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Showing 4 results for Two Stage

Mahdi Bashiri, Hamidreza Rezaei,
Volume 24, Issue 1 (2-2013)
Abstract

In this paper, we propose an extended relocation model for warehouses configuration in a supply chain network, in which uncertainty is associated to operational costs, production capacity and demands whereas, existing researches in this area are often restricted to deterministic environments. In real cases, we usually deal with stochastic parameters and this point justifies why the relocation model under uncertainty should be evaluated. Albeit the random parameters can be replaced by their expectations for solving the problem, but sometimes, some methodologies such as two-stage stochastic programming works more capable. Thus, in this paper, for implementation of two stage stochastic approach, the sample average approximation (SAA) technique is integrated with the Bender's decomposition approach to improve the proposed model results. Moreover, this approach leads to approximate the fitted objective function of the problem comparison with the real stochastic problem especially for numerous scenarios. The proposed approach has been evaluated by two hypothetical numerical examples and the results show that the proposed approach can find better strategic solution in an uncertain environment comparing to the mean-value procedure (MVP) during the time horizon.
Parviz Fattahi, Seyed Mohammad Hassan Hosseini, Fariborz Jolai, Azam Dokht Safi Samghabadi,
Volume 25, Issue 1 (2-2014)
Abstract

A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and ready in the second stage and finally the parts are assembled in an assembly stage to produce the products. Two objective functions are considered that are: (1) to minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (∑_i▒(E_i∕T_i ) . Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that performance of the proposed algorithms is good in both efficiency and effectiveness criterions.
Dr. Mustafa Jahnagoshai Rezaee, Dr. Alireza Moini,
Volume 26, Issue 4 (11-2015)
Abstract

Data envelopment analysis (DEA) and balanced scorecard (BSC) are two well-known approaches for measuring performance of decision making units (DMUs). BSC is especially applied with quality measures, whereas, when the quantity measures are used to evaluate, DEA is more appropriate. In the real-world, DMUs usually have complex structures such as network structures. One of the well-known network structures is two-stage processes with intermediate measures. In this structure, there are two stages and each stage uses inputs to produce outputs separately where the first stage outputs are inputs for the second stage. This paper deals with integrated DEA and game theory approaches for evaluating two-stage processes. In addition, it is an extension of DEA model based on BSC perspectives. BSC is used to categorize the efficiency measures under two-stage process. Furthermore, we propose a two-stage DEA model with considering leader-follower structure and including multiple sub stages in the follower stage. To determine importance of each category of measures in a competitive environment, cooperative and non-cooperative game approaches are used. A case study for measuring performance of power plants in Iran is presented to show the abilities of the proposed approach.

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Fatemeh Bayatloo, Ali Bozorgi-Amiri,
Volume 29, Issue 4 (12-2018)
Abstract

Development of every society is incumbent upon energy sector’s technological and economic effectiveness. The electricity industry is a growing and needs to have a better performance to effectively cover the demand. The industry requires a balance between cost and efficiency through careful design and planning. In this paper, a two-stage stochastic programming model is presented for the design of electricity supply chain networks. The proposed network consists of power stations, transmission lines, substations, and demand points. While minimizing costs and maximizing effectiveness of the grid, this paper seeks to determine time and location of establishing new facilities as well as capacity planning for facilities. We use chance constraint method to satisfy the uncertain demand with high probability. The proposed model is validated by a case study on Southern Khorasan Province’s power grid network, the computational results show that the reliability rate is a crucial factor which greatly effects costs and demand coverage. 

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