Effects of Bait on Male White-Tailed Deer Resource Selection

Bait is often used to increase wildlife harvest susceptibility, enhance viewing opportunities, and survey wildlife populations. The effects of baiting depend on how bait influences space use and resource selection at multiple spatial scales. Although telemetry studies allow for inferences about resource selection within home ranges (third-order selection), they provide limited information about spatial variation in density, which is the result of second-order selection. Recent advances in spatial capture-recapture (SCR) techniques allow exploration of second- and third-order selection simultaneously using non-invasive methods such as camera traps. Our objectives were to describe how short-term baiting affects white-tailed deer (Odocoileus virginianus) behavior and distribution. We fit SCR models to camera data from baited and unbaited locations in southwestern Georgia to assess the effects of short-term baiting on second- and third-order selection of deer during summer and winter surveys. We found little evidence of second-order selection during late summer or early winter surveys when camera surveys using bait are typically conducted. However, we found evidence for third-order selection, indicating that resource selection within home ranges is affected. Concentrations in space use resulting from baiting may enhance disease transmission, change harvest susceptibility, and potentially bias the outcome of camera surveys using bait.

The Order Selection Strategy of Polluting OEMs under Environmental Regulations

Under environmental regulations, the government restricts the economic activities of polluting OEMs (Original Equipment Manufacturers) in order to improve ecological and economic efficiency. The most direct measure is to limit the production capacity of the companies. Under the condition of limited capacity, the order selection strategy of OEMs will be the direct determinant of the company’s own profits. In the foundry market, there are many low-profit orders, while the number of high-profit orders is limited and uncertain. Companies who choose to wait for high-profit orders must bear the waiting costs and the risk of losing a certain profit. Therefore, it is of great significance for the long-term development of the company to select orders to obtain the best profit under the condition of limited production capacity. This paper takes polluting OEMs as the research object and studies the optimal order selection problems of companies under environmental regulations by establishing order selection decision models for different foundry cycles under the condition of limited production capacity. The study found that in the single foundry cycle, there will be an optimal waiting-time threshold for high-profit orders. Based on this optimal waiting-time threshold, the corresponding order selection strategy can be effectively formulated. However, in the multi-foundation cycle, since the optimal waiting-time threshold of high-profit orders is affected by the long-term average profit, the company’s optimal order selection strategy is based on the long-term average profit maximization.

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.