The purpose of this paper is developing a method to choose an appropriate Computerized Maintenance Management System (CMMS) using Multiple Criteria Decision Making (MADM) for a dairy company. Among different methods, the grey group TOPSIS and fuzzy group VIKOR methods were selected. TOPSIS is a Technique for order of Preference by Similarity to Ideal Solution and VIKOR technique is a method based on compromise programming. The data in this article were gathered using the interviews with the company’s managers and elites in the field of maintenance. Then, out of five types of maintenance software, the best ones were selected using these two techniques and finally the results were compared with each other. In the selection process, 13 sub-criteria were introduced under 5 main criteria. These criteria were selected out of a huge number of criteria using the studies of others and by consulting with the company’s managers and experts. We have tried to make the choices in a way that the majority of aspects could be considered. This paper helps the maintenance managers in decision-making related to choosing the CMMS software in uncertainty environment. Using two different fuzzy and grey approaches and comparing the results can lead to appropriate selection and improve the confidence in decision-making. Maintenance planning issues are among the important issues in industrial production systems. Maintenance systems have basically made remarkable progress in recent years. The increase of companies’ competitive pressure on the one hand and the close relationship between maintenance activities and companies’ core activities, on the other hand, have encouraged companies to use the software in order to manage their maintenance activities.

In construction cost management system, normally earned value management (EVM) is applied as an efficient control approach in both status detection and estimation at completion (EAC) cost forecasting. The traditional approaches in EAC predictions normally extend the current situation of a project to the future by employing pervious performance factor. The proposed approach of this paper considers both qualitative and quantitative factors affecting the EAC prediction. The proposed approach of this research not only estimates the completion of the project, but also it can generate accurate forecast for the entire future periods using a fuzzy neural network model. The model is also implemented for a real-world case study and yields encouraging preliminary results.

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.

Employers are looking for reducing execution time and maintaining the quality of the projects that are the main objective of the projects. In this article, we focus on crashing projects by con-sidering different factors such as cost, time, quality and risk. For the proposed integer linear model, cost of conformance and cost of non-conformance are considered as parts of the costs of quality of deliverables in projects. The cost of conformance consists of the costs of training the project team, inspection and test of deliverables. The cost of non-conformance also includes costs of rework and scrap. Project risk management is one of the important aspects of the pro-jects. The present study also considers the impact of risks, which is highly applicable in projects with a high level of uncertainty. Results are presented using integer programming approach with the aim of minimizing the costs of the project.