Optimizing pig marketing decisions under price fluctuations

Bayesian method is a method that synthesizes the prior information about unknown parameters with sample information, then obtains the posterior information according to Bayesian formula, and then deduces the unknown parameters according to the posterior information. Markov decision process is the optimal decision process of stochastic dynamic system based on Markov process theory. In this paper, we establish a two-stage hierarchical Markov decision process, which simulates the sequential marketing decision under the fluctuation of pigsty price. The decision-making process is based on market information such as pork, piglets and feed prices. In addition, the bayesian method is used to update the information and embed it into the hierarchical Markov decision process, and the optimal strategy under different price fluctuation patterns is analyzed. We also evaluated the value of incorporating price information into the model. In the manufacture of fattening pigs, pig sales refer to a series of selection decisions until the production unit is emptied. The profit of a production unit depends on the price of pork, the cost of raising and the cost of buying piglets. The price fluctuation in the market will affect the profit, and the optimal marketing decision may also change under different price conditions. Most studies have considered pig marketing at constant prices. However, because price fluctuation can not have no effect on marketing decision, it is necessary to consider pig marketing under price fluctuation. In this paper, we establish a two-level hierarchical markov decision-making process that simulates sequential marketing decisions under price fluctuations in pigsty. The decision-making process is based on market information such as pork, piglets and feed prices. In addition, the bayesian method is used to update the information and embed it into the hierarchical markov decision process to analyze the optimal strategy in different price fluctuation modes. we also evaluated the value of incorporating price information into the model.

A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs

This paper presents a decision mechanism based on hierarchical Markov decision processes as a solution for two important problems in multitarget multisensor tracking - distributed data fusion and collaborative sensor management. In application to the distributed data fusion, this paper presents a hierarchical multi-level decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. In application to collaborative sensor management, this paper studies the problem of decentralized cooperative control of a group of unmanned aerial vehicles (UAVs) carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. Simulation examples demonstrate the operation and the performance results.

Electric vehicle (EV) is emerging as an effective choice to reduce carbon emissions in the modern transportation system. However, the large access of EVs brings tremendous passive influence on the stability and performance of power grid operation. Thus, it is valuable to investigate the charging power management method to improve charging experience of EV drivers while maintaining normal bus voltage of the power system. This paper builds the charging power control problem of multiple EVs as a hierarchical Markov decision process (MDP) model and propose a hierarchical multi-objective reinforcement learning method (Hamlet) to obtain the real-time charging power control decisions. Specifically, by treating EVs with the same remaining charging time as the same agent, this paper addresses the dynamic state and action spaces issue and significantly reduce the input dimension of both state and action spaces. Besides, this paper quantifies drivers' anxiety about the battery volume and distributes the comfort reward over the entire charging interval, which overcomes the delayed comfort reward issue. In order to alleviate the influence of multiple objectives on training stability, this paper develops a multi-objective learning method to dynamically adjust the optimization direction. Simulation results on IEEE 33 bus and 69 bus test feeders prove the validity of the proposed method in mitigating voltage fluctuation, minimizing bill payment and maximizing battery comfort.

Markov decision processes (MDPs) have proven useful as models of stochastic planning and decision problems. To try to propose practical implementation of MDPs, hierarchical methods are often used in MDPs or reinforcement learning to delegate the optimization of the total problem to simpler hierarchical sub-problems. The goal of the paper is to propose a generic discrete-event based software Framework allowing to use hierarchical MDPs and reinforcement learning to solve planning or decision problems. The proposed approach has been validated using the "grid world" typical MDP use case.

To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring.

Actor-critic algorithms for hierarchical Markov decision processes

This study investigates the dispatch problem in interregional power grids with uncertain renewable energy sources and loads. A knowledge-transfer-based hierarchical learning optimization method is designed to search for the optimal dispatch strategy. First, the model of various sources, loads, and tie-lines are established according to each components characteristics in the system. The dispatch process of an interregional power grid is correspondingly divided into tie-line dispatch in the upper layer and both generator units and flexible load dispatch in the lower layer. The dispatch optimization problems of both layers are formulated with a hierarchical Markov decision process model. Finally, a learning optimization method is adopted to obtain the dispatch strategy, in which a pre-learning technique is incorporated. A case study is performed to validate the effectiveness of the proposed method, and the simulation results show that the pre-learning technique improves the learning speed and reduces the cost of relearning.

The challenges of food insecurity and hunger in Nigeria have continued to receive attention from experts and the government. To increase both protein intake and food security, the government called for urgent need to increase broiler production at both household and commercial holdings. Primary data obtained in year 2020, from 205 randomly selected broiler farmers was analyzed using descriptive statistics and Tobit regression model. This study assessed small-scale broiler production’s contribution to farmers’ food security in Kaduna state, Nigeria. The result showed that mean age was 40 years and majority of the farmers (89.4%) had household size ranged from 1 – 10 persons. In addition, about 73.8% were members of a cooperative society, about 40.5% of the farmers did not have access to extension service while about 78 % of the farmers have access to credit. About 75.10 percent of broiler farming households were found to be food secure. Of the food insecure households, 6.70 % were marginally food insecure, 4.20 % moderately food insecure and 14.00 % severely food insecure. The regression results showed that coefficients of level of education (0.872), access to credit (0.113), membership of association (0.068), cost of day-old chicks (0.363), cost of feed (0812), cost of labour (-2.211), cost of drugs (-0.263) and income from broiler (0.161) were found to be significant contributing to the food security status of the broiler farming households. Finally, broiler farmers attested to the fact that; lack of constant market (95.8%), high cost of feed (92.7%), high cost of input (88%), and price fluctuations (86.9%) are most severe constrains militating the chances of increasing broiler production. Built on the findings of the study it can be concluded that the proportion of food secure broiler farmers are more than the food insecure broiler farmers in the study area. Education, cost of feed and income from broiler production are variables that significantly influences broiler farmers’ food security status. Therefore, farmers should adopt self-feed formulation using locally available resources that will reduce the cost of feeding and increase their profit margin and thereby their food security status.

The price conduction and interaction in the circulation of agricultural products affect the development of the whole swine industry. In recent years, the imbalance between the conduction and interaction of the price of pork has caused the contradiction between residents’ consumption and pig breeding enterprises. In this paper, Granger causality test, pulse function test and variance decomposition test were conducted on price fluctuations in the processes of production, wholesale and retail based on VAR model, it was found that the price fluctuation in the production, wholesale and retail of pig market in Guangdong obviously presents the characteristic of “demand driving and the tendency of “cost push” is not statistically significant.

An integer linear programming model was developed to establish the optimal replacement strategy for a commercial dairy herd. Monthly revenues and costs for cows were computed based on factors such as milk production, calf and carcass values, feed expenses, replacement heifer costs, insemination charges, and veterinary expenses. The model characterized cows using five parity numbers, two decision options (retain or replace), a 5-year planning horizon, and ten cow categories. It accounted for variations in milk yield, milk prices, calf and carcass values, feed costs, and replacement expenses. The parameters and prices used were tailored to reflect the Holstein-Friesian population in a commercial dairy herd in Egypt. The optimal annual replacement rate was determined to be approximately 30%, with an average productive life of 3.33 years. The maximum annual net revenue, expressed as a yearly annuity over the 5-year planning horizon, was estimated at 1,055 LE per cow. Sensitivity analyses were conducted to assess the impact of variations in average milk yield and the prices of milk, calves, carcasses, feed, and replacement heifers on the optimal replacement decisions. Profitability declined as the replacement rate increased from 10 to 50%. Productive life, replacement rate, and annual net present value were most influenced by changes in average milk yield and the prices of milk, feed, and replacement heifers. In contrast, variations in calf and carcass prices had relatively minor effects on the optimal replacement policy.

Determining the optimum replacement policy for Holstein dairy herds in Iran

A study with the aim of assessing the economic value of fermented liquid pig feed in various levels of tamarind seeds and different protein sources has been carried out from July-August 2022. Liquid feed is formulated according to the needs of the grower phase of pigs. The treatments, Feed A : soybean meal (SM) and Whole tamarind seed (WTS) 0, 10, 20, 30%; Feed B: Meat and bone meal (MBM) and WTS 0, 10, 20, 30%; Feed C: SM and MBM and WTS 0.10,20,30%; Feed D: Fish meal and WTS 0, 10, 20, 30%. The research variables were the price of feed per kg and the total feed costs incurred in rearing grower pigs from weaning until 5 months of rearing reached a slaughter weight of 80 kg. This research is a qualitative research based on the results of the calculation of 4 feed formulations. Data analysis was descriptively by calculating the difference and the percentage of feed costs incurred. The results showed that the decrease in the price of feed per kg and the total cost of feed incurred to reach the slaughter weight of pigs was caused by the use of tamarind seeds which increased up to 30% as well as the price, quality, and type of protein source feed ingredients are used. The conclusion is the use of tamarind seeds in feed with different levels and different protein sources can reduce the price of feed per kg and the cost of feed required to reach slaughter weight.

Economic results of pig-fattening systems vary greatly and depend mainly on prices of pork and feeds, and pig growth performance (e.g. feed efficiency, slaughter weight, lean percentage). Previous studies revealed that feeding and shipping strategies are critical factors in the economic outputs of pig production. However, they failed to consider both strategies and the variability in pig growth performance simultaneously. Consequently, we developed a new approach to improve the profitability of pig farms by estimating the best compromise among feeding costs, animal performance, and shipping constraints. We used an individual-based bioeconomic model that simulates the growth of each pig according to its biological traits (e.g. feed intake and protein deposition potential) as a function of different feeding and shipping strategies. The optimization problem is solved using an evolutionary algorithm (CMA-ES, covariance matrix adaptation evolution strategy) that manages the objective function, which is discontinuous, non-convex, nonlinear, and multimodal. Various case studies were constructed to investigate the behavior of the optimization procedure. Effects of pork price on optimal strategies were investigated using three different price scenarios: low (1.173 €/kg), medium (1.314 €/kg), and high (1.662 €/kg) pork prices. Optimizing only feeding strategies improved the gross margin per pig by 5.0% while optimizing shipping strategies improved the mean gross margin per pig by 4.7%. Optimizing both feeding and shipping strategies improved the gross margin per fattened pig by 10% (2.88 €/pig, with medium pork price (1.314€/kg)) compared to the common practice on farms in France. Pork price had a limited effect on feeding decisions when optimized alone, but a strong impact on shipping decisions. Economic optimization of pig fattening inconsistently affected the environmental impacts. However, increasing pork price improved the optimized mean gross margin per pig but increased all environmental impacts. To our knowledge, this is the first tool able to optimize both feeding and shipping strategies while considering effects of variability in growth potential among a batch of pigs. These features allow consideration of the interaction effect of feeding and shipping strategies on the economic outputs of the batch, and investigation of the trade-off between production cost and technical performance. This tool should interest the pig sector since it can identify the best feeding and shipping strategies depending on the economic context. Further work should consider multiobjective optimization with both economic and environmental objectives.

<p>The objective of the study was to evaluate the impact of feed technology on changing of milk and feed price, and also its impact on efficiency of feed technology in dairy farm. In this study, fifteen lactating dairy cows were divided into 3 groups based on the feed technology. Production parameters measured were milk production and quality for 42 days, while economic parameters were milk and feed price. The highest milk production was produced by feed technology III which was Rp. 11,845 l/herd/day. The highest milk quality was resulted in by feed technology II with quality standard of fat 3.960%, SNF 8.312%, TS 12.272%, and milk density 1.029. Result showed that the lowest was by feed technology III, which was Rp. 1,143.80/l. Income Over Feed Cost (IOFC) showed that the use of feed technology III was more efficient than other feed technologies, which was Rp. 24,664.55/herd/day with the milk price of Rp. 4,207.55/l.<br />Key words: Feed technology, milk production, milk quality, milk price, feed price</p>