جستجوی مقالات مرتبط با کلیدواژه "ii)" در نشریات گروه "مدیریت"

New Product Development (NPD) is one of the key factors for achieving competitive advantage and maintaining firm growth. Therefore, given the importance of this type of projects, this paper is an endeavor to make these projects more successful by effectively managing their risks in the conceptual design phase of new product development. For this purpose, a multi-objective model, with three objective functions, including risk, overall effectiveness of the design and cost, has been developed in this study. Among the innovations and features of this model, one can refer to its considering the mutual effect of risks to the outcomes of one another, as well as its taking into consideration the risks associated with the continuous design variables in calculating the risk measure of product design, which will ultimately lead to an increase in the accuracy of risk measure calculation for each product design.

Resource Constrained Project Scheduling Problem (RCPSP) is the most general scheduling problem. Job shop scheduling, flow shop scheduling and other scheduling problems are the subsets of RCPSP. The present paper examines the multimode multi-objective resource constrained project scheduling problem (RCPSP) with partial precedence relations. To enhance the practical aspects of this prominent problem, important practical purposes including minimizing the completion time of the project, maximizing the quality of project activities and minimizing the total cost of the project were considered. After validation of the model using the Bees Algorithm, the proposed multi-objective model was solved. The results obtained from the proposed model were compared with those obtained from NSGA-II. The results demonstrated the good performance of the proposed algorithm in solving RCPSPs.

One of the organizations’ fundamental issues is supply chain network design. Optimization of this network can lead to effective management of the whole supply chain. Network design specifies the position، capacity، number and type of network facilities، and transportation network of materials and products from the supplier to the customer and vice versa. This research proposes new solution procedure based on Multi-objective Genetic Algorithm (MOGA) and Non-dominated Sorting Genetic algorithm-II (NSGAII) to find the set of Pareto optimal solutions that empowers the decision-makers by alternative solutions. Considering that in this study the level of service is very important، so this modeling was based on satisfying all customer demands. Objectives for network optimization are minimization of total cost and maximization of capacity utilization balance for network facilities that lead to the reduction of customers’ service time (increase service levels). Nine problems were designed from small to large. In order to compare the quality of the obtained Pareto solutions of both algorithms، seven criteria (for multi-objective problems) were used in this study. The results indicated that the solutions produced by NSGAII algorithm have higher quality.