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Please cite this article as: M. Sakhaii, R. Tavakkoli-Moghaddam, M. Bagheri, B. Vatani, A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines, Appl. Math. Modelling (2015), doi: http://dx.Abstract:In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP)model to solve a dynamic cellular manufacturing system (DCMS) with unreliable machines anda production planning problemsimultaneously. This model is incorporated with dynamic cell formation, intercell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system's planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposedapproach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demonstrate the validity of the presentedmodel and the performanceof the developed approach in finding an optimal solution. Finally, the conclusion is presented.

Summary This paper presents a new mixed‐integer linear programming model for day‐ahead self‐scheduling of generating companies integrating the underlying ideas of robust optimization (RO) and stochastic programming (SP) to cope with the uncertainties of electricity market prices and availability/unavailability of units. The proposed hybrid approach models the uncertainty of electricity market prices by bounded intervals instead of probability distributions, aiming to derive a more tractable optimization model. Conservatism against uncertain electricity market prices is adjusted by a certain parameter named budget of robustness. Also, a renovated Markov chain approach considering the chance of return (i.e., return rate) for failed units, in addition to forced outage rate of units, in each hour of the scheduling period is introduced in this paper to produce a set of scenarios modeling the availability/unavailability of units. Therefore, the proposed hybrid self‐scheduling approach benefits from both the tractability of RO and modeling accuracy of SP. The proposed approach is implemented on IEEE 118‐bus test system under different circumstances to illustrate its effectiveness compared to deterministic self‐scheduling model as well as the models only employing RO or SP. Copyright © 2015 John Wiley & Sons, Ltd.

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