Abstract The issue of preserving fresh food during the transportation is one of the long-standing issues for food distributors. On the other hand, the issue of vehicle routing is one of the most important issues in distribution management. Affecting the quality of food is the distribution process, which reduces the distribution time through vehicle routing plays an important role in reducing food spoilage. Therefore, in this study, the problem of vehicle routing in the distribution of perishable food materials is investigated. The time is different so that the distance is minimized and the capacities and time windows are not violated. This research aims to find facilities, identify the size of each cargo, and establish the most efficient vehicle routes to reduce overall costs at three levels in the event of a system failure. Undoubtedly, the traveling salesman issue is one of the most famous issues in the field of artificial intelligence.

Abstract Vegetable oils (VOs) come in a wide range of flavors and trademarks. VOs are very similar in appearance, flavor, and taste, and it's frequently difficult to tell them from just by looking at them. Approaches for classifying these oils are sometimes expensive and time-intensive, and they frequently include analytical chemical techniques as well as mathematical algorithms like as Artificial Neural Networks (ANNs), Properties of Partial Least Squares (PLS), Principal Components Regression (PCR), and Principal Component Analysis (PCA) to enhance their effectiveness. Because of the large range of goods available, more productive techniques for qualifying, characterizing, and classifying these substances are required, as the ultimate cost should indicate the quality of the commodity that reaches the user. This study provides a technique for classifying VOs such as different manufacturers' soybean, corn, sunflower, and canola. This method utilized a Charge-Coupled Device (CCD) array sensor, a light emission diode, and a straightforward mathematical approach to capture the generated fluorescence spectrum (FS) in diluted oil. The spectrum classifications are performed using an ANN with three layers, each having four neurons. The approach can categorize VO and enables rapid network training with a 72% success rate utilizing only a few mathematical changes in the spectra data.

Abstract The development of the food industry, along with the protection of water and energy resources, is crucial for sustainable economic growth and human well-being. Water, energy and food nexus approaches can help reduce food waste and other resources by adopting policies and regulations based on comprehensive information and Nexus that promote the use of more efficient production technologies in terms of water and energy consumption against food waste. This paper aims to identify the potential of integrated management of food industry, water and energy in Sulaimaniyah. In this paper, the WEAP planning system, as well as Excel software, have been used. Also, five scenarios were proposed that predicted the level of food demand and shortage of water and energy resources from 2021 to 2025. Considering the simultaneous development of food industry and agriculture, scenario 5 was selected as the best scenario. In this scenario, simultaneous management of water and energy demand is also considered to increase food production. In scenario 5, the amount of water supply is 94.85%, which indicates that it is more effective than in scenarios 3 and 4. Also, in scenario 5, with the increase in surface water use and full use of electric pumps instead of diesel, the amount of energy required to pump water from the aquifer is 55% and 49% less than in scenarios 3 and 4 respectively.