A. M. Kimiagari; F. Lotfian Delouyi; M. Shabani
Volume 3, Issue 1 , June 2016, Pages 1-14
Abstract
In the recent years, renewable energy sources are an important component of world energy consumption. GDP is one of the main measures of a country’s economic activity. Most of the studies examine the impact of renewable energy consumption on GDP with single equation model and the others use dynamic ...
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In the recent years, renewable energy sources are an important component of world energy consumption. GDP is one of the main measures of a country’s economic activity. Most of the studies examine the impact of renewable energy consumption on GDP with single equation model and the others use dynamic panel data. Since the Granger causality analysis’s findings of this paper establish bidirectional causality between GDP and renewable energy consumption, the purpose of this study is to develop a simultaneous-equations model to explore the interaction between GDP and renewable energy consumption in a dynamic panel data. This model uses GDP and renewable energy consumption as endogenous variables and seven factors as exogenous variables. By using a dynamic panel data of 34 OECD countries from 1990 to 2012, the model is estimated by using the two-stage least-squares method. The results confirm the important influence of renewables and non-renewables as well as capital and labor force on GDP in OECD countries. Based on the results, both GDP and real oil price play an important role in renewable energy consumption. Our findings suggest that energy planners and policy makers need to increase renewable energy investment to ensure sustainable economic development in future.
M. Sayyah; H. Larki; M. Yousefikhoshbakht
Volume 3, Issue 1 , June 2016, Pages 15-38
Abstract
One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) ...
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One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) which includes insert, swap and 2-Opt moves for solving VRPSPD that is different with common ant colony optimization (ACO). ACO is a meta-heuristic algorithm inspired by the foraging behavior of real ants. Artificial ants are used to build a solution for the problem by using the pheromone information from previously generated solutions. An extensive numerical experiment is performed on 68 benchmark problem instances involving up to 200 customers available in the literature. The computational result shows that EACO not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms such as tabu search, large neighborhood search, particle swarm optimization and genetic algorithm for solving VRPSPD problems.
F. Gholian Jouybari; A. J. Afshari; M. M. Paydar
Volume 3, Issue 1 , June 2016, Pages 39-60
Abstract
In this paper, we consider the fuzzy fixed-charge transportation problem (FFCTP). Both of fixed and transportation cost are fuzzy numbers. Contrary to previous works, Electromagnetism-like Algorithms (EM) is firstly proposed in this research area to solve the problem. Three types of EM; original EM, ...
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In this paper, we consider the fuzzy fixed-charge transportation problem (FFCTP). Both of fixed and transportation cost are fuzzy numbers. Contrary to previous works, Electromagnetism-like Algorithms (EM) is firstly proposed in this research area to solve the problem. Three types of EM; original EM, revised EM, and hybrid EM are firstly employed for the given problem. The latter is being firstly developed and proposed in this paper. Another contribution is to present a novel, simple and cost-efficient representation method, named string representation. It is employed for the problem and can be used in any extended transportation problems. It is also adaptable for both discrete and continues combinatorial optimization problems. The employed operators and parameters are calibrated, according to the full factorial and Taguchi experimental design. Besides, different problem sizes are considered at random to study the impacts of the rise in the problem size on the performance of the algorithms.
E. Babaee Tirkolaee; M. Alinaghian; M. Bakhshi Sasi; M. M. Seyyed Esfahani
Volume 3, Issue 1 , June 2016, Pages 61-76
Abstract
The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making ...
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The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making slight improvements in this issue lead to a huge saving in municipal consumption. Some incidents such as altering the pattern of waste collection and abrupt occurrence of events can cause uncertainty in the precise amount of waste easily and consequently, data uncertainty arises. In this paper, a novel mathematical model is developed for robust capacitated arc routing problem (CARP). The objective function of the proposed model aims to minimize the traversed distance according to the demand uncertainty of the edges. To solve the problem, a hybrid metaheuristic algorithm is developed based on a simulated annealing algorithm and a heuristic algorithm. Moreover, the results obtained from the proposed algorithm are compared with the results of exact method in order to evaluate the algorithm efficiency. The results have shown that the performance of the proposed hybrid metaheuristic is acceptable.
F. Molavi; E. Rezaee Nik
Volume 3, Issue 1 , June 2016, Pages 77-88
Abstract
Resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. In this study, budget, investment costs and earnings are considered to be stochastic. ...
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Resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. In this study, budget, investment costs and earnings are considered to be stochastic. The objectives are maximizing net present values of selected projects and minimizing variance of them. Benefiting an efficient multi-objective approach to satisfy every conflicting objective, an integer non-linear goal programming model is developed. Another contribution of this paper is to consider cost dependency between the projects, in project portfolio selection and scheduling problem. Due to the complexity of this problem, especially in large sizes, imperialist competitive algorithm and genetic algorithm are presented. The effectiveness of the model and proposed algorithms are demonstrated via a case study in a knowledge based company at Ferdowsi University of Mashhad. The result shows high performance of the both proposed algorithms.
M. Zandieh
Volume 3, Issue 1 , June 2016, Pages 89-107
Abstract
This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based ...
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This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algorithms is that, KBNSGA-II has an additional learning module. Finally, we draw an analogy between the results obtained from algorithms applied to various test problems. The superiority of our KBNSGA-II, based on set coverage and mean ideal distance metrics, is inferred from results.