Abstract The goal of this research is providing optimization approach for bi-objective scheduling work shifts and job rotation problem in order to exploit efficient performance of employees. In this article scheduling work shifts and job rotation problems had been modeled in one mathematical model with two objective functions: minimizing labor cost and maximizing number of job rotation. Also human factors (fatigue, learning and forgetting) that have effect on workers performance were modeled. Presented model was mixed integer and nonlinear and genetic algorithm and ε-constraint technique have been used to solve it and gain Pareto sets. To illustrate efficiency of provided algorithm, its performance has been compared to results of LINGO. The results indicated that performance of genetic algorithm is better than that of LINGO in terms of computational time and objective value. To relate relationship between objectives, set of problems have been solved. Obtained Pareto indicated that there is a conflict between objectives. Hence with considering human factors that have effect on workforce’s performance it is needed to plan work shifts and job rotation simultaneously. Results indicated that the proposed optimization approach is capable to provide suitable alternatives while managers try to consider decreasing cost, increasing jobs variety and multi-skilled training.

Abstract The main purpose of this research is to provide a good insight into the use of analytic network process (ANP) that is a multiple criteria decision-making methodology in evaluating the supplier selection problems. Supplier selection problems are complex problems, which have many qualitative and quantitative concerns. In this study, an ANP model as a framework for evaluation of the most important factor and selecting strategic supplier is proposed. ANP can be used as a decision analysis tool to solve multi-criteria supplier selection problems that contain interdependencies. ANP is a complex methodology and requires more comparisons and efforts than the traditional AHP. To show the application of the proposed mode, a case as an example is proposed.

Abstract Risk management and improving the processes’ reliability are important in operations and production management. Failure modes and effects analysis (FMEA) is regarded as one of the most powerful methods in this area. High applicability and proper analyzability have caused FMEA to be among the most important techniques of systems’ risk analysis and safety improvement. On the other hand, widespread application of this method in various fields has revealed some weak points and constrains; subsequently many researchers have offered revisions and modifications on it. In this article, analytical network process (ANP), a modern and powerful method in decision-making field, has been used in combination with FMEA (ANPFMEA) for defeating the shortcomings. ANP-FMEA considers mutual relationships of hazardous factors, and by offering certain structure, develops a systematic and flexible view in risk management scope. The suggested method deploys simple concept of risk priority number and assigns different importance in the form of power for each factor. The resulted RPN will cope better with the system, in which it is applied. This method provides more accurate analysis of risk and, consequently, more efficient and effective actions, causing attainment and maintenance of more desirable reliability.