Purpose: This study aimed to evaluate the relationship between agricultural exports and food security in Zimbabwe from 1990 to 2023. Research Methodology: A time-series analysis was conducted using the Ordinary Least Squares (OLS) method and autoregressive distributed lag (ARDL) bounds testing approach to examine both short-run and long-run dynamics. Diagnostic tests, such as ADF, multicollinearity, heteroskedasticity, and serial correlation, were performed to ensure robustness.   Results: The findings revealed that agricultural exports had a positive and statistically significant effect on food security. A 1% increase in agricultural exports leads to a 0.007% increase in food security. Other variables, such as rainfall, capital expenditure, and consumption expenditure, positively influenced food security, whereas rural and urban population growth negatively impacted it. Conclusions: Agricultural exports play a critical role in enhancing food security in Zimbabwe by generating foreign currency and promoting investment in the agricultural sector. However, demographic pressures and climate-related risks remain significant challenges. Limitations: The study is limited by data availability, as annual data may overlook short-term changes. Additionally, some potentially influential variables were excluded because of data constraints. Contribution: This study contributes empirical evidence to policy debates on balancing export-oriented agriculture with domestic food needs. It offers actionable insights for government ministries and institutions involved in agricultural planning, advocating greater support for capital investment, infrastructure, and farmer education to ensure sustainable food security outcomes.

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.

Purpose: This study aims to explore trends in guest satisfaction at the Courtyard by Marriott Bali Nusa Dua Resort by analyzing online reviews from TripAdvisor, with the objective of enhancing service quality. Methods: The research follows a systematic methodology that begins with scraping guest reviews from TripAdvisor, ensuring the collection of relevant data. Subsequently, a thorough data cleaning and preprocessing process is undertaken to guarantee high-quality data. The study then utilizes time series analysis, specifically the ARIMA model, to analyze the evolving patterns of guest satisfaction over time. Results/findings: The findings show that the majority of guest feedback is positive, indicating general satisfaction with the hotel. The ARIMA model reveals that guest satisfaction is highly influenced by previous satisfaction levels, suggesting a trend where past experiences strongly impact future perceptions. Conclusions: These results provide valuable insights into the key drivers of guest satisfaction, offering actionable recommendations for hotel management. By understanding the dynamic factors that influence guest experiences, management can improve service quality, respond more effectively to unexpected situations, and remain competitive in the market. Limitations: The ARIMA model does not account for external factors, such as holiday seasons or marketing changes, nor does it analyze the specific content of reviews or differentiate between guest segments. Additionally, comparisons with competitors can provide a broader strategic context for a more comprehensive understanding. Contribution: The combination of sentiment analysis and time series forecasting in this study offers a unique contribution, enabling data-driven decisions that support continuous service improvement and customer satisfaction.