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A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand

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Abstract

This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.

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Authors and Affiliations

  1. Department of Industrial Engineering, Yazd University, Yazd, Iran

    Alireza Goli

  2. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

    Erfan Babaee Tirkolaee

  3. Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Erfan Babaee Tirkolaee

  4. Department of Systems and Information Engineering, University of Virginia, Charlottesville, 22904, USA

    Behnam Malmir

  5. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Gui-Bin Bian

  6. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India

    Arun Kumar Sangaiah

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  1. Alireza Goli
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Correspondence to Arun Kumar Sangaiah.

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Goli, A., Tirkolaee, E.B., Malmir, B. et al. A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing 101, 499–529 (2019). https://doi.org/10.1007/s00607-018-00692-2

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