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Enterprise resource planning (ERP) software is one of costly and crucial projects for business investment. Due to the selection criteria of ERP software are numerous and fuzziness, selecting the optimal ERP software is a critical process in the early phase of an ERP project. This paper presents a practical procedure which combines both the ISO 9126 standard and the fuzzy analytic hierarchy process (FAHP) approach to optimize the ERP selection problems.There are total 32 criteria sifted out which include 21 software quality criteria of ISO 9126 standard. Two practical cases which belong to different industries are applied to illustrate the practicality of the procedure, one is semiconductor manufacturing industry and another is chain store retailer service industry. We find that there are diverse weights for software quality criteria between these two industries. We also find that the cost issue and time issue are significantly important in both two cases.

A periodic review inventory model with backorders and lost sales in which both lead-time and the periodic review length are decision variables and the production interval demand follows a normal distribution is explored in this article. Ouyang and Chuang discussed this problem in a recent paper published in the International Journal of Systems Science. However, their algorithms might not, although intended to, find the optimal solution due to questionable results in their solution procedure. The purpose of this study is 3-fold. First, the criteria for the existence and uniqueness of the critical solution for minimising the total expected annual cost are determined. Second, a correct and efficient algorithm to improve their method is constructed to find the optimal lead-time and periodic review length simultaneously. Finally, some numerical examples are provided to compare our solution procedure with that of Ouyang and Chuang's method to demonstrate their questionable results.

Manufacturing factories are always in pursuit of achieving higher productivity in order to satisfy the growing demands of the consumers but are often meet with challenges in achieving these goals due to various complexities. These complexities arise from the limitations of human operator's i.e. their inability to handle uncertainties, complexity, and understand/memorize large data. Intelligent manufacturing systems are positioned to yield superior results than traditional manufacturing as they are capable of analyzing, self-learning, apprehending complexity, and self-driven for quality and cost. Unlike traditional manufacturing, intelligent manufacturing systems are also able to store and analyze large amounts of production data in order to achieve higher production rate and shorten the time-tomarket. This keynote is to provide a longitudinal perspective on various aspects of recent achievements in intelligent design, scheduling, maintenance, and control. For each aspect, concept, requirement, and application implemented, methodologies deployed are also presented along with their limitations and future research directions. This keynote also presents fundamental concepts and futuristic views that the authors are currently implementing in an intelligent manufacturing system for factories next-generations.

In practical project management cases, they company many complex resource and large-scale related activities. Especially, it is under the conflict and incommensurate of time and cost, that makes it more difficult to make decision. Hence, we construct fuzzy multiobjective programming model from CPM technique, by this we emphasize the selectable flexibility among the feasible projects, and describe the decision problem brought from uncertainty and complex in project. In this paper, we relax the consumed time and cost of activity events when assumptions are certain to merge the practical situation. We applied fuzzy number to express estimate time and cost. It based on Lee and Li method to solve this problem. Finally, we employ numerical example to explain it, and LINGO package to calculate.