Perishable agricultural supply chains face significant uncertainties in production yield, market demand, and availability of resources. This paper presented a multi-period, multi-echelon, and multi-product mathematical model for a perishable agricultural supply chain network comprising farms, cold storage facilities, and wholesale markets. The model determined optimal allocation of cultivation area, harvest timing, duration of storage, and product flow distribution to minimize total supply chain costs, including production, transportation, storage, and import costs. A key innovation is the integration of interval programming to handle uncertainty in production costs, transportation rates, and water availability. Three metaheuristic algorithms including particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) were employed to solve the nondeterministic polynomial (NP)-hard problem. A comprehensive case study of three major perishable crops, i.e., potato, onion, and tomato, across 31 Iranian provinces and 24 candidate cold storage facilities attested to the effectiveness of the model. Results demonstrated that PSO outperformed GA and SA in solution quality by achieving a total cost of 64.14 trillion Rials, with 93.7% of costs attributed to production, 5.2% to transportation, and 1.1% to storage. The model reduced final product prices to one-third of market averages. Sensitivity analyses revealed that production cost was the most sensitive parameter, with a 100% increase triggering economically viable imports. Water availability reduction below 10% of baseline rendered domestic production infeasible. The model achieved 19.9% average water savings (1.062 billion cubic meters annually) through optimized cultivation patterns. The proposed framework provided agricultural policymakers with a quantitative tool for balancing domestic production, investment in storage capacity, and import decisions under uncertainty.

New product selection is a strategic decision problem in which market opportunities must be considered alongside financial requirements, technological capabilities, and production constraints. Existing fuzzy multi-criteria decision-making (MCDM) methods can rank product alternatives under uncertainty, but they do not always maintain consistency between the priority classes assigned to evaluation criteria and their final weights. This study develops a fuzzy decision-analytics approach that links Pareto-based ABC classification with dynamic criterion weighting for strategic new product prioritization. Linguistic assessments from nine experts were represented by triangular fuzzy numbers (TFNs) and combined with quantitative data to evaluate eight candidate products against ten market, economic, technological, and production-related criteria. The criteria were first assigned to ABC classes, after which class-specific scaling factors were calculated to preserve their relative importance within each class while enforcing the required weight order across classes. The resulting weights were then used to rank and classify the candidate products. The procedure maintained the specified priority structure among the criteria. Transport racks obtained the highest overall decision value of 0.825 and were assigned to Class A, followed by excavator buckets with a value of 0.769 and excavator chassis with a value of 0.704. Six products were placed in Class B, while quick couplers ranked last with a value of 0.611 and were assigned to Class C. The results indicate that integrating ABC classification directly into criterion weighting provides a consistent basis for product ranking and priority grouping. The proposed approach supports manufacturing managers in aligning new product decisions with strategic objectives, existing production capabilities, and resource constraints.

This study evaluates whether public-sector health financing and selected governance dimensions are associated with longevity in South Asia. The analysis uses a balanced panel of 168 country-year observations for Bangladesh, India, Maldives, Nepal, Pakistan, and Sri Lanka over 1996–2023. Life expectancy at birth (LE) is modelled against domestic general government health expenditure (HE_GDP), control of corruption (CC), political stability and absence of violence/terrorism (PS), and government effectiveness (GE).Pooled ordinary least squares (OLS), feasible generalized least squares (FGLS), panel-corrected standard errors (PCSEs), and Driscoll–Kraay standard errors are reported to assess the stability of the estimates under different error structures. Diagnostic testing indicates heteroskedasticity and cross-sectional dependence, while the test for first-order serial correlation is not statistically significant. In the FGLS specification, a one-percentage-point increase in government health expenditure as a share of gross domestic product (GDP) is associated with 1.2514 additional years of life expectancy ($p <$ 0.01). CC and political stability also show positive, statistically significant coefficients of 2.9629 and 2.3174, respectively. GE is negative but not statistically significant in the FGLS model (-1.3262, $p$ = 0.131), and its significance is sensitive to the estimator used. Across the robustness specifications, the central pattern remains that health financing, corruption control, and political stability are important correlates of longevity. The findings support a policy approach in which budget expansion is accompanied by institutional accountability and a stable environment for implementation.