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Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing

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Abstract

Evolutionary algorithms are stochastic search methods that mimic the principles of natural biological evolution to produce better and better approximations to a solution and have been used widely for optimization problems. A general problem of continuous-time aggregate production planning for a given total number of changes in production rate over the total planning horizon is considered. It is very important to identify and solve the problem of continuous-time production planning horizon with varying production rates over the interval of the planning period horizon. Some of the researchers have proposed global search methods for the continuous-time aggregate production-planning problem. So far, less work is reported to solve the problem of continuous-time production planning using local search methods like genetic algorithms (GA) and simulated annealing (SA). So in this work, we propose a modified single objective evolutionary program approach, namely GA, SA, and hybrid genetic algorithms-simulated annealing (GA-SA) for continuous-time production plan problems. The results are compared with each other and it was found that the hybrid algorithm performs better.

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Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Technology Madras, Chennai, 600036, India

    K. Ganesh

  2. Department of Management Studies, Regional Engineering College, Tiruchirappalli, 620015, India

    M. Punniyamoorthy

Authors
  1. K. Ganesh
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  2. M. Punniyamoorthy
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Correspondence to K. Ganesh.

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Ganesh, K., Punniyamoorthy, M. Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int J Adv Manuf Technol 26, 148–154 (2005). https://doi.org/10.1007/s00170-003-1976-4

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  • DOI: https://doi.org/10.1007/s00170-003-1976-4

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