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Both natural turfgrass and synthetic turf fields have distinct advantages and disadvantages and present unique challenges. The challenges evolve over time because of climate change, players' ever-changing needs, and the development of technologies. It is imperative to identify these challenges and devise effective solutions to overcome them. We conducted a survey of 97 administrators and managers from various organizations in the United States who were responsible for managing community sports fields. Our findings identified budget constraints as the biggest challenge for natural turfgrass field management, followed by issues related to use/scheduling and weather/climate. For synthetic turf field management, the top three challenges included budget constraints, use/scheduling, and other challenges (mainly safety issues). Additionally, administrators and managers consistently indicated increased funding as a solution for addressing challenges of both natural turfgrass and synthetic turf field management. We discuss the implications of these findings and provide potential ways to address these challenges. [ABSTRACT FROM AUTHOR]

Abstract Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge’s performance is more substantial.

Consumers encounter costs and inconvenience in the purchase and consumption of goods. Time, effort, training, and expertise, also referred to as access costs, are examples of factors that constrain the ability of consumers to derive utility from offerings beyond the price of a good. Economies of scope exist when household production of these factors share commonalities among choice alternatives, making it more likely that some products would be purchased and consumed in conjunction with others. For example, consumers planning out-of-town travel might consider visiting multiple tourist attractions during their trip, and course offerings might appeal more to students who are already in the mindset of taking courses. We develop a model with economies of scope that captures a leveraging effect where the purchase of one good makes the purchase of other goods more likely. Our model offers an alternative explanation of complementary purchases that are driven by cost reductions rather than utility enhancements. The model is applied to four conjoint datasets where we find that economies of scope and access costs play an important role in consumer purchases and have high economic value relative to the role played by price. This paper was accepted by Eric Anderson, marketing. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4841. [ABSTRACT FROM AUTHOR]

Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multi-objective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model's performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions. [ABSTRACT FROM AUTHOR]

To investigate the effects of various government subsidies on the decision of agricultural products supply chain under adverse weather, this paper depicts the random profit function of adverse weather affecting the output of fresh agricultural products. Then, both the centralised and decentralised decision-making models are set with various government subsidies, i.e. no subsidy, output subsidy, procurement subsidy, one-time subsidy to company and one-time subsidy to farmer. Through comparative analysis and numerical analysis, we explore the effects of adverse weather and different government subsidies on the supply chain decisions including planting effort, procurement price and retail price. [ABSTRACT FROM AUTHOR]

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Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge's performance is more substantial., (© 2024. The Author(s).)

The theory of inverse location involves modifying parameters in such a way that the total cost is minimized and one/several prespecified facilities become optimal based on these perturbed parameters. When the modifying parameters are grouped into sets, with each group's cost measured under the rectilinear norm and the overall cost measured under the Chebyshev norm, the resulting problem is known as the max-sum inverse location problem. This paper addresses the max-sum inverse median location problem on trees with a budget constraint, where the objective is to modify the vertex weights so that a specified vertex becomes a 1-median, while minimizing the max-sum objective within the available budget. To solve this problem, a uni-variable optimization problem is first induced, where the objective function for each specified value of the variable can be obtained through a continuous knapsack problem. Leveraging the monotonicity of the cost function, a combinatorial algorithm is developed, which solves the problem in O (n log ⁡ n) time, where n denotes the number of vertices present in the tree. • We address the max-sum inverse median location problem on trees. • We reformulate the problem as a univariate optimization problem. • The objective of the induced optimization problem is a piecewise linear monotone function. • A combinatorial algorithm is developed to solve the problem in O (n log ⁡ n) time. [ABSTRACT FROM AUTHOR]