nep-cmp New Economics Papers
on Computational Economics
Issue of 2009‒11‒14
eight papers chosen by
Stan Miles
Thompson Rivers University

  1. A Genetic Algorithm for Lot Size and Scheduling under Capacity Constraints and Allowing Backorders By José Fernando Gonçalves; Paulo S. A. Sousa
  2. Computing the probability mass function of the maximum flow through a reliable network By Megha Sharma; Diptesh Ghosh
  3. Biofuels in the world markets: A Computable General Equilibrium assessment of environmental costs related to land use changes By Antoine BOUET; Betina DIMARANAN; Hugo VALIN
  4. Evaluating Downside Risks in Reliable Networks By Megha Sharma; Diptesh Ghosh
  5. Approximating Closed Form Solutions to a Class of Feedback Policies By Sandal, Leif K.
  6. Encouraging Cooperation in Ad-hoc Mobile-Phone Mesh Networks for Rural Connectivity By Kavitha Ranganathan; Vikramaditya Shekhar
  7. The World Gas Market in 2030: Development Scenarios Using the World Gas Model By Daniel Huppmann; Ruud Egging; Franziska Holz; Sophia Ruester; Christian von Hirschhausen; Steven A. Gabriel
  8. Forecasting Inflation Using Dynamic Model Averaging By Gary Koop; Dimitris Korobilis

  1. By: José Fernando Gonçalves (LIAAD and Faculdade de Economia, Universidade do Porto); Paulo S. A. Sousa (LIAAD and Faculdade de Economia, Universidade do Porto)
    Abstract: This paper addresses the problem of scheduling economic lots in a multi-product single machine environment. A mixed integer non-linear programming formulation is developed which finds the optimal sequence and economic lots. The model takes explicit account of initial inventories, setup times, allows setups to be scheduled at arbitrary epochs in continuous time and models backorders. To solve the problem we develop a hybrid approach, combining a genetic algorithm and linear programming. The approach is tested on a set of instances taken from the literature and compared with other approaches. The experimental results validate the quality of the solutions and the effectiveness of the proposed approach.
    Keywords: ELSP, Lot-sizing, Control, Production, Scheduling, Optimization, Genetic algorithm
    Date: 2009–11
    URL: http://d.repec.org/n?u=RePEc:por:fepwps:341&r=cmp
  2. By: Megha Sharma; Diptesh Ghosh
    Abstract: In this paper we propose a fast state-space enumeration based algorithm called TOP-DOWN capable of computing the probability mass function of the maximum s-t flow through reliable networks. The algorithm computes the probability mass function in the decreasing order of maximum s-t flow values in the network states. This order of enumeration makes this algorithm attractive for commonly observed reliable networks, e.g., in telecommunication networks where link reliabilities are high. We compare the performance of the TOP-DOWN algorithm with a path-based exact algorithm and show that the TOP-DOWN algorithm solves problem much faster and is able to handle much larger problems than existing algorithms.
    Date: 2009–10–05
    URL: http://d.repec.org/n?u=RePEc:iim:iimawp:2009-10-01&r=cmp
  3. By: Antoine BOUET; Betina DIMARANAN; Hugo VALIN
    Abstract: Biofuels in the world markets: A Computable General Equilibrium assessment of environmental costs related to land use changes
    Date: 2009–11
    URL: http://d.repec.org/n?u=RePEc:tac:wpaper:6&r=cmp
  4. By: Megha Sharma; Diptesh Ghosh
    Abstract: Reliable networks are those in which network elements have a positive probability of failing. Conventional performance measures for such networks concern themselves either with expected network performance or with the performance of the network when it is performing well. In reliable networks modeling critical functions, decision makers are often more concerned with network performance when the network is not performing well. In this paper, we study the single-source single-destination maximum flow problem through reliable networks and propose two risk measures to evaluate such downside performance. We propose an algorithm called COMPUTE-RISK to compute downside risk measures, and report our computational experience with the proposed algorithm
    Date: 2009–10–01
    URL: http://d.repec.org/n?u=RePEc:iim:iimawp:2009-09-02&r=cmp
  5. By: Sandal, Leif K. (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration)
    Abstract: Dynamic optimization problems cover a large class of problems in theoretical and applied economics. A simple iterative algorithm with fast convergence is proposed. It is demonstrated that the algorithm in a few steps produce excellent analytic (closed form) approximations including error bounds to a class of nonlinear problems. The algorithmic scheme is also well suited to produce numerical solutions. The notions of dynamic and potential rents are operationalized. The algorithm is utilizing a relation balancing these concepts. The result is particularly strong in the case of zero discounting where the exact CU-optimal policy is determined in a single step. Applying a particular seed in the general convergent scheme reproduces in a simple way results (formulas) published in the last decade in bioeconomics.
    Keywords: Closed form approximations; Contraction algorithm; Renewable resource economics; Capital dynamic modeling; Zero discounting and optimality
    JEL: A12 C61 C63 E10 Q00
    Date: 2009–09–15
    URL: http://d.repec.org/n?u=RePEc:hhs:nhhfms:2009_008&r=cmp
  6. By: Kavitha Ranganathan; Vikramaditya Shekhar
    Abstract: This paper proposes a rating based scheme for encouraging user participation in ad-hoc mobile phone mesh networks. These networks are particularly attractive for remote/rural areas in developing countries as they do not depend on costly infrastructure and telecom operators. We evaluate our scheme using extensive simulations and find that our proposed scheme is successful in enhancing the network throughput.
    Date: 2009–09–09
    URL: http://d.repec.org/n?u=RePEc:iim:iimawp:2009-08-01&r=cmp
  7. By: Daniel Huppmann; Ruud Egging; Franziska Holz; Sophia Ruester; Christian von Hirschhausen; Steven A. Gabriel
    Abstract: In this paper, we discuss potential developments of the world natural gas industry at the horizon of 2030. We use the World Gas Model (WGM), a dynamic, strategic representation of world natural gas production, trade, and consumption between 2005 and 2030. We specify a "base case" which defines the business-as-usual assumptions based on forecasts of the world energy markets. We then analyze the sensitivity of the world natural gas system with scenarios: i) the emergence of large volumes of unconventional North American natural gas reserves, such as shale gas; ii) on the contrary, tightly constrained reserves of conventional natural gas reserves in the world; and iii) the impact of CO2-constraints and the emergence of a competing environmental friendly "backstop technology". Regional scenarios that have a global impact are: iv) the full halt of Russian and Caspian natural gas exports to Western Europe; v) sharply constrained production and export activities in the Arab Gulf; vi) heavily increasing demand for natural gas in China and India; and finally vii) constraints on liquefied natural gas (LNG) infrastructure development on the US Pacific Coast. Our results show considerable changes in production, consumption, traded volumes, and prices between the scenarios. Investments in pipelines, LNG terminals and storage are also affected. However, overall the world natural gas industry is resilient to local disturbances and can compensate local supply disruptions with natural gas from other sources. Long-term supply security does not seem to be at risk.
    Keywords: Natural gas, investments, reserves, climate policy
    Date: 2009
    URL: http://d.repec.org/n?u=RePEc:diw:diwwpp:dp931&r=cmp
  8. By: Gary Koop (Department of Economics, University of Strathclyde and RCEA); Dimitris Korobilis (Department of Economics, University of Strathclyde and RCEA)
    Abstract: There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which incorporate dynamic model averaging. These not only allow for coefficients to change over time (i.e. the marginal effect of a predictor for inflation can change), but also allows for the entire forecasting model to change over time (i.e. different sets of predictors can be relevant at different points in time). In an empirical exercise involving quarterly US inflation, we fi…nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark approaches (e.g. random walk or recursive OLS forecasts) and more sophisticated approaches such as those using time varying coefficient models.
    Keywords: Option Pricing; Modular Neural Networks; Non-parametric Methods
    JEL: E31 E37 C11 C53
    Date: 2009–01
    URL: http://d.repec.org/n?u=RePEc:rim:rimwps:34_09&r=cmp

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