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The term “high reliability organization” (HRO) evolved from research countering the claim by Perrow after the 1979 Three Mile Island nuclear plant incident that “accidents happen,” implying that failures were inevitable. Soon after the publication of Perrow's book researchers began questioning his assertion, by observing that some organizations exhibited significantly higher rates of reliability. In 2001, Weick and Sutcliffe converged on five principles, or hallmarks, which became the de facto characteristics most often cited since. Since 2001, HRO theory spread to non‐high‐risk industries and the amount of published literature has more than doubled, including several literature reviews. This led to the questions, “how has HRO theory evolved since the seminal research, how far has it spread from high risk industries, and what is the focus of HRO research?” Seeking to capture the breadth of spread of HRO as well as the dominant areas of research and practice, a comprehensive literature review was performed to capture the most current state of theory and practice. The results show HRO theory within the published literature has not significantly evolved past the original characteristics and hallmarks. Though the theoretical implications have not changed much the practical implications have grown and spread throughout the critical infrastructure sectors. In conclusion, the research team believes that a culture of reliability is the sixth hallmark that works synergistically with certain foundational organizational characteristics and allows mindfulness, which is the distinctive hallmark of HROs, to thrive and grow. This is the fundamental difference between “reliable” and “highly reliable” operations and differentiates HROs from well‐run organizations that are not HROs.

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The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.

The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.