Accurately modeling claims data and determining appropriate insurance premiums are vital responsibilities for non-life insurance firms. This article presents...

In this paper, we introduce a new continuous distribution mixing exponential and gamma distributions, called new Sushila distribution. We derive some properties of the distribution include: probability density function, cumulative distribution function, expected value, moments about the origin, coefficient of variation (C.V.), coefficient of skewness, coefficient of kurtosis, moment generating function, and reliability measures. The distribution includes, a special cases, the Sushila distribution as a particular case p=1/2 (θ = 1). The hazard rate function exhibits increasing. The parameter estimations as the moment estimation (ME), the maximum likelihood estimation (MLE), nonlinear least squares methods, and genetic algorithm (GA) are proposed. The application is presented to show that model to fit for waiting time and survival time data. Numerical results compare ME, MLE, weighted least squares (WLS), unweighted least squares (UWLS), and GA. The results conclude that GA method is better performance than the others for iterative methods. Although, ME is not the best estimate, ME is a fast estimate, because it is not an iterative method. Moreover, The proposed distribution has been compared with Lindley and Sushila distributions to a waiting time data set. The result shows that the proposed distribution is performing better than Lindley and Sushila distribution.

Cold chain logistics for perishable goods faces increasing challenges in balancing product quality and cost efficiency. This study proposes a mixed integer linear programming (MILP) model that jointly optimizes transportation and inventory decisions in temperature-controlled supply chains by incorporating both transportation and perishability-related holding costs within a multi-node distribution network. A real-world case study based on a ten-node cold chain system in Thailand is used to validate the model. The results indicate that the proposed approach effectively determines routing structures, shipment quantities, and vehicle utilization while accounting for product deterioration. Compared with experience-based planning, the proposed model reduces total logistics cost by 8.02%, primarily through improved transportation efficiency. These findings demonstrate the importance of integrating routing decisions with perishability considerations and highlight the model’s potential as a practical decision-support tool for cold chain logistics operations.