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Aggregate production planning (APP) is concerned with determining the optimum production and workforce levels for each period over the medium term planning horizon. It aims to set overall production levels for each product family to meet fluctuating demand in the near future. APP is one of the most critical areas of production planning systems. After the state-of-the-art summaries in 1992 by Nam and Logendran [ Nam, S. J., & Logendran, R. (1992). Aggregate production planning-a survey of models and methodologies. European Journal of Operational Research, 61(3), 255-272. ], which specifically summarized the various existing techniques from 1950 to 1990 into a framework depending on their abilities to either produce an exact optimal or near-optimal solution, there has not been any systematic survey in the literature. This paper reviews the literature on APP models to meet two main purposes. First, a systematic structure for classifying APP models is proposed. Second, the existing gaps in the literature are demonstrated in order to extract future directions of this research area. This paper covers a variety of APP models' characteristics including modeling structures, important issues, and solving approaches, in contrast to other literature reviews in this field which focused on methodologies in APP models. Finally some directions for future research in this research area are suggested.

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Quality is one of the main pillars that should be effectively considered in managing of industrial projects. In traditional project scheduling problems, only the time and cost are considered without the quality parameters. In this paper, it is suggested that the quality parameter is embedded into the traditional time-cost trade-off problem to develop a time, cost, and quality trade-off problem (TCQTP) with some practical assumptions. To the best of our knowledge, this problem has not been extensively treated in literature yet. The contributions of this article are in threefold: (1) incorporating a practical assumption to the TCQTP that is a relaxation of deterministic assumption to stochastic ones, which no paper has ever attempted to consider it in TCQTP; (2) relaxing the linearity assumption of time function, cost function, and quality function to a general form; and (3) integrating the TCQTP with a robust solution method to minimize the variation effect on time, cost, and quality. Computational experiments, which were done on benchmark problem, show the applicability and good performance of suggested TCQTP.

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