In this paper, a new robust approach is presented to handle demand uncertainty in cell formation and layout design process. Unlike the scenario based approaches, which use predefined scenarios to represent data uncertainty, in this paper, an interval approach is implemented to address data uncertainty for the part demands, which is more realistic and practical. The objective is to minimize the total inter- and intra-cell material handling cost. The proposed model gives machine cells and determines inter-and intra-cell layouts in such a way that the decision maker can control the robustness of the layout against the level of conservatism. An illustrative example is solved by CPLEX 10 to demonstrate the performance of the proposed method. The results reveal that when the level of conservatism is changed the optimal layout can vary, significantly.

Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM), a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain.

In this paper, an integrated layout model has been considered to incorporate intra and inter-department layout. In the proposed model, the arrangement of facilities within the departments is obtained through the QAP and from the other side the continuous layout problem is implemented to find the position and orientation of rectangular shape departments on the planar area. First, a modified version of QAP with fewer binary variables is presented. Afterward the integrated model is formulated based on the developed QAP. In order to evaluate material handling cost precisely, the actual position of machines within the departments (instead of center of departments) is considered. Moreover, other design factors such as aisle distance, single or multi row intra-department layout and orientation of departments have been considered. The mathematical model is formulated as mixed-integer programming (MIP) to minimize total material handling cost. Also due to the complexity of integrated model a heuristic method has been developed to solve large scale problems in a reasonable computational time. Finally, several illustrative numerical examples are selected from the literature to test the model and evaluate the heuristic.

One of the key challenges of nurse scheduling problem (NSP) is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients’ demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software.

In this paper, we study a reverse supply chain consists of three layers of the supply chain including suppliers, producers and customers by considering customers’ requirements. In the customer layer, we analyze the customer’s data to identify and fulfill their needs by collecting a list of customers’ views into consideration. In this case, the proposed model analyzes the customers view in the three areas of transport, production and quality and it uses the coding system for getting customers’ opinions. Then, by using the K-means algorithm, which is one of the data analyzing algorithms, the proposed model clusters the data so that similar data enter to the same cluster. The mathematical model is developed for each of the categories and Lingo software package is employed to solve the resulted problem in each category.