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This research develops an optimization model for growing items in a supply chain with three stages: farmer, processor, and retailer while considering imperfect quality, mortality, shortages with full backordering, and carbon emissions. In the farmer stage, during the growing period, not all articles survive until the end of the period, so a density function of the probability of survival and death of the growing articles is taken into account. Moreover, it is considered imperfect quality in the retailer’s stage because as the supply chain goes down, there exists a greater probability of product defects. Here, the end customer (consumer) can detect poor-quality aspects such as poorly cut, poorly packed, expired products, etc. An inventory model that maximizes the expected total profit is formulated for a single type of growing items with price-dependent polynomial demand. An algorithm is developed to solve the optimization problem generating the optimal solution for order quantity, backordering quantity, selling price, and the number of shipments that maximizes the expected total profit per unit of time, and a numerical example is used to describe the applicability of the proposed inventory model. Finally, a sensitivity analysis has been carried out for all the input parameters of the inventory model, where the effect of each of the parameters on the decision variables is shown to extract some management knowledge. It was found that holding costs in the three stages of the supply chain have a substantial impact on the total profit per unit of time. In addition, as the demand scale parameter increases, the company must raise the selling price, which directly impacts the expected total profit per unit of time. This inventory model has the advantage that it can be applied to any growing item, including animals or plants, so it helps the owners of farms or crops to generate the most significant possible profit with their existing resources.

Mental health issues are prominent among gender minorities; however, research examining these issues among college campuses is limited, and has often overlooked individuals who identify outside of transgender or cisgender identities. Our findings indicated that transgender participants reported the highest rates and frequencies of non-suicidal self injury, suicidal thoughts, and suicide attempt compared to cisgender peers; followed closely by gender-expansive participants. Gender minority participants reported higher rates of family distress and social anxiety. [ABSTRACT FROM AUTHOR]

Foodbank networks provide adequate infrastructure and perform logistics activities to supply food to people in need on a day-to-day basis. However, in the case of a sudden event, such as a natural disaster, they must reconfigure themselves to quickly and fairly satisfy the needs of the affected people, despite the rapid changes in supply and demand, as much as possible. In contrast to most of the studies in the humanitarian logistics literature, which have focused on aid distribution—the downstream part of the supply chain—this paper extends the field of view upstream, explicitly considering supply (or, in the case of foodbanks, donors). To this end, we compare several network design strategies in order to assess the potential benefits of centralized decisions in a context where, in practice, there exists no formal protocol to support bank coordination. We propose a mathematical formulation for the design of such logistics processes, including collection, transshipment, and aid distribution, over a network of foodbanks inspired by the real case of Bancos de Alimentos de México (BAMX). The case considers several categories of food and encompasses restrictions on their mixture to ensure the nutritional quality of the delivered food, distinct from other models in the literature. Finally, we assess the differences in the strategies through the use of effectiveness and efficiency performance metrics.

Inventory models that consider environmental and quality concerns have received some attention in the literature, yet no model developed to date has investigated these features in combination with growing items. Therefore, there is a need to incorporate these three relevant aspects together in a single inventory model to support decisions, compare results, and obtain new knowledge for the complexities of the real world. Moreover, current sustainable inventory management practices aim at mitigating the ecological consequences of an industry while preserving its profitability. The present study aligns with this perspective and introduces an economic order quantity (EOQ) model that considers imperfect quality while also accounting for sustainability principles. More specifically, the model addresses growing items, which have a demand dependent on selling price and the unique ability to grow while being stored in inventory. Additionally, the analysis acknowledges the possibility of classification errors during the inspection process, encompassing both Type-I and Type-II inspection errors. Furthermore, the model permits shortages and ensures that any shortage is completely fulfilled through backorders. The optimization model produces an optimal solution for the proposed model that is derived by optimizing three decision variables: order quantity of newborn items, backordering quantity, and the selling price of perfect items. A numerical example is presented, and the results are discussed. Finally, a sensitivity analysis on variations of parameters such as Type-I and Type-II errors shows that it is advantageous to reduce the percentage of good items that are misclassified as defective (i.e., Type-I error). As there is a direct impact of such errors on sales, it is imperative to address and mitigate this issue. When defective items are mistakenly classified as good Type-II errors, adverse consequences ensue, including a heightened rate of product returns. This, in turn, results in additional costs for the company, such as penalties and diminished customer confidence. Hence, the findings clearly suggest that the presence of Type-I and Type-II errors has a negative effect on the ordering policy and on the total expected profit. Moreover, this work provides a model that can be used with any growing item (including plants), so the decision-maker has the opportunity to analyze a wide variety of scenarios. [ABSTRACT FROM AUTHOR]