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Complexity of assembly supply chains (ASCs) is a challenge for designers and managers, especially when ASC systems become increasingly complex due to technological developments and geographically various sourcing arrangements. One of the major challenges at the early design stage is to make decision about an appropriate configuration of ASC. This paper addresses modeling and measuring the structural complexity of ASC networks in order to establish a framework obtaining the optimal ASC configuration. Considering relationship between supply chains and assembly systems, structural complexity measures for ASC network and assembly lines inside the network are developed based on Shannon's information entropy. This complexity model can be used to configure supply chain networks and assembly systems with robust performance. In order to generate different feasible configurations of ASCs, a four-step algorithm is proposed considering assembly sequence constraint. Finally, the optimal ASC network is obtained by comparing the total complexity values of the feasible configurations.

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Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA, however the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit for the period of 2014–2019.

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This article investigates how to simultaneously optimize both strategic and tactical decisions in the supply chain network design. For this purpose, a bi-level programming model is developed in which supply chain network design problem is considered as a strategic decision in the upper-level model, while the lower-level model contains the assembly line balancing as a tactical decision. In addition, the problem is extended to include push-pull strategy where decisions such as production amount and inventory level of each component in manufacturers are made. Based on the special structure of the model, a heuristic method is proposed to solve the developed bi-level model. A numerical example is employed to show the performance of proposed method in terms of feasibility and convergence. Finally, computational experiments on several problem instances are presented to demonstrate the applications of developed model and the solution method. KeywordsSupply chain network design, assembly line balancing, bi-level programming model, heuristic method, push-pull strategy Date

Effective energy planning and governmental decision-making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two artificial neural network (ANN) models, two regression analysis models, and one autoregressive integrated moving average (ARIMA) model are developed based on the historical data from 1950 to 2013. While ANN model 1 and regression model 1 use gross domestic product (GDP), gross national product (GNP), and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP, and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA; however, the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed that the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit (btu) for the period of 2014–2019. In addition, we have discussed the possibility of self-sufficiency of the United States in terms of energy generation based on the information of current available technologies nationwide.