Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Aboozar Jamalnia, Jian-bo Yang, Ardalan Feili, Dong-ling Xu, Gholamreza Jamali

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This is the first literature survey of its kind on aggregate production planning (APP) under uncertainty. Different types of uncertainty, such as stochasticity, fuzziness and possibilistic forms, have been incorporated into many management science techniques to study APP decision problem under uncertainty. In current research, a wide range of the literature which employ management science methodologies to deal with APP in presence of uncertainty is surveyed by classifying them into five main categories: stochastic mathematical programming, fuzzy mathematical programming, simulation, metaheuristics and evidential reasoning. First, the preliminary analysis of the literature is presented by classifying the literature according to the abovementioned methodologies, discussing about advantages and disadvantages of these methodologies when applied to APP under uncertainty and concisely reviewing the more recent literature. Then, APP literature under uncertainty is analysed from management science and operations management perspectives. Possible future research paths are also discussed on the basis of identified research trends and research gaps.
Original languageEnglish
JournalThe International Journal of Advanced Manufacturing Technology
Early online date2 Jan 2019
Publication statusPublished - 2019


  • Aggregate production planning (APP) under uncertainty
  • Management science methods
  • Literature on uncertain APP models


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