Developing new products has received much attention within the last decades. This issue can be highlighted for strategic innovations, in particular. Recently, knowledge-based networks have been introduced in order to facilitate the affair of transforming knowledge into commercial products which can be regarded as a set of research centers, universities, knowledge intermediaries, customers, and so forth. However, there is a wide range of risk factors that are liable to affect the chain performance. Hence, this paper aims to consider the most influencing criteria that can play a more significant role in achievements of such networks. To do so, DEMATEL has been applied to take the relationships between the risk factors into account. Moreover, fuzzy set theory has been utilized in order to deal with the linguistic variables. Finally, the most important factors are identified and their relations are determined.
The maximal covering location problem (MCLP) seeks to locate a predefined number of facilities in order to maximize the number of covered demand points. In a classical sense, MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations are extended: the gradual cover models, the cooperative cover models, and the variable radius models. In this paper, we develop a special form of MCLP which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed problem has many applications such as locating cell phone towers. The model is formulated as a mixed integer non-linear programming (MINLP). In addition, a simulated annealing algorithm is used to solve the resulted problem and the performance of the proposed method is evaluated with a set of randomly generated problems.
Today's competitive world conditions and shortened product life cycles have led to the rise of attention towards new product development issue which can guarantee both growth and survival of organizations. The agility of new product development is directed by the efficiency and efficacy of knowledge management skills of an organization. A key issue in thorough success of such networks is the developed knowledge preservation amongst the members. Thus, it is important that reliable relations can be established between the members in order to promote further interactions. To do so, an integrated framework is developed in this paper to configure the new product development network so that sustainable collaborations can be maintained amongst the entities. The proposed framework consists of the network configuration in addition to the supplier selection phase. They are taken into consideration using a biobjective mathematical model in which incurred costs and suppliers' superiority determine the final configuration of the network. Finally, different numerical instances are solved to address the applicability of the proposed model.
In this paper the problem of scheduling of n jobs on m non-identical parallel machines is considered. All jobs can be processed on all machines and the processing time and cost of each job depend on the machine on which the job is performed. Jobs cannot be split or divided and all jobs are available at time zero. The goal is to minimize cost which is composed of two parts: earliness-tardiness cost and production cost. The problem is formulated as a MILP model. A hybrid method based on simulated annealing and genetic algorithm is proposed to solve this problem. After parameter tuning of the algorithm, the proposed algorithm was tested on different combinations of jobs and machines and the results were compared with those obtained by genetic algorithm.
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