PRODUCTION AND OPERATIONS MANAGEMENT
Year:2012 | Volume:3 | Issue:1 (4)|Papers Count:7

Considering its application, stochastic lot sizing is a significant subject in production planning. Also the concept of service level is more applicable than shortage cost from managers’ viewpoint. In this paper, the stochastic multi period multi item capacitated lot sizing problem has been investigated considering service level constraint. First, the single item model has been developed considering service level and with no capacity constraint and then, it has been solved using dynamic programming algorithm and the optimal solution has been derived. Then the model has been generalized to multi item problem with capacity constraint. The stochastic multi period multi item capacitated lot sizing problem is NP-Hard, hence the model could not be solved by exact optimization approaches. Therefore, simulated annealing method has been applied for solving the problem. Finally, in order to evaluate the efficiency of the model, low level criterion has been used.

In recent years, numerous methods have been proposed to deal with supplier evaluation and selection problem, but a point which has been usually neglected by researchers is the role of purchasing items. The aim of this paper is to propose an integrated approach to select suppliers and allocate orders on the basis of the nature of the purchasing items which means that this issue plays an important role in supplier selection and order allocation. Therefore, items are first categorized according to the Kraljic’s model by the use of FMEA technique. Then, suppliers are categorized and evaluated in four phases with respect to different types of purchasing items (Strategic, Bottleneck, Leverage and Routine). Finally, an integer linear programming is utilized to allocate purchasing orders to suppliers. Furthermore, an empirical example is conducted to illustrate the stage of proposed approach. Results imply that ranking of suppliers and allocation of purchasing items based on the nature of purchasing items will create more capabilities in managing purchasing items and suppliers.

Development and management of human capital is an important task because it affects the performance of organization and hence the process improvements. However, it is necessary to deploy performance evaluation to prioritize improvable processes due to limited human resource, time and equipments. The objective of this research is to evaluate performance of the developed and managed human capital based upon the predetermined key performance indicators of American Productivity and Quality Center (APQC) and to prioritize processes by Importance-Performance Analysis (IPA) approach. As a case study, Esfahan Oil Refining Company (EORC) has been studied. In order to analyze hypotheses, the state of human resource has been determined through data extracting among the best practice industries, and hence the strengths and weaknesses of the EORC have been identified. Finally, fuzzy numbers have been assigned to the Key Performance Indicators (KPIs) and then the processes have been measured using the average value of its related KPIs. Consequently, the importance of the developed and managed human capital processes could be determined using the five points Likert spectrum. The reliability of the questionnaire has been determined by the Cronbach’s Alpha of higher than 0.7, which is satisfactory. Findings imply that 10 out of 35 HCM processes in needs of improvement are prioritized as managing employee performance; managing employee relations; managing employee orientation and deployment; developing and managing employee metrics; developing and training employees; managing employee communication; managing human resource information systems (HRIS); managing and maintaining employee data; managing employee inquiry process; and developing and managing time and attendance.

In this paper, a non linear mathematical model has been proposed for solving a single machine scheduling problem with a linear earliness and quadratic tardiness cost, where machine idle time and preemptions are allowed. As the model is complex and cannot be solved in polynomial time, it has been assumed to be a NP hard problem, so the known optimal solution methods may not be applicable for its solution. A Genetic Algorithm approach has been developed for solving the model and numerical examples has been presented, which imply that the proposed method is efficient and effective.

Taxonomic studies in manufacturing not only give appropriate description of dominated organizational groups, but also provide necessary context for more special studies and theory building in subjects such as performance evaluation and manufacturing decision making. Available taxonomic investigations on manufacturing systems are limited. In this paper, taxonomic study of manufacturing systems has been performed based on nine decision categories in Iran. K-mean cluster analysis has been used and four clusters have been identified as industry average, world class, less developed and developed. Their business performance has been also studied. Findings imply that only the less developed cluster has significant difference in business performance from other clusters. Justification of the four clusters has been discussed and finally, limitations and suggestions have been addressed.

Nowadays, warehouses dedicate considerable proportion of network’s costs in a supply chain and conditions such as uncertainty in cost parameters, production capacity and demands emphasize on necessity of reasonable decisions on warehouse management. In this paper, uncertainty has been investigated under discrete different scenarios. Moreover, a new model has been proposed for redesigning of warehouses considering the min-max cost function for scenarios in specific customer servicing coverage radius. The robust approach used in this paper has been investigated by numerical examples and the obtained costs have been compared with the expected value model results. Findings imply efficiency of the proposed robust approach. Also, the costs resulted from the new relocation model have been evaluated with respect to the costs of continuing the current supply chain configuration and the findings indicate a significant cost improvement.

The aim of this paper is to prioritize projects by a new approach. For prioritizing EFQM projects, self-assessment is conducted by industry experts and then, improvement projects are determined. For these projects, “prioritizing” is the main problem in organizations. Because of resource constraints, those projects should be selected and implemented that provide most benefits. Selecting the appropriate indicators, such as “quantity of required resources”, “impact on stakeholders” and “probability of project success” can significantly influence selection of projects. These indicators have been considered in the Try-Success matrix. Also, since each project is depended on the EFQM’s criteria, the weights of the criteria have been localized. In the designed software of this study, the scoring mechanism is based on Try-Success matrix, and the local weights are determined by Fuzzy-group AHP. In this paper, the application of software in prioritization of projects has been also addressed. The output of this approach together with software and investigation, are the determined projects that have higher priorities and provide considerable improvement to the industry.