Scientia Iranica
Volume:21 Issue: 6, 2014

Project selection process can be known as the most important action in the success of Six Sigma projects. In this way, ranking and assigning projects to implementation teams are considered as the most important steps in this process. There are copious of researches have worked on Six Sigma Projects Selection (SSPS). None of them, although, have not focused on selecting and allocating the projects as coherent process simultaneously. In this regard, this article presents a framework for decision making, selecting and assigning the six sigma projects to implementation teams. Owing to this, first of all, the most important criteria in SSPS process are selected. Subsequently, after identifying six sigma potential projects in the organization, fuzzy TOPSIS methodology is utilized to prioritize them. Afterwards, the Impact and Effort indexes for each project are calculated. Then, the Takagi-Sugeno-Kang(TSK) Fuzzy Expert System is used to allocate the projects to six sigma specialists. Finally, a case study in automobile industry is presented and then the framework is discussed to illustrate the application of the frameworkdeveloped.

This paper proposes a Stackelberg game-based approach for channel coordination for a supply chain consisting of one supplier and two competing retailers facing stochastic demand that is sensitive to both sales effort and retail price. In the proposed approach, the supplier, as the leader, defines the contract format and parameters, and the retailers determine the order quantity, retail price and sales effort. The literature primarily focuses on the design of the contract parameters to ensure channel coordination, whereas much less attention is given to the analysis of conditions supporting the contract implementation. This study focuses on the implementation of the return policy with sales rebate and penalty (RSRP) contract as a coordination mechanism. The negotiation and trading procedure among supply chain members is modeled using a simulation optimization-based decision support tool. The possibility that the retailers impose their own preferences that disturb the channel coordination after signing the RSRP contract is analyzed and a new limited return policy with sales rebate and penalty (LRSRP) contract, which helps the supplier guarantee channel coordination and control retailer decisions, is proposed.

Aggregate production planning is a medium-term production planning to determine the production plan to satisfy fluctuating demand. In this paper, a robust approach is used to formulate the aggregate production planning that some parameters such as production costs and customer demand are fuzzy variables. The concept of entropy is used to reduce the sensitivity of noisy data and to obtain a more robust aggregate production plan based on the proposed model. Finally, a numerical example is presented to explain the model solution. In addition the robustness of proposed model solutions are compared with other classical fuzzy programming approach.

Hybrid Make-To-Stock (MTS)/ Make-To-Order (MTO) production systems have recently attracted practitioners and academicians in the field of operations management, since these systems benefit from both stock-based and order-driven strategies. In this paper, capacity coordination dynamics of a hybrid MTS/MTO production system is addressed whose continuous production line comprises three workstations. Also, product portfolio of the considered system includes three kinds of products; pure MTS, pure MTO, and hybrid MTS/MTO. In the developed model, system performance is explored and assessed in terms of system delivery lead time. To do so, three capacity coordination rules are studies; simple average of expected demands, weighted average of expected demands, and the dynamic mechanism upon the difference between target and actual delivery lead times. Moreover, effects of demand uncertainty are taken into account.

In this paper an integrated approach was presented to simultaneously solve the cell formation and its layout problems. Many real world parameters such as part demands, alternative processing routings, machine capacities, cell dimensions, multi-rows arrangement of machines within the cells, aisle distances, etc., were considered in this approach, to make the problem more realistic. Also, in order to measure the material handling cost more precisely, the actual position of machines within the cells was used (instead of the center-to-center distances between the cells). Due to the complexity of the proposed problem a genetic algorithm was developed to efficiently solve it in a reasonable computational time. Finally, the performance of the genetic algorithm was evaluated by solving several numerical examples from the literature. The results revealed that when the decisions about the cell formation, inter and intra-cell layouts and routing of parts are simultaneously made the total material handling cost may reduce significantly in comparison with the sequential design approach.

With respect to multi-criteria decision-making (MCDM) problems under both stochastic and intuitionistic fuzzy uncertainties, this paper proposes an intuitionistic random MCDMapproach based on prospect theory. Since the reference point in prospect theory is affected by many factors, whichresults indifficulties for decision makers(DMs)to determine it,this paper developsan approach to acquire multiple reference points inthe form of interval numberstosupport a certain alternative to be the most preferred one, thus helping DM to finda satisfyingsolution by comparison with her/his own preference. Meanwhile, a novel score function of intuitionistic fuzzy(IF)number is proposed based on DM''s psychology of loss aversion, and a distance measure of IFset is proposed as well, considering fully its actual meaning. Finally, we illustrate the effectiveness and practicalityof the proposed method through a numerical example.

In 2013, Guo andWu (A complete ranking of DMUs with undesirable outputs using restrictions in DEA models«, Mathematical and Computer Modeling, Vol. 58, Nos. 5-6, pp. 1102-1109) proposed a model for ranking Decision Making Units (DMUs) in the presence of undesirable outputs. In this paper, we show that their model can be infeasible when some of the input data are zero. We also extend the super-eciency model proposed by Lee and Zhu in 2012 (Super-eciency infeasibility and zero data in DEA», European Journal of Operational Research, Vol. 216, No. 2, pp. 429-433) in the presence of undesirable output. Our proposed model is feasible when input and/or output data are nonnegative. A numerical example addresses the applicability of the proposed model.

This paper presents a vibration damping optimization (VDO) algorithm to solve multi-objective optimization problems for the first time. To do that, fast non-dominated sorting and crowding distance concepts were used in order to find and manage the Pareto-optimal solution. The proposed VDO is validated using several examples taken from the literature. The results were compared with the multi-objective simulated annealing (MOSA) and non-dominated sorting genetic algorithms (NSGA-II) presented as the state-of-the-art in evolutionary multi-objective optimization algorithms. The results indicate that multi-objective VDO (MOVDO) shows better performances with significant difference in terms of computational timewhile NSGA-IIis better to find Pareto solutions. In other standard metrics, MOVDO is able to generate true and well-distributed Pareto optimal solutions and compete with NSGA-II and MOSA.

Multi-layer ceramic capacitors (MLCC) are extensively used as important components of various electric and electronic products. Generally, electronic consumer products, such as mobile phones and digital TVs, contain at least 150 MLCCs, which are manufactured in batches of millions of units. Automated inspection machines have replaced manual optical inspection of MLCC units. To improve the quality of optical inspection is this manual operation, the Taguchi method is used to formulate an experimental layout of machines using five important parameters that describe practical manufacturing processes. In this study, not only is the parameter design for machines that automatically inspect MLCC optimized, but also significant parameters that influence the quality of optical inspection of the MLCCs are obtained. Experimental results illustrate the effectiveness of the method.

This study presents the implementation of a Flight Operations Risk Assessment System (FORAS) for an airline company, as well as a decision support tool for identifying the factors that critically determine the risk of a flight. The FORAS risk model is a hierarchical tree structure that breaks down the concerned operation risk to subcomponents and risk factors. The relation between a risk and its subcomponents is described by a fuzzy inference system. The use of fuzzy inference systems enables the quantification of qualitative risk assessments by domain experts. The inference of the operation risk is obtained through approximate reasoning. Algorithms are developed to identify critical risk factors based on the concept of the sensitivity of a risk factor and a heuristic search. Experiments based on practical data are conducted to evaluate the validation and performance of the FORAS model.

Modern societies are strongly dependent on the continuous and efficient operation of electric power systems as one of the critical infrastructures. Besides, information and communication systems play a crucial role in resiliency enhancement of the power system. As power communication systems are vulnerable against physical and cyber challenges, these systems can be an internal source of threats for power grids by themselves. Therefore, there is a need to identify and study the threats and weaknesses of power communication systems using a comprehensive framework. This framework helps power communication network planners evaluate all challenges and their numerous effects on the system, as a very important step in designing such systems. In the present paper, we propose such a framework by introducing the concept of ‘resiliency matrix’. In this regard, the resiliency of two alternative network plans, both of which are the solutions of a multi objective optimal design problem, is evaluated and compared using the proposed framework. The results reveal that the defined framework is capable to enhance network resiliency and thus can be used as a complementary step to design optimal and robust power communication networks.

Scheduling of a project is one of the most important factors, which has great effects on a project success. In the real world atmosphere, project scheduling problems involve multiple objectives which must be optimized simultaneously. According to the literature, several meta-heuristic algorithms have used single objectiveresource-constrained project scheduling problem,while very few of them have used a multi-objective framework. In this study, we focus on a multi-objective resource-constrained project scheduling problem by minimizing project completion time and time value of project costs, which is the main contribution of the current research. The goal is to provide an algorithm that canfind the optimum Pareto front solutions, using a multi-objective Cuckoo optimization algorithm. In order to increase the efficiency of the algorithm, the algorithm parameters are tuned using Taguchi tests. Finally, the solutions derived from the algorithm have been compared to the ones obtained from NSGA-II. The experimental shows the efficiency of the proposed algorithm.

It’s for a long time that the Economic Order Quantity(EOQ) model has been successfully applied to inventory management. This paper studies a multiproduct EOQ problem in which the defective items will be screened out by %100 screening process and will be sold after the screening period. Delay in payment is permissible though payment should be made during the grace period and the warehouse capacity is limited. Otherwise, there will be an additional penalty cost for late payment so the retailer would not be able tobuy products at discount prices.All-units and incremental discounts are considered for the products which dependon the order’s quantity just like the permissible delay in payment. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to solve the proposed model and numerical examples are provided for better illustrations.

In this paper, we study the resource investment problem (RIP) in which the availability levels of the resources are considered as decision variables. The objective is to maximize the net present value of a project by a given project deadline subject to progress payments. The project has activities interrelated by generalized precedence relations (GPR’s), which require a set of renewable resources. A non-linear mixed integer programming formulation is proposed for the problem. The problem formed in this way is an NP-hard one leading us to use modified version of SA (MSA) algorithm in order to obtain a satisfying solution based on hybridizing it with a local search procedure. In order to improve the MSA, Taguchi technique executed to tune its parameters.Moreover, genetic algorithm (GA) is also applied to validate the performance of the proposed algorithm. Finally, for examining the algorithms performance, the relative percent deviation (RPD) index is applied for comparison. The results of the performance analysis of the proposed MSA show efficiency of the presented algorithm.