A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

A Market Analysis on Green Production Lines Penetrating into Original Equipment Manufacturers (OEMs)

Fierce market competition has been big obstacle in the growth process of sustainable/green production industry. Sustainable production lines often come into play by penetrating into traditional Original Equipment Manufacturers (OEMs). This is due to the high setup cost and opportunity risks. In this study, we look into the scenario when traditional Original Equipment Manufacturers (OEMs) introduce green production lines into their production systems. We want to address the questions whether and how the green production lines can survive in the market competitions. Analyses are conducted between ordinary and green production sectors which produce a similar type of product with different materials and techniques. A game theoretic model is formulated to analyze the competition in the market. Sensitivity analyses and numerical examples can provide suggestions to assist policy makers, government, company executives and consumers to make better and rational decisions

Potential competition for biomass between biopower and biofuel under RPS and RFS2

Driven by Renewable Portfolio Standards and Renewable Fuel Standard, biopower generation and biofuel production will increasingly compete for the same biomass resource over the next two decades. We use a linear programming model to study this competition as well as other interactions between the two policies. Our model describes the U.S. renewable energy portfolio by explicitly accounting for all major renewable energy resources, unique resource availability and policy requirements in all 50 states and Washington, DC, and policy deadlines set by all RPS and RFS2 policies within a 2013-2035 modeling horizon. Our modeling results were used to address five important questions regarding interactions between RPS and RFS2 and the impact on U.S. renewable energy portfolio

An Oligopoly Model to Analyze the Market and Social Welfare for Green Manufacturing Industry

As public concerns on sustainable economic development increase, an increasing number of manufactured products have found their environmentally preferable alternatives. In this study, we propose an oligopoly game theoretical model to analyze the competition between the green and ordinary manufacturing sectors. We identify cost efficiency and innovative design as key elements to the survival of green products. We also find that the effectiveness of Pigouvian tax and subsidy policies depend on product characteristics, market structures, as well as targeted results. Our small empirical examples on Corolla vs. Prius and Incandescent lamp vs. Compact fluorescent lamp (CFL) show that our modeling results are more optimistic than real market statistics. We identify pre-equilibrium market dynamics, consumer bias towards green products, and modeling limitations as the main reasons for such differences. We also investigate the market competition and total societal welfare in the presence of tax and subsidy policy intervention. The study results not only provide guidelines and managerial insights for green producers to understand the underlying factors that determine the competitiveness of green products in the market but also benefit policy makers by quantitatively showing the effectiveness of tax and subsidy policies in promoting green products

An Optimization Approach To Assessing the Self-Sustainability Potential of Food Demand in the Midwestern United States

Conventional agriculture faces significant challenges as world population grows, food demand increases, and mobility becomes increasingly constrained. Reducing the distance food needs to travel is an important goal of sustainability and resiliency, particularly in the context of a variety of transportation challenges. In this study, we developed a linear programming optimization method to assess the potential of regions to meet dietary requirements with more localized and diversified agricultural systems. Emphasis is on minimizing the distance between population centers and available cropland, accounting for variations in yield among 40 of the most marketable food crops that can be grown in the Midwestern United States. We also derived two new metrics to guide strategic planning toward more localized systems: the per capita cropland requirement and the regional self-sustainability index. Overall, we conclude that the eight-state study region would require an average of 0.49 acres (0.2 ha) per consumer with an average absolute deviation of 0.09 acres (.04 ha). The self-sustainability index is estimated at 9.3, which indicates that the region has 9.3 times the cropland needed to become self-sustaining. Targeted dietary recommendations could potentially be met within a population-weighted average distance of 13.6 miles (21.9 km)

Anti-ulcerogenic efficacy and mechanisms of edible and natural ingredients in NSAID-induced animal models

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) are a class of the most commonly used medicines and proven to be effective for certain disorders. Some people use NSAIDs on daily basis for preventive purpose. But a variety of severe side effects can be induced by NSAIDs. Studies have shown that edible natural ingredients exhibit preventive benefit of gastric ulcer. This paper reviews the efficacy and safety of edible natural ingredients in preventing the development of gastric ulcer induced by NSAIDs in animal models.Methods: A systematic literature search was conducted on PubMed, using the terms “herbal medicines” and “gastric ulcer”, “herbal medicines” and “peptic ulcer”, “food” and “peptic ulcer”, “food” and “gastric ulcer”, “natural ingredient” and “peptic ulcer”, “natural ingredient” and “gastric ulcer”, “alternative medicine” and “peptic ulcer”, “alternative medicine” and “gastric ulcer”, “complementary medicine” and “peptic ulcer”, “complementary medicine” and “gastric ulcer” in papers published in English between January 1, 1960 and January 31, 2016, resulting in a total of 6146 articles containing these terms. After exclusion of studies not related prevention, not in NSAID model or using non-edible natural ingredients, 54 articles were included in this review.Results: Numerous studies have demonstrated that edible natural ingredients exhibit antiulcerogenic benefit in NSAIDinduced animal models. The mechanisms by which edible, ingredient-induced anti-ulcerogenic effects include stimulation of mucous cell proliferation, antioxidation, inhibition of gastric acid secretion, as well as inhibition of H(+), K(+)- ATPase activities. Utilization of edible, natural ingredients could be a safe, valuable alternative to prevent the development of NSAID-induced gastric ulcer, particularly for the subjects who are long-term users of NSAIDs.Keywords: Food, Gastric ulcer, Prevention, Animal models，Nonsteroidal anti-inflammatory drug

A reinforcement learning approach to resource allocation in genomic selection

Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species. Allocation of resources is a key factor in GS. At each selection cycle, breeders are facing the choice of budget allocation to make crosses and produce the next generation of breeding parents. Inspired by recent advances in reinforcement learning for AI problems, we develop a reinforcement learning-based algorithm to automatically learn to allocate limited resources across different generations of breeding. We mathematically formulate the problem in the framework of Markov Decision Process (MDP) by defining state and action spaces. To avoid the explosion of the state space, an integer linear program is proposed that quantifies the trade-off between resources and time. Finally, we propose a value function approximation method to estimate the action-value function and then develop a greedy policy improvement technique to find the optimal resources. We demonstrate the effectiveness of the proposed method in enhancing genetic gain using a case study with realistic data.This article is published as Moeinizade, Saba, Guiping Hu, and Lizhi Wang. "A reinforcement learning approach to resource allocation in genomic selection." Intelligent Systems with Applications 14 (2022): 200076. DOI: 10.1016/j.iswa.2022.200076. Copyright 2022 The Author(s). Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Posted with permission

Complementarity‐based selection strategy for genomic selection

Genomic selection is a technique that breeders use to select plant or animal individuals to mate and produce new generations of species. The conventional selection method is to select individuals that are either observed or predicted to be the best based on the assumption that parents with better phenotypes will produce better offspring. A major limitation of this method is its focus on the short‐term genetic gains at the cost of genetic diversity and long‐term growth potential. Recently, several new genomic selection methods were proposed to maximize the long‐term potential. Along this research direction, we propose a new method, the complementarity‐based selection strategy (CBS), to improve the tradeoff between short‐term genetic gain and long‐term potential. This approach is inspired by the genetic compatibility mate‐choice mechanism in animals. Our selection method selects the individual with the highest genomic estimated breeding value to emphasize short‐term achievement and then pairs it with the individual that is the most complementary to the one with highest genomic estimated breeding value to emphasize the long‐term growth potential. The CBS method allows favorable alleles to be accounted for within the selection and more of them to be included. We present simulation results that compare the performance of the new method against the state‐of‐the‐art methods in the literature and show that the CBS approach has a great potential to further improve long‐term response in genomic selection

The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

Recent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible

Toward a More Sustainable, Local Food Production System—From a System Modeling Perspective

Localized food production and sourcing is drawing increasing attention due to environmental and health considerations. In this study, we used population, dietary and geographical information to map potential foodsheds with emphasis on minimizing total geographic distribution. We also developed innovative protocols, metrics and optimization methods to analyze the foodshed localization of geographic areas. We used data from Iowa to analyze and validate the optimization model. This study can also be extended to other regions outside of Iowa