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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.

Companies pursuing extension of their activities and new companies in establishment phase are using various concepts and techniques to consider location decision, because location greatly affects both fixed and variable costs and on the overall profit of the company. This paper suggests a new use of quality function deployment (QFD) for facility location selection problem instead of applying it to traditional product quality promotion. Fuzzy sets concept is also incorporated to deal with imprecise nature of the linguistic judgments of decision makers. First, fuzzy QFD as a stand-alone approach is presented to address international facility location selection decision. To consider resource limitations and operational constraints, fuzzy goal programming is combined with fuzzy quality function deployment to present a developed approach to deal with global facility location-allocation decision. A demonstration of the applicability of proposed methodologies in a real-world problem is presented.

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