International Journal of Industrial Engineering & Production Research

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  Improving knowledge worker productivity has been one of the most important tasks of the century. However, we have few measures or management interventions to make such improvement possible, and it is difficult to identify patterns that should be followed by knowledge workers because systems and processes in an organization are often regarded as a death blow to creativity. In this paper, we seek to present a method for prediction of Knowledge worker productivity (KWP) that it must be capable of predicting the productivity of the knowledge workers in a one year period of time based on the Fuzzy cognitive maps (FCM) technique Based on Real Coded Genetic Algorithm (RCGA) , as well as presenting the best option from among different options as the knowledge workers’ productivity improving strategy (suggesting solution), based on the results gained from this and the previous section and depending on the requirements. The validity of the suggested model will be tested in an Iranian Company .

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Generally, human resources play an important role in manufacturing systems as they can affect the work environment. One of the most important factors affecting the human resources is being an interactional interest among the workers in the workshops. If the workers in a manufacturing cell have the highest surface of the interactional interest level, it causes a significant raise in coordination and cooperation indicators and in long time periods. In this paper, a new concept of being an interactional interest between workers in a manufacturing cell besides the ability to work with its machines is proposed and a bi-objective mathematical model to carry out this new point of view in cellular manufacturing systems is presented. Applying the ε-constraint method as an optimization tool for multi-objective mathematical programming, a comprehensive numerical example is solved to exhibit the capability of the presented model.

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Nowadays, the necessity of manufacturers’ response to their customers’ needs and their fields of activities have extended widely. The cellular manufacturing systems have adopted reduced costs from mass-production systems and high flexibility from job-shop manufacturing systems, and therefore, they are very popular in modern manufacturing environments. Manufacturing systems, in addition to proper machinery and equipment, workforces and their performance play a critical role.

Staff creativity is an important factor in product development, and their interest in cooperating with each other in the work environment can help the growth and maturity of this factor. In this research, two important aspects of cellular manufacturing take into consideration: Cell formation and workforce planning. Cell formation is a strategic decision, and workforce planning is a tactical decision. Practically, these two sectors cannot be planned simultaneously, and decision making in this regard is decentralized. For this reason, a bi-level mathematical model is proposed. The first level aims to reduce the number of voids and exceptional elements, and the second level tends to promote the sense of interest between the workforces for working together, which will result in synergy and growth of the organization.

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In many real scheduling situations, it is necessary to deal with the worker assignment and job scheduling together. However, in traditional scheduling problems, only the machine is assumed to be a constraint and there isn’t any constraint about workers. This assumption could be due to the lower cost of workers compared to machines or the complexity of workers' assignment problems. This research proposes a flexible flow shop scheduling problem with two simultaneous issues: finding the best worker assignment, and solving the corresponding scheduling problem. We present a mathematical model that extends flexible flow shop scheduling problem to admit the worker assignment. Due to the NP-hardness of the research problem, two approximation approaches based on particle swarm optimization, named PSO and SPSO, are applied to minimize the makespan. The experimental results show that the proposed algorithms can efficiently minimize the makespan but the SPSO generates better solutions especially for large-size problems.

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Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.