Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated temperature changes impacting produce shelf life, and compare it against traditional approaches such as Robust Optimization, Distributionally Robust Optimization, and Stochastic Programming. Additionally, we conduct a series of computational experiments using Internet of Things (IoT) sensor data to evaluate the performance of our proposed model. Our study demonstrates that the proposed adaptive model achieves a higher shelf life, extending it by over 18\% compared to traditional optimization models, by dynamically mitigating temperature deviations through a temperature feedback mechanism. The promising results demonstrate the potential of this approach to improve both the freshness and efficiency of logistics systems an aspect often neglected in previous works.

This paper proposes a novel method for analyzing food retail processes with a focus on reducing food waste. The approach integrates object-centric process mining (OCPM) with stochastic process discovery and analysis. First, a stochastic process in the form of a continuous-time Markov chain is discovered from grocery store sales data. This model is then extended with supply activities. Finally, a what-if analysis is conducted to evaluate how the quantity of products in the store evolves over time. This enables the identification of an optimal balance between customer purchasing behavior and supply strategies, helping to prevent both food waste due to oversupply and product shortages.

Agricultural products are often subject to seasonal fluctuations in production and demand. Predicting and managing inventory levels in response to these variations can be challenging, leading to either excess inventory or stockouts. Additionally, the coordination among stakeholders at various level of food supply chain is not considered in the existing body of literature. To bridge these research gaps, this study focuses on inventory management of agri-food products under demand and lead time uncertainties. By implementing effective inventory replenishment policy results in maximize the overall profit throughout the supply chain. However, the complexity of the problem increases due to these uncertainties and shelf-life of the product, that makes challenging to implement traditional approaches to generate optimal set of solutions. Thus, the current study propose a novel Deep Reinforcement Learning (DRL) algorithm that combines the benefits of both value- and policy-based DRL approaches for inventory optimization under uncertainties. The proposed algorithm can incentivize collaboration among stakeholders by aligning their interests and objectives through shared optimization goal of maximizing profitability along the agri-food supply chain while considering perishability, and uncertainty simultaneously. By selecting optimal order quantities with continuous action space, the proposed algorithm effectively addresses the inventory optimization challenges. To rigorously evaluate this algorithm, the empirical data from fresh agricultural products supply chain inventory is considered. Experimental results corroborate the improved performance of the proposed inventory replenishment policy under stochastic demand patterns and lead time scenarios. The research findings hold managerial implications for policymakers to manage the inventory of agricultural products more effectively under uncertainty.

The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses product quality and its impact on various stakeholders and their communities. Such an approach offers numerous benefits in increasing product quality and eliminating superfluous measurements while appraising and alleviating the broader societal and environmental repercussions. A traceable impact management model (TIMM) provides an impact structure and a reporting mechanism that identifies each stakeholder's role in the total impact of food production and consumption stages. The model aims to increase traceability's utility in understanding the impact of changes on communities affected by food production and consumption, aligning with current and future government requirements, and addressing the needs of communities and consumers. This holistic approach is further supported by an ontological model that forms the logical foundation and a unified terminology. By proposing a holistic and integrated solution across multiple stakeholders, the model emphasizes quality and the extensive impact of championing accountability, sustainability, and responsible practices with global traceability. With these combined efforts, the food supply chain moves toward a global tracking and tracing process that not only ensures product quality but also addresses its impact on a broader scale, fostering accountability, sustainability, and responsible food production and consumption.

Abstract: The demand of the halal food products is increasing rapidly around the world. The consumption of halal food product is just not among the Muslims but also among non-Muslims, due to the purity of the halal food products. However, there are several challenges that are faced by the halal food consumers. The challenges raise a doubt among the halal food consumers about the authenticity of the product being halal. Therefore, a solution that can address these issues and can establish trust between consumers and producers. Blockchain technology can provide a distributed ledger of an immutable record of the information. Artificial intelligence supports developing a solution for pattern identification. The proposed research utilizes blockchain an artificial intelligence-based system for developing a system that ensure the authenticity of the halal food products by providing the traceability related to all the operations and processes of the supply chain and sourcing the raw material. The proposed system has been tested with a local supermarket. The results and tests of the developed solution seemed effective and the testers expressed interest in real-world implementation of the proposed system. Introduction The demand of the halal food and concerns regarding the traceability of halal food are increasing worldwide (Tan et al., 2022). The term'halal' is an Arabic language word. The consumers of the halal food and halal products are Muslims primarily.

Growing mechanical progressions and quick assembling development are impressively affecting the worldwide store network. For instance, computerized reasoning is assuming control over quality control, Internet of Things (IoT) gadgets and robots are observing assembling and upkeep, and more than 1.9 million robots are as of now conveyed in assembling and warehousing universally. Digital ledger has been proclaimed as a state of the art innovation that will further develop the contemporary store network structure by expanding store network trust, productivity, and straightforwardness. Be that as it may, as promising as digital ledger innovation is, ledger isn't a panacea for production network issues. Cryptography keys consist of two keys, Private Key and Public key.

Some people call this Artificial Intelligence (AI), but the reality is this technology will enhance us. So instead of AI, I think we'll augment our intelligence, quoted by Ginni Rometty, CEO (Chief executive officer) of IBM. The business of selling food to customers is being disturbed to a level not since the last pandemic, over 100 years ago. So, it's not true that the crisis accelerated the adoption of technology in the manner that is occurring today with Artificial Intelligence (AI) in the food industry. It is increasingly possible that our food system was ill-prepared (antifragile) for this Covid-19 induced crisis.

The emerging field of artificial intelligence can help countries improve their food security, especially in places like Qatar that import majority of their food products from outside, according to Dr. Tareq Al-Ansari, Assistant Professor at Hamad Bin Khalifa University's College of Science and Engineering. "While Qatar has significantly ramped up its production of vegetables, meat, and dairy products, a large percentage of our food products are still being imported. The availability and stability of food supply are still of particular concern. Therefore, it's important to develop data-driven strategies to secure multiple sources from where food is acquired through robust and diversified supply chains," said Dr. Al-Ansari, whose research focuses on the water-energy-food nexus and sustainable development. "There is a need for informed, insightful, and pre-emptive decision-making processes in the field of food security, and we strongly believe artificial intelligence (AI) can enable this and play an important role for a more sustainable and resilient future in the local food sector."

There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of 7 billion. In fact, the World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that, food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. On the food front, a record $1.71 trillion was spent on food and beverages in 2018 at grocery stores and other retailers and away-from-home meals and snacks in the United States alone. During the same year, 9.7% of Americans' disposable personal income was spent on food -- 5% at home and 4.7% away from home -- a percentage that has remained steady amidst economic changes over the past 20 years. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand and regulations stemming from consumer trends.

How Artificial Intelligence can help with Biosecurity. Around the globe, different parts of the food supply chain are contaminated on a daily bases. Biosecurity, as defined by the Food and Agriculture Organization of the United Nations is the strategic and integrated approach to manage risks in food safety, animal and plant life and health, and biosafety. It relates to policy and regulatory framework that improves food health inside different points in the global food supply chain. China, a country that consumes more pork per capita than any other country after Vietnam, is contending with a deadly case of African Swine Fever.