BUFFER STOCK MODEL FOR STABILIZING PRICE WITH CONSIDERING THE EXPECTATION STAKEHOLDERS IN THE STAPLE-FOOD DISTRIBUTION SYSTEM

The extremely different supplies between the harvest season and the planting season are one of serious problem in the staple-food distribution system. In free-market mechanism, this extreme difference will trigger price-volatility and shortage of staple-food. This situation causes opportunity-losses for the stakeholders (producer, consumer, agent and government) in the staple-food distribution system. The government has got incurred losses because the government cannot achieve food-security for the households. The government has several price stabilization policies; one of them is market intervention policy by using buffer stock schemes to stabilize price and to reduce losses for the stakeholders. The objective of this research is to determine the buffer stock schemes required for market-intervention program. In the previous researches, the buffer stock models have been developed separately based on optimization and econometrics methods. Optimizations methods have been used to determine the level of availability with schemes consist of time and quantity of buffer stock. Econometrics methods have been used to determine the equilibrium price by using the selling-price and the amount of buffer stock. In this research, the integration of optimization model (multi-objectives programming) and econometrics model are used to develop a buffer stock model with the decision variables that consist of quantity, time, and price. Key Words: Buffer Stock Model, Market-Intervention, Price-Stabilizatio

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Agent-Based Computational Modeling And Macroeconomics

Agent-based Computational Economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. This essay discusses the potential use of ACE modeling tools for the study of macroeconomic systems. Points are illustrated using an ACE model of a two-sector decentralized market economy. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/amulmark.htmagent-based computational economics

Agent-Based Computational Economics: A Constructive Approach to Economic Theory

This chapter explores the potential advantages and disadvantages of Agent-based Computational Economics (ACE) for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, behaviors, and institutional arrangements. Extensive annotated pointers to ACE surveys, research, course materials, and software can be accessed here: http://www.econ.iastate.edu/tesfatsi/ace.htmagent-based computational economics; Learning; network formation; decentralized market economy

Inventories in motion : a new approach to inventories over the business cycle

I propose an inventories-in-motion concept which represents a new approach to inventories over the business cycle. This channel has previously been ignored by macroeconomists. I build a general equilibrium business cycle model in which inventories arise naturally as a result of gaps between production of goods and their consumption as goods are distributed. These inventories are actively managed and adjusted to meet consumption and investment needs in the economy. Although conceptually very simple, I show that such inventory behaviour matches a number of stylised facts of aggregate inventories. Nonetheless, my model does not admit an important role for inventory management improvements in declining macroeconomic volatility in the last 30 years

Combining Top-Down and Bottom-up in Energy Policy Analysis: A Decomposition Approach

The formulation of market equilibrium problems as mixed complementarity problems (MCP) permits integration of bottom-up programming models of the energy system into top-down general equilibrium models of the overall economy. Despite the coherence and logical appeal of the integrated MCP approach, implementation cost and dimensionality both impose limitations on its practical application. A complementarity representation involves both primal and dual relationships, often doubling the number of equations and the scope for error. When an underlying optimization model of the energy system includes upper and lower bounds on many decision variables the MCP formulation may suffer in robustness and efficiency. While bounds can be included in the MCP framework, the treatment of associated income effects is awkward. We present a decomposition of the integrated MCP formulation that permits a convenient combination of top-down general equilibrium models and bottom-up energy system models for energy policy analysis. We advocate the use of complementarity methods to solve the top-down economic equilibrium model and quadratic programming to solve the underlying bottom-up energy supply model. A simple iterative procedure reconciles the equilibrium prices and quantities between both models. We illustrate this approach using a simple stylized model. --Mathematical Programming,Mixed Complementarity,Top-Down/Bottom-Up

Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework

In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) ï¾– for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs