nep-cmp New Economics Papers
on Computational Economics
Issue of 2012‒01‒18
nine papers chosen by
Stan Miles
Thompson Rivers University

  1. An Artificial Immune System Algorithm for the Resource Availability Cost Problem By V. VAN PETEGHEM; M. VANHOUCKE
  2. Truncated branch-and-bound guided meta-heuristics for the unrelated parallel machine scheduling problem By V. SELS; J. COELHO; M. VANHOUCKE
  3. An Experimental Investigation of Metaheuristics for the Multi-Mode Resource-Constrained Project Scheduling Problem on New Dataset Instances By V. VAN PETEGHEM; M. VANHOUCKE
  4. A Goal Programming Model for Optimal Portfolio Diversification By Davide La Torre; Marco Maggis
  5. Solution representation, diversity and space reduction: A computational experiment with meta-heuristics By B. MAENHOUT; M. VANHOUCKE
  6. An Artificial Immune System based approach for solving the Nurse Re-rostering Problem By B. MAENHOUT; M. VANHOUCKE
  7. Managing periodic review inventory systems with capacitated replenishments By B. RAA; T. DUBOIS; W. DULLAERT
  8. An Assessment of the Perceived Learning by Millennials during One-Day One-Topic Marketing Simulations By Timothy E. Burson; Bradley W. Brooks; Steven Cox
  9. Leveraged Network-Based Financial Accelerator By Luca RICCETTI; Alberto RUSSO; Mauro GALLEGATI

  1. By: V. VAN PETEGHEM; M. VANHOUCKE
    Abstract: In this paper, an Artificial Immune System (AIS) algorithm for the resource availability cost problem (RACP) is presented, in which the total cost of the (unlimited) renewable resources required to complete the project by a pre-specified project deadline should be minimized. The AIS algorithm makes use of mechanisms inspired by the vertebrate immune system and includes different algorithmic components, such as a new fitness function, a probability function for the composition of the capacity lists, and a K-means density function in order to avoid premature convergence. All components are explained in detail and computational results for the RACP are presented.
    Date: 2011–11
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/757&r=cmp
  2. By: V. SELS; J. COELHO; M. VANHOUCKE
    Abstract: In this paper, we consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop two heuristic approaches, i.e. a genetic algorithm and a tabu search algorithm and the hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branch-and-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on standard data sets available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are (almost) able to compete with the best known results from the literature.
    Date: 2011–10
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/753&r=cmp
  3. By: V. VAN PETEGHEM; M. VANHOUCKE
    Abstract: In this paper, an overview is presented of the existing metaheuristic solution procedures to solve the multi-mode resource-constrained-project scheduling problem, in which multiple execution modes are available for each of the activities of the project. A fair comparison is made between the different metaheuristic algorithms on the existing benchmark datasets and on a newly generated dataset. Computational results are provided and recommendations for future research are formulated.
    Date: 2011–11
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/758&r=cmp
  4. By: Davide La Torre; Marco Maggis
    Abstract: We present a goal programming model for risk minimization of a financial portfolio managed by an agent subject to different possible criteria. We extend the classical risk minimization model with scalar risk measures to general case of set-valued risk measure. The problem we obtain is a set-valued optimization program and we propose a goal programming-based approach to obtain a solution which represents the best compromise between goals and the achievement levels. Numerical examples are provided to illustrate how the method works in practical situations.
    Date: 2012–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1201.1783&r=cmp
  5. By: B. MAENHOUT; M. VANHOUCKE
    Abstract: In this paper we study the characteristics of population based meta-heuristics that distinguish the procedures from a standard meta-heuristic and that positively contribute to the quality of the solutions obtained. More precisely, we investigate and discuss the importance of a wellconsidered solution representation, the beneficial effect of diversity in the solution population and the possible improving effect of solution space reduction techniques on the overall quality of the solution. Empirical results are obtained by a computational experiment of different metaheuristics on resource-constrained project scheduling and personnel scheduling problems.
    Date: 2011–11
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/756&r=cmp
  6. By: B. MAENHOUT; M. VANHOUCKE
    Abstract: Personnel resources are a variable and dynamic resource that introduce uncertainty in the operational processes. Constructed personnel rosters can be disrupted and render infeasible rosters. Feasibility has to be restored by adapting the original announced nurse rosters. In this paper, we investigate the potential of an artificial immune system to solve the nurse re-rostering problem, which entails a reactive approach to these schedule disruptions. In our computational results, we show that the proposed procedure performs consistently well under many different circumstances. We validate the performance of different problem-specific operators and compare the proposed procedure with the existing literature.
    Date: 2011–11
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/754&r=cmp
  7. By: B. RAA; T. DUBOIS; W. DULLAERT
    Abstract: This paper studies periodic review inventory systems in which replenishments are capacitated. This capacity restriction implies that the order-up-to level may not always be reached at each replenishment, such that additional safety stock is needed to achieve the same service level as in the uncapacitated case usually assumed in the existing literature. To determine the required level of safety stock, and hence the order-up-to level, an iterative procedure is proposed which can be adjusted to either find an approximate solution quickly, or a more accurate solution when larger computation times are allowed. Computational experiments illustrate the impact of a restricted replenishment capacity on the required safety stock level, and the effectiveness of the proposed iterative method in calculating the required order-up-to levels.
    Keywords: Periodic review, Order-up-to, Base-stock, Safety stock, Fill rate.
    Date: 2011–09
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:11/736&r=cmp
  8. By: Timothy E. Burson (McColl School of Business, Queens University of Charlotte); Bradley W. Brooks (McColl School of Business, Queens University of Charlotte); Steven Cox (McColl School of Business, Queens University of Charlotte)
    Abstract: Millennials have been characterized as active learners who seek engaging, customized, and relevant educational experiences. Born in the digital era they expect rapid feedback and an environment where they can quickly test different strategies. Simulations would seem to mesh well with Millennial learning styles. However, professors have often criticized simulations as too complex, too time consuming, and unfocused. Recently, a new group of simulations have been developed which focus on a single issue, are simple to learn, and can be completed within a single class period. This research explores how Millennials will find these simplified products in terms of the learning experience and subject matter mastery.
    Date: 2012
    URL: http://d.repec.org/n?u=RePEc:msb:wpaper:2012-01&r=cmp
  9. By: Luca RICCETTI (Universit… Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali); Alberto RUSSO (Universit… Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali); Mauro GALLEGATI (Universit… Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)
    Abstract: In this paper we build on the network-based financial accelerator model of Delli Gatti et al. (2010), modelling the firms' financial structure following the "dynamic trade-off theory", instead of the "pecking order theory". Moreover, we allow for multiperiodal debt structure and consider multiple bank-firm links based on a myopic preferred-partner choice. In case of default, we also consider the loss given default rate (LGDR). We find many results: (i) if leverage increases, the economy is riskier; (ii) a higher leverage pro-cyclicality has a destabilizing effect; (iii) a pro-cyclical leverage weakens the monetary policy effect; (iv) a Central Bank that wants to increase the interest rate, should previously check if the banking system is well capitalized; (v) policy maker has to develop the laws about bankruptcies to reduce the LGDR and improve the stability of banks.
    Keywords: Leverage, agent based model, bankruptcy cascades, dynamic trade-off theory, monetary policy
    JEL: C63 E32 E52 G01
    Date: 2011–12
    URL: http://d.repec.org/n?u=RePEc:anc:wpaper:371&r=cmp

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