Agri-food distribution optimization using modified simulated annealing algorithm considering stochastic market demand

In recent years, the total loss of agricultural fresh product distribution has increased from 20% to 60% of the total amount of harvested products due to their fixed shelf-life time. Consequently, it is essential to select a logistics distribution path that is reasonable for the transportation of fresh agricultural products. To minimize the loss in the distribution of agricultural products in logistics, this study developed an optimization model for agri-food logistic distribution that takes into account the uncertainty of market demand. A novel algorithm called modified simulated annealing (mSA) is introduced to solve a problem with multiple objectives that involves randomness. As a result, the proposed mSA successfully optimizes the availability of the right quantity, quality, and supply chain net profit. The effectiveness of the proposed solution methods is assessed by comparing them with the current state-of-the-art techniques. The findings confirm the effectiveness of the proposed mSA algorithm in tackling the problem across various dimensions. The mSA algorithm led to a decrease in the overall cost of distribution, surpassing the results achieved by SA algorithms. Additionally, the data gathered from the avocado distribution network in the Ethiopian market was used to test the validity of the suggested model. The results showed that as transportation time increased, the quality deterioration rate also increased.