Evaluating the performance of aggregate production planning strategies under uncertainty

UoM administered thesis: Phd

  • Authors:
  • Aboozar Jamalnia

Abstract

The thesis is presented in three papers format. Paper 1 presents the first bibliometric literature survey of its kind on aggregate production planning (APP) in presence of uncertainty. It surveys a wide range of the literatures which employ operations research/management science methodologies to deal with APP in presence of uncertainty by classifying them into six main categories such as stochastic mathematical programming, fuzzy mathematical programming and simulation. After a preliminary literature analysis, e.g. with regard to number of publications by journal and publication frequency by country, the literature about each of these categories is shortly reviewed. Then, a more detailed statistical analysis of the surveyed research, with respect to the source of uncertainty, number of publications trend over time, adopted APP strategies, applied management science methodologies and their sub-categories, and so on, is presented. Finally, possible future research paths are discussed on the basis of identified research trends and research gaps.The second paper proposes a novel decision model to APP decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of present study, are discussed. The third paper is to appraise the performance of different APP strategies in presence of uncertainty. The relevant models for various APP strategies including the pure chase, the pure level, the modified chase and the modified level strategies are derived from the fundamental model developed for the mixed chase and level strategy in paper 2. The same procedure, which is used in paper 2, follows to solve the models constructed for these strategies with respect to the aforementioned objectives/criteria in order to provide business and managerial insights to operations managers about the effectiveness and practicality of these APP policies under uncertainty. Multiple criteria decision making (MCDM) methods such as additive value function (AVF), the technique for order of preference by similarity to ideal solution (TOPSIS) and VIKOR are also used besides multi-objective optimisation to assess the overall performance of each APP strategy.

Details

Original languageEnglish
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Award date31 Dec 2017