Journal of Quality Engineering and Production Optimization
Volume:1 Issue: 2, Summer - Autumn 2015

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers’ requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large-sized instances to show the efficiency of the method in finding near optimal solution.

Supply chain includes two or more parties linked by flow of goods, information, and funds. In a decentralized system, supply chain members make decision regardless of their decision's effects on the performance of the other members and the entire supply chain. This is the key issue in supply chain management, that the mechanism should be developed in which different objectives should be aligned, and integrate their activities to optimize the entire system. Therefore, a coordination mechanism could be necessary to motivate members to achieve coordination. The contracts help the supply chain members to achieve coordination that will lead to improved supply chain performance. This paper analyzes a quantity-flexibility (QF) contract. The objective of this paper is to explore the applicability and benefits of the contracts, so to realize the importance of coordination by contracts, two cases have been studied. The first case is "no coordination" and the other case is "coordination with QF contract". Utilizing differential game theory, this paper formulates the optimal decisions of the supplier and the retailer in two different game scenarios: Nash equilibrium and cooperative game.It is expected that by designing the contracts as per the requirements of the supply chain members as well as the whole supply chain, supply chain performance can be improved.

An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted response models are obtained for each response according to their related conditions. In order to consider uncertainty in these models, confidence interval of fitted responses has been defined in the proposed method. This method uses all values in the confidence region of model outputs to define the robustness measure. This method has been applied on the traditional desirability function of each scenario in order to get the best setting of controllable variables for all scenarios simultaneously. To achieve this, the Imperialist Competitive Algorithm has been used to find the robust optimal controllable factors setting. The reported results and analysis of the proposed method confirm efficiency of the proposed approach in a dynamic environment.

In zero acceptance number sampling plans, the sample items of an incoming lot are inspected one by one. The proposed method in this research follows these rules: if the number of nonconforming items in the first sample is equal to zero, the lot is accepted but if the number of nonconforming items is equal to one, then second sample is taken and the policy of zero acceptance number would be applied for the second sample. In this paper, a mathematical model is developed to design single stage and double stage sampling plans. Proposed model can be used to determine the optimal tolerance limits and sample size. In addition, a sensitivity analysis is done to illustrate the effect of some important parameters on the objective function. The results show that the proposed two stage sampling plan has better performance than single stage sampling plan in terms of total loss function, sample size and robustness.

Uncertainty plays a major role in any project evaluation and management process. One of the trickiest parts of any production project work is its cost and time forecasting. Since in the initial phases of production projects uncertainty is at its highest level, a reliable method of project scheduling and cash flow generation is vital to help the managers reach successful implementation of the project. In the recent years, some scholars have tried to address uncertainty of projects in time and cost by using basic uncertainty modeling tools such as fuzzy sets theory. In this paper, a new approach is introduced to model project cash flow under uncertain environments using Atanassov fuzzy sets or intuitionistic fuzzy sets (IFSs). The IFSs are presented to calculate project scheduling and cash flow generation. This modern approach enhances the ability of managers to use their intuition and lack of knowledge in their decision-makings. Moreover, unlike the recent studies in this area, this model uses a more sophisticated tool of uncertain modeling which is highly practical in real production project environments. Furthermore, a new effective IFS-ranking method is introduced. The methodology is exemplified by estimating the working capital requirements in an activity network. The proposed model could be useful for both project proposal evaluation during feasibility studies and for performing earned value analysis for project monitoring and control.

In data envelopment analysis, the relative efficiency of a decision making unit (DMU) is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the DMUs to freely allocate weights to their inputs/outputs. However, this measure may not reflect a the true efficiency of a DMU because some of its inputs/outputs may not contribute reasonably in computing the efficiency measure. Traditionally, to overcome this problem weights restrictions have been imposed. But an approach for solving this problem by inclusion of some unobserved DMUs, obtained via a process with four steps, has been proposed in 2004. These unobserved DMUs are created by adjusting the output levels of certain observed relatively efficient DMUs. The method used in this research is for DMUs that are operating under a constant return to scale (CRS) technology with a single input multi-output context. This method is implemented for 47 branches of bank Maskan in northeast of Tehran and the results will be analysed.