be of interest to various specialists of the executive authorities involved in the regulation of laws and rights in the forestry sector; commercial enterprises trying to rationalize their activities, while achieving maximum economic returns and efficiency and causing minimal harm to the environment; as well as subject specialists working in the direction of modeling effective simulation systems.

By effectively including diffusion into the framework, this study further illustrates whether Optimal Control Theory may be used to identify and address the control of prices issue of technology items. There is a three-stage paradigm that it uses to describe the procedure of adoption process: consciousness, inspiration, and adopting itself. The process is described by the diffusion logistic functions; furthermore, the model takes into account price fluctuations’ sensitivity. The selling price is a choice variable constrained such that the total profit over the relevant planning horizon is optimised. In this paper, the Hamiltonian function is used in obtaining necessary optimality conditions with spectral learning effects and Costate equations complemented by the adoption rates by use of Pontryagin’s Maximum Principle. As the problem is formulated as the continuous optimization problem, it is discretized for its practical applications, and the model is solved with the help of LINGO 15.0 software. The data used to validate the model implemented was derived from historical sales records of the electronics and semiconductor industries to obtain a measure of realism. Analysed sensitivity studies show how variations in adoption parameters including the price elasticity and customer attrition affect adoption rates and profitability. As such, the study offers managerial implications for the management of private sector schemes to focus on the application of dynamic pricing strategies as the optimal balance between consumers’ perceived value and firm revenues. It provides managers with strong tools for the implementation of adoption into a new generation of technology-enabled markets, maximization of revenues, and sustaining of competitive advantage. Outperforming all analysed models, the suggested technique employing Optimal Control Theory obtains an accuracy of 96%. This proves that the suggested strategy is the best at forecasting when a product will be adopted.

This research explores the great potential of blockchain based smart contracts in Iraq’s financial and banking sector. It looks into how this technology can improve financial operations by automating transactions and reducing operational cost, increasing transparency, and reducing intermediaries. The research also tackles the challenges of adoption such as lack of digital infrastructure and lack of legal frameworks and cybersecurity risks. The findings show that smart contracts can lead to higher operational efficiency and more strategic flexibility for financial institutions in Iraq. Therefore, the research recommends developing digital infrastructure and establish comprehensive regulatory frameworks to support smart contracts and digital transformation in the financial and banking sector according to international standards.

This paper presents a multi-server Markovian queuing-inventory system (MQIS) that incorporates attractionretention (AR) mechanisms for impatient customers and models catastrophic inventory losses within a warehouse setting. The system consists of C identical servers, a limited waiting area, and a storage capacity of Q items. Periodic disruptions may destroy all inventory in the system, compelling waiting customers either to remain until stock is replenished or to exit the system. A subset of servers may take joint vacations when no customers are waiting. To analyze this queuing-inventory system (QIS), we derive balance equations using a three-dimensional continuous-time Markov chain framework, solving for steady-state solutions through a recursive method. We then derive performance metrics and identify special-case queuing-inventory models within the broader system. A cost-loss model is formulated to optimize the service rate and server vacation strategies, minimizing overall costs. A genetic algorithm is employed to conduct a cost analysis. We collected primary data from the Ethio Telecom district head office in Arba Minch, Ethiopia to validate our theoretical findings. The empirical analysis serves a dual purpose: to investigate performance measure sensitivity to parameter variations and to discuss an optimization problem aimed at minimizing expected total cost (ETC) while assessing the impacts of AR mechanisms and catastrophic events on ETC.

The suggested models for the spread of technical breakthroughs make use of a phase structure to illustrate the steps involved in becoming familiar with the problem and making a choice. For it to portray genuine adopting conduct, a time-lag factor is included into the dispersion process. Depicts a two-step dissemination process by taking into account the reliance of adopting on the informed group of potential purchasers. Assuming that a prospective customer first becomes intrigued by an upcoming the item's availability and then accepts the novel idea at an ulterior point, a method of analysis for sales functions that incorporates time delay is proposed. The efficient propagation method for invention is shown using the various lag factors. Applying nonlinear regression modelling to worldwide shipping data of Acer PCs and Samsung smartphones experimentally validates the suggested models for mathematics. Several comparison models are used to evaluate the predicting abilities of the suggested models. By integrating a distributed time delay function into the implementation manage, a theoretical intergenerational diffusion model is created. To measure how long it takes for innovation to be eventually accepted, the distributed time lag function that follows the Erlang distributions is used. This framework incorporates switch and substituting, two forms of pragmatist shift behaviour. Using real shipping data of LCD (Liquid Crystal Display) computer monitors from consecutive generations, the predicted effectiveness of the suggested methods is examined and contrasted with well-established research. Here is the total accuracy of the approaches that have been proposed: When contrasted with more conventional models, MGDM 1 achieves a 99.33% accuracy rate, MGDM 2 a 99.81% rate, and MGDM 3 a 99.91% accuracy rate.

Determining web services according to Quality of Service (QoS) restrictions is the topic of discussion in this section. Decision tree classifiers are used to accomplish this classification. Because of the ever-changing and expanding nature of online services, it is necessary to accurately categorize them in order to make choosing them more efficient for consumers. It makes use of decision tree techniques, more especially the C5.0 classifier, this is an advancement over older approaches such as the C4.5 classifiers. It incorporates characteristics like as noisy handling, incomplete information administration, and improved decision-making correctness. Web services are classified into four distinct groups: Outstanding, Good, Average, and Poor. These classifications are determined by QoS metrics that include time to response, accessibility, performance, dependability, and success rate. The choice of features is accomplished utilizing an evolutionary algorithm with a wrapper technique with the goal to maximize the effectiveness of this category. This method minimizes the number of repetitive features and improves the method of classification for the purpose of optimization. The resilience and predicted reliability of the algorithm are ensured by additional approaches like as cross-validation and error reduction. These approaches also address difficulties such as overfitting and redundant characteristics. The construction of integrated web services for complicated corporate operations is a particularly valuable use of this technology, which also considerably improves the procedure for making choices for identifying services and consumption. Service 7 stands out with an impressive 98% performance, while Service 6 and Service 3 are also among the top-performing services. Compared to the others, Service 1, Service 2, Service 5, and Service 4 all exhibit comparatively poor results.