One of the most important techniques on improving customer satisfaction in clothing and textile industry is to increase the quality of goods and services. There are literally different methods for detecting important items influencing clothing products and the proposed model of this paper uses quality function deployment (QFD). The proposed model of this paper designs and distributes a questionnaire among some experts to detect necessary factors and using house of quality we determine the most important factors impacting the customer & apos; s clothing selection. The proposed study of this paper focuses men who are 15 to 45 years old living in Yazd/Iran. The brand we do the investigation sells the products in three shopping centers located in this city. We have distributed 100 questionnaires and collected 65 properly filled ones. Based on the results of our survey, suitable design, printing and packaging specifications, necessary requirements, optimization of production planning and appropriate sewing machine setting are considered as the most important characteristics influencing the purchase of a clothing products.

Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.

The world trade has tremendous growth in marine transportation. This paper studies yard crane scheduling problem between different blocks in container terminal. Its purpose is to minimize total travel time of cranes between blocks and total delayed workload in blocks at different periods. In this way the problem is formulated as a mixed integer programming (MIP) model. The block pairs between which yard cranes will be transferred, during the various periods, is determined by this model. Afterwards the model is coded in LINGO software, which benefits from branch and bound algorithm to solve. Computational results determine the yard cranes movement sequence among blocks to achieve minimum total travel time for cranes and minimum total delayed workload in blocks at different planning periods. Also the results show capability and adequacy of the developed model.

We consider a capacitated facility location problem (CFLP) which contains a production facility and distribution centers (DCs) supplying retailers' demand. The primary purpose is to locate distribution centres in the network and the objective is the minimization of the sum of fixed facility location, pipeline inventory, safety stock and lost sales. We use Greedy randomized adaptive search procedures (GRASP) to solve the model. The preliminary results indicate that the proposed method of this paper could provide competitive results in reasonable amount time.

One of the most important strategic issues for any enterprise is to create
and deliver superior value for its customers and provide enough
evidences to convince them not to choose another competitive. In this
study, we study the effects of customer value, customer satisfaction and
customer loyalty on customer retention. The results indicate that all
aspects of the customer value have positive relationships with customer
satisfaction but only emotional value has a positive and direct
relationship with customer loyalty. The study confirms that while none
of customer value aspects has direct and positive relationship with
customer retention, both customer satisfaction and customer loyalty
positively and directly affect customer retention.