Research Article
Stochastic Optimization for Job Generation in Angola’s Crustacean Industry to 2050
Alcides Romualdo Neto Simbo*
Issue:
Volume 14, Issue 6, December 2025
Pages:
250-266
Received:
17 September 2025
Accepted:
4 October 2025
Published:
12 November 2025
Abstract: Unemployment still affecting families of a country with potential resources and the implementation of government policies have been facing uncertainties, constraints and difficulties for providing jobs. This paper deals on generating new jobs in Angolan's Crustacean industry business through 2050. How stochastic optimization can be applied to create more jobs? Simulation method of forecasting, tree-stage stochastic optimization model considering 10 notable scenarios and belief modeling strategy for managing risks were applied involving two fishery cooperatives representatives in the probabilities assignment to each demand and availability scenarios in stage 2 and stage 3, resulting in expected revenues around 6,248,283,563.47 Kz and 259 new jobs annually in fishing. These results provides 9% more than the business's earnings, expecting to achieve 3,367 new jobs by 2037 and 6,734 new jobs to 2050. This could lead to the sale of 2,158.8 to 4,035.9 tons of crustaceans (striped grant, shrimp, crab, prawn, and lobster) abundant on the coasts of the provinces of Bengo, Benguela, Cabinda, Cuanza Sul, Icólio Bengo, Luanda, Namibe, and Zaire. To this end, mathematical algorithm were also developed for showing the precise, sequential and logical instructions to follow for helping government and decision-makers to achieve the objectives of unemployment rates reduction in Angola.
Abstract: Unemployment still affecting families of a country with potential resources and the implementation of government policies have been facing uncertainties, constraints and difficulties for providing jobs. This paper deals on generating new jobs in Angolan's Crustacean industry business through 2050. How stochastic optimization can be applied to creat...
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Research Article
Recursive Estimation of the Intensity Function of the Non-homogeneous Poisson Process
Marcel Sihintoe Badiane*,
Siba Kalivogui,
Bakary D Coulibaly,
Aguemon Wiwegnon Uriel-Longin
Issue:
Volume 14, Issue 6, December 2025
Pages:
267-276
Received:
7 October 2025
Accepted:
25 October 2025
Published:
19 December 2025
DOI:
10.11648/j.ajtas.20251406.12
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Views:
Abstract: This study focuses on the recursive nonparametric estimation of the intensity function associated with a nonhomogeneous Poisson process. Accurately estimating the intensity function is crucial for understanding the dynamics of events in fields such as finance, neuroscience, and environmental monitoring. While traditional nonparametric estimators are theoretically robust, their reliance on the entire dataset for every update makes them impractical for real-time applications. To overcome this limitation, we introduce a recursive estimator that supports efficient, online updates as new data becomes available. This approach significantly lowers computational overhead while maintaining strong statistical reliability. We thoroughly analyze the asymptotic behavior of the proposed estimator, paying particular attention to the Asymptotic Mean Integrated Squared Error (AMISE), a key measure of estimation accuracy. Additionally, we compare the performance of our recursive estimator with Cucala’s non-recursive method. The results reveal that our approach achieves equivalent or superior accuracy in terms of AMISE, particularly in large-sample scenarios. A computational performance comparison further underscores the advantages of the proposed method, demonstrating its substantial reduction in execution time and its suitability for applications requiring rapid processing.
Abstract: This study focuses on the recursive nonparametric estimation of the intensity function associated with a nonhomogeneous Poisson process. Accurately estimating the intensity function is crucial for understanding the dynamics of events in fields such as finance, neuroscience, and environmental monitoring. While traditional nonparametric estimators ar...
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