Abstract
In the previous paper of this series [Vasili et al., 2006. Comparison of different dwell point policies for SP-AS/RS. Int J of Eng and Tech, 3(1):91-106], two reliable travel time models for SP-AS/RS under return to middle and return to start, dwell point policies were developed and presented based on the continuous rack approximation approach. In this paper, the authors present an alternative configuration for the input/output (I/O) station in SPAS/ RS, in order to reduce average handling time of this system. A travel time model is developed for this new configuration and the accuracy of the model is validated by Monte Carlo simulation. The results of new model and previous models are compared and the best policy is introduced. The results show that in a range of shapes (when shape factor is less than or equal to 2), new configuration of SP-AS/RS is more preferable.

Abstract
Customers gain their own experiences and knowledge through using products or services. This knowledge is recognized as one of the most important competitive resources for the companies in recent years. However the models were introduced in the literature for customer knowledge management (CKM) focused on the human based interaction with customers to exchange the knowledge. The aim of this paper is to propose the applicability of agents in knowledge exchange between customers and companies. Autonomy, reactivity and pro activity of agents aid CKM to response the customers as quickly as possible and to customize their services for individuals. The abilities of agents cover the requirements of CKM models. An introductory model is proposed to show the abilities of agents in CKM system. A comparison between agents' abilities and CKM is provided as final goal of this paper. Copyright © (2008) by Computers & Industrial Engineering.

Abstract
In recent years optimization of fuzzy controllers has been widely considered by researchers, especially using genetic algorithms. Genetic supervisory fuzzy (GSF) control architecture for multi-part-Type production line is proposed in this paper. GSF control architecture composed of two layer controller. In first layer heuristic distributed fuzzy (HDF) controllers has been used to control each machine separately, while GSF controllers has been used in second layer. GSF controllers tune the decisions made by HDF controllers, based on overall conditions of production system. Genetic algorithm (GA) is used to adapt the membership functions of fuzzy supervisory controllers, to improve the performance of such controllers. The overall objective is to control the production rate in a way that satisfies the demand for final products while keeping minimum work-in-process (WIP) and backlog within the production system. GA is used to minimize costs of WIP and backlog. The results show that in most of the cases GSF outperform the conventional supervisory controllers. Copyright© (2008) by Computers & Industrial Engineering.

Abstract
Six sigma may be named as a powerful and comprehensive management tool which is necessary in order to bring changes in organization and to make conformity with customer needs. Despite of other quality approaches, six sigma is not only a method, but it contains a vision, a goal and a symbol. Beside the six sigma, symmetrical valuation method is interpreted as a tool to collect strategy. This system offers a method to converge occupation's activities with strategy, and also it offers a method in order to watch the operation of strategic goals during time. Undoubtedly, the first step in reducing the risk failure of the six sigma projects is to select the optimal choices. In this paper, the effectiveness of criteria in choosing six sigma projects are identified and defined. Moreover, a case study in Poly Acryl Corp is illustrated to demonstrate the effectiveness and practicality of the fuzzy multi-criteria decision making approach to select six sigma projects. In fact, we evaluated how selecting six sigma projects and goals using the fuzzy multi-criteria decision making technique affect the organization. © 2012 CIE & SAIIE.

Abstract
Conventionally, the containers are stacked in storage yards inside the container terminals. However, in recent years a new storage system called split-platform automated storage/retrieval system (SP-AS/RS) has been introduced in the literature. On the other hand, integrated scheduling of handling equipment has been introduced as an essential concept to improve the performance of the automated container terminals. Improving the performance of container terminals would result in reduction of seaborne trade costs. In this paper a mixed integer programming model is formulated for the integrated scheduling of handling equipment (including the SP-AS/RS) in automated container terminals. In the formulation of the problem, tasks of cranes are scheduled considering the availability of the vehicles. Moreover, the vehicles are scheduled in accordance with the SP-AS/RS operations. The integrated scheduling has been proved as an NP-hard problem. Thus, a simulated annealing algorithm is developed to find near optimal solutions for the problem in relatively low computational time. Using a set of numerical test cases it is justified that the proposed simulated annealing algorithm is suitable to solve the problem. Further analyses on the number of available vehicles are the final notes of this paper. © 2012 CIE & SAIIE.

Abstract
Complex production systems can produce more than one part type. For these systems, production rate and priority of production for each part type is determined by production controllers. In this paper, genetic fuzzy logic control (GFLC) methodology is used to develop two production control architectures namely "genetic distributed fuzzy" (GDF), and "genetic supervisory fuzzy" (GSF) controllers. Previously these controllers have been applied to single-part-type production systems. in the new approach the GDF and GSF controllers are developed to control complex production systems. The methodology is illustrated and evaluated using two test cases; two-part-type production line and re-entrant production systems. Genetic algorithm is used to tune the membership functions of input variables of GSF or GDF controllers. The objective function of the GSF controller minimizes the production cost based on work-in-process (WIP) and backlog costs, while surplus minimization is considered by GDF controller. The results show that GDF and GSF controllers can improve the performance of production systems. GSF controllers decrease the WIP level and its variations. GDF controllers show their abilities in reducing the backlog level but generally, production cost for GDF controller is greater than GSF controller.