Forecasting the Effect of Renewable Energy Consumption on Economic Welfare: Using Artificial Neural Networks

Energy as a production process input has an effective role on economic indicators such as gross domestic production (GDP). Limitations in fossil fuel and nuclear energy sources urge utilizing renewable energies. In this paper, the impact of renewable energy consumption on economic welfare indicators (i.e. GDP, GDP per capita, annual income of urban households, and annual income of rural households) is investigated. For this purpose, 41 annual data sets are collected, from 1971 to 2011, mostly from Iran’s Statistical Yearbook and Iran’s Balance Sheet. Artificial neural networks (ANNs) are used for forecasting the effect of renewable energy consumption on economic welfare indicators. Advantages in using the proposed ANN-based method are demonstrated by comparing its results with the multi-layer regression (MLR) model. The comparison between the artificial neural network and the multi-layer regression model demonstrates that the artificial neural network has more accurate results than the multi-layer regression model. Both ANN and MLR models show significant effect of using renewable energies on the economic welfare. Results demonstrate the importance of using the proposed model for policy makers in implementing new policies for renewable energies. The ANN prediction results show that GDP, GDP per capita, annual income of urban households, and annual income of rural households will grow by 35.63%, 62.59%, 167.61% and 143.19%, respectively, from 2007 to 2016

Optimization of multi-product, multi-period closed loop supply chain under uncertainty in product return rate: case study in Kallehdairy company

Abstract Closed Loop production systems attempt to economic improvement, deliver goods to customers with the best quality, decrease in the return rate of expired material and decrease environmental pollution and energy usage. In this study, we solve a multi-product, multi-period closed loop supply chain network in Kalleh dairy company, considering the return rate under uncertainty. The objective of this paper is to develop a supply chain model including raw material suppliers, manufacturers, distributors and a recycle center for returned products. Solving this model helps us to make a good decision about providing materials, production, distribution and recovery. Our basic goal is to estimate optimum return rate of some products such as yoghurt, to production cycle. Once the products pass 3 4 of their shelf life, they are returned to production cycle. For this study, we develop a linear programming model with a consideration of chance constraints. Finally, this model is implemented by Lingo software with using real data. The obtained results by our model show 9.5 % decrease for total cost in comparison with the current status

A Three-Echelon Multi-Objective Multi-Period Multi-Product Supply Chain Network Design Problem: A Goal Programming Approach

In this paper, a multi-objective multi-period multi-product supply chain network design problem is introduced. This problem is modeled using a multi-objective mixed integer mathematical programming. The objectives are maximizing the total profit of logistics, maximizing service level, and minimizing inconsistency of operations. Several sets of constraints are considered to handle the real situations of three-echelon supply chains. As the optimum value of conflictive objective functions of the proposed model cannot be met concurrently, so a goal programming approach is used. An illustrative numerical example is provided to show the mechanism of proposed model and the solution procedure. In a numerical example, 1 manufacture, 2 warehouses, 2 distribution centers (DCs), and 2 types of final products are considered in a planning horizon consists of 3 time periods. Products are shipped from the manufacturer to warehouses and then are shipped from the two warehouses to two distribution centers. The distribution centers are the point from which the product are shipped to final consumers. The Model is coded using GAMS software on a Core i7 CPU, using 8GB of RAM with MS-Windows 8.0. The optimum design of supply chain, inventory level for warehouses and distributors, and amount of shipments between echelons are determined

A Comparative Study of Multi-Objective Multi-Period Portfolio Optimization Models in a Fuzzy Credibility Environment Using Different Risk Measures

The purpose of the present research is to compare portfolio optimization models in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Value at Risk (VaR), Average Value at Risk (AVaR) and semi Entropy. In order to get closer to the real world investment model, while allowing for transaction costs and investing part of wealth in risk-free assets, in addition to the cardinal constraints, other constraints including the minimum and maximum amount of wealth assigned to each asset, and the minimum and maximum number of stocks present in portfolio were applied. The results of the multi-period models running by MOPSO algorithm indicated for the models Mean-AVaR, Mean-Semi Entropy, and Mean-VaR, respectively, performed better, in terms of Sharp and Treynor measures

A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains

Data envelopment analysis (DEA) is a linear programming method for assessing the efficiency and productivity of organizational units called decision-making units (DMUs). We propose a new network DEA (NDEA) model for measuring the performance of agility in supply chains. The uncertainty of the input and output data is modeled with linguistic terms parameterized with fuzzy sets. The proposed fuzzy NDEA model is linear and independent of the α-cut variables. The linear feature allows for a quick identification of the global optimum solution and the α-cut independency feature allows for a significant reduction in the computational efforts. We show that our model always generate solutions within a bounded feasible region. Our model also eliminates the potential for conflict by producing unique interval efficiency scores for each DMU. The proposed model is used to measure the performance of agility in a real-life case study in the dairy industry

A Comprehensive Framework for Sustainable Project Portfolio Selection Based on Structural Equation Modeling

Project selection problems are inherently complex problems with multiple and often conflicting objectives. The complexity of project selection problems is due to the high number of projects from which a subset (portfolio) has to be chosen. Various analytical methods, ranging from the simple weighted sum to complex mathematical programming have been proposed to solving these problems. We propose an integrated approach for strategic and sustainable project portfolio selection, which is composed of two distinct but interrelated modules. In the first module, we use the strategic planning and sustainability concepts to select a set of promising projects. In the second module, we use a project portfolio selection procedure to choose among the promising projects identified in the first module. A structural equation model is used to analyze and explain the relationships among different factors in the proposed framework. More specifically, we investigate the effects of: (1) strategic level performance on sustainability, post-implementation, and overall performance; (2) implementation performance on post-implementation and overall performance; (3) portfolio selection performance on implementation and overall performance; and (4) post-implementation performance on overall performance. A case study in investment banking is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms

A new two-stage Stackelberg fuzzy data envelopment analysis model

Data Envelopment Analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of Decision Making Units (DMUs). Conventional DEA methods treat DMUs as “black boxes”, focusing entirely on their relative efficiencies. We propose an efficient two-stage fuzzy DEA model to calculate the efficiency scores for a DMU and its sub-DMUs. We use the Stackelberg (leader–follower) game theory approach to prioritize and sequentially decompose the efficiency score of the DMU into a set of efficiency scores for its sub-DMUs. The proposed models are linear and independent of the α-cut variables. The linear feature allows for a quick identification of the global optimum solution and the α-cut independency feature allows for a significant reduction in the computational efforts. Monte Carlo simulation is used to discriminately rank the efficiencies in the proposed method. We also present a case study to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a two-stage performance evaluation problem in the banking industry

Selecting the most appropriate maintenance strategies using fuzzy Analytic Network Process: A case study of Saipa vehicle industry

It is necessary for companies and industries to select the most appropriate maintenance strategy to increase the reliability and safety level with reasonable cost. The primary objective of this paper is to assess different maintenance strategies and to select the best and the most appropriate alternatives for Saipa vehicle industry in Tehran, Iran. For this purpose, we simultaneously consider numerous conflicting objectives and constraints. In this study to counter with this conflicting and to consider the dependency among the qualitative and quantitative criteria and sub-criteria, an integration of Analytic Network Process (ANP) and fuzzy set theory are considered. Therefore, factors playing important role in selecting the best maintenance strategy are determined by reviewing the research literature and interviewing with the experts by Delphi technique. Considering the relations among different factors, a network with 4 criteria and 28 sub-criteria are proposed. In the next step, ANP technique is applied for ranking effective factors in evolution of appropriate maintenance strategy. Results reveal that the best maintenance strategy for fixture body of pride (setter) is corrective maintenance

Reverse Logistics and Supply Chains: A Structural Equation Modeling Investigation

The process of transforming rawmaterials into final products and delivering those products to customers, knownas supply chain (SC) management, is becoming increasingly complex. Most of SCmanagement research has been concerned with procurement and production. However, recently,  it has becomeincreasingly important to extend SC issues beyond the point of sale to reverselogistic (RL) where the flow of returned products is processed from thecustomers back to the collection centers for repair, remanufacturing ordisposal.  We propose a conceptual frameworkand empirically investigate the relationship between the key factors in RL and SCperformance measurement using a series of hypotheses. Structural equation modeling(SEM) is used to test the hypotheses. The results reveal insightful informationabout the effects of RL factors on the SC performance