A review of Multi-Agent Simulation Models in Agriculture

Multi-Agent Simulation (MAS) models are intended to capture emergent properties of complex systems that are not amenable to equilibrium analysis. They are beginning to see some use for analysing agricultural systems. The paper reports on work in progress to create a MAS for specific sectors in New Zealand agriculture. One part of the paper focuses on options for modelling land and other resources such as water, labour and capital in this model, as well as markets for exchanging resources and commodities. A second part considers options for modelling agent heterogeneity, especially risk preferences of farmers, and the impacts on decision-making. The final section outlines the MAS that the authors will be constructing over the next few years and the types of research questions that the model will help investigate.multi-agent simulation models, modelling, agent-based model, cellular automata, decision-making, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

A cloud driven dynamic pricing system for retail companies

This project develops a dynamic pricing framework over a cloud based architecture, being scalable and highly configurable, considering the great cardinality of the solution in terms of the analytic models to build and apply. This architecture was defined using AWS and Terraform, ensuring an easy deployment agnostic to the client's infrastructure. The dynamic optimization of the prices is achieved by combining the training of a sales prediction model and the execution of a discount combination optimizer. The framework tries to be as general as possible in order to be easily adaptable to any given client. We provide general interfaces that can be reimplemented if the default implementations are not suitable for a given project. We performed simulations with data from a real client from the fashion retail sector, and the results obtained were promising, suggesting an improvement in the company's revenue

Forecasting etfs- price movements using convolutional neural networks - methodology and comparison of industries - focus on industrials etf

The aim of this paper is to achieve two goals. Firstly, build and apply a convolutional neural network to make predictions on historical data of the Vanguard Industrials ETF (VIS) in the form of Buy, Hold and Sell signals. Secondly, making comparisons among different indus triesin order to derive potential performance deviations. By using three image encoding tech niques and a randomly generated model for comparison purposes, some promising results have been achieved. Nevertheless, several classic strategies and the market performance could not be beaten, mainly because model predictions for Buy and Sell signals showed weaknesses

Objectives, stimulus and feedback in signal control of road traffic

This article identifies the prospective role of a range of intelligent transport systems technologies for the signal control of road traffic. We discuss signal control within the context of traffic management and control in urban road networks and then present a control-theoretic formulation for it that distinguishes the various roles of detector data, objectives of optimization, and control feedback. By reference to this, we discuss the importance of different kinds of variability in traffic flows and review the state of knowledge in respect of control in the presence of different combinations of them. In light of this formulation and review, we identify a range of important possibilities for contributions to traffic management and control through traffic measurement and detection technology, and contemporary flexible optimization techniques that use various kinds of automated learning

Lost in optimisation of water distribution systems? A literature review of system operation

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified

Application of Machine Learning: An Analysis of Asian Options Pricing Using Neural Netwoprk

Pricing Asian Option is imperative to researchers, analysts, traders and any other related experts involved in the option trading markets and the academic field. Not only trading highly affected by the accuracy of the price of Asian options but also portfolios that involve hedging of commodity. Several attempts have been made to model the Asian option prices with closed-form over the past twenty years such as the Kemna-Vorst Model and Levy Approximation. Although today the two closed-form models are still widely used, their accuracy and reliability are called into question. The reason is simple; the Kemna-Vorst model is derived with an assumption of geometric mean of the stocks. In practice, Average Priced Options are mostly arithmetic and thus always have a volatility high than the volatility of a geometric mean making the Asian options always underpriced. On the other hand, the Levy Approximation using Monte Carlo Simulation as a benchmark, do not perform well when the product of the sigma (volatility) and square root maturity of the underlying is larger than 0.2. When the maturity of the option enlarges, the performance of the Levy Approximation largely deteriorates. If the closed-form models could be improved, higher frequency trading of Asian option will become possible. Moreover, building neural networks for different contracts of Asian Options allows reuse of computed prices and large-scale portfolio management that involves many contracts. In this thesis, we use Neural Network to fill the gap between the price of a closed-form model and that of an Asian option. The significance of this method answers two interesting questions. First, could an Asian option trader with a systematic behavior in pricing learned from previous quotes improve his pricing or trading performance in the future? Second, will a training set of previous data help to improve the performance of a financial model? We perform two simulation experiments and show that the performance of the closed-form model is significantly improved. Moreover, we extend the learning process to real data quote. The use of Neural Network highly improves the accuracy of the traditional closed-form model. The model’s original price is not so much accurate as what we estimate using Neural network and could not capture the high volatility effectively; still, it provides a relative reasonable fit to the problem(Especially the Levy Model). The analysis shows that the Neural Network Algorithms we used affect the results significantly.Computer Scienc

Low-carbon multi-objective location-routing in supply chain network design

Traditional supply chain modelling tends to focus on singular objectives, with a predominant focus on cost. Within this discipline location-routing problems are one of the most researched categories in recent years. This study extends this paradigm to consider the multi-objective of cost and environmental impact in the form of carbon emissions. The focus of this study is on the design of a low-cost low-carbon structure for the demand side of supply chain networks. This research has developed two-layer and three-layer multi-objective 0-1 mixedinteger AHP-integrated location-routing models. Disparate multi-objective Genetic Algorithm, Particle Swarm, and Simulated Annealing-based optimisers are used to execute these developed models. The main execution platform used is modeFRONTIER®, a multi-objective optimisation and design environment. The main contributions from this research are 1) the modelling extension to include low carbon emissions; costs; demand as an objective function component; and the inclusion of the decision makers’ priority as a green constraint, 2)with regard to implementing these specific NP-hard models, a DoE-guided solution approach is used. Various heuristics/meta-heuristics are adopted and compared in terms of their efficiency, with the three-layer model being solved in two phases, 3) both sets of developed models are applied to the demand side of a dairy supply chain in Ireland

The integration of pumped hydro storage systems into PV microgrids in rural areas

Photovoltaic (PV) systems are popular in rural areas because they provide low cost and clean electricity for homes and irrigation systems. The primary challenge of PV systems is their intermittent nature. The typical solution is storing energy in batteries; however, they are expensive and possess a short lifespan. This research proposes a new type of pumped hydro storage (PHS) which can be implemented as an alternative to batteries. The components of the system are modelled to consider losses of the system accurately. The mathematic model developed in this project assists the management system to make more efficient decisions. The proposed storage is integrated into a farmhouse that has a PV pumping system where economic aspects of implementing the proposed storage is investigated. The integration of the proposed PHS into a microgrid needs a management system to make this system efficient and 3 cost-effective. This research proposes a multi-stage management system to schedule and control the microgrid components for optimal integration of the PHS. The designed management system is able to manage the pump, turbine, and irrigation time on real-time taking into account both present and future conditions of the microgrid. This study investigates the technical aspects of the proposed system. The PHS and the management system are tested experimentally in a setup installed at smart energy laboratory at Edith Cowan university. Data used in this project are real data collected in the laboratory in order to have a realistic analysis. Economic analysis is done in different sizes with different conditions. Results indicate that the proposed system has a short payback period and a large lifetime benefit, featuring as a cost-effective and sustainable energy storage system for use in rural areas. Video abstract: https://youtu.be/VuyEvHRY7W

Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented