Railroad industry has received tremendous challenges in the world in terms of handling cost and efficiency. For many years, the railroad business lost money in many countries such as Japan until many governments decided to privatize the industry in an attempt to reduce the cost components and to increase the efficiency of various units, significantly. In this paper, we propose a new goal programming technique to handle two objectives of operating cost and the number of passengers travel by train. We consider different types of trains for public transportation of passengers in order to make the proposed model of this paper more realistic. The implementation of the proposed model is demonstrated using some numerical examples to show the effectiveness of the method.

Railroad transportation planning is strategically a long term and an important decision making problem especially in the area of travelling passengers. There have been literally various methods to use in order to provide optimum traveling schedule such as direct or indirect methods. Direct solutions involve the implementation of mixed integer programming, which is often hard to solve for real-world applications. The proposed model of this paper uses a column generation method to decompose a large-scale railroad passenger-scheduling problem into some smaller scale problems, which are easier to solve. The primary concern with the resulted problem is that final solutions of the method need to be integer and this is in contrast with convexity assumption of column generation techniques. We propose heuristic method to handle this problem and apply the proposed model for some examples. The preliminary results indicate that the proposed model of this paper could provide optimal solutions for small-scale problems and it can reach some reasonable solutions for larger problems when direct implementation fails to do in reasonable amount of time.

In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

Clustering plays an important role on developing industries, since business units can take advantage of many existing industries for trouble shooting or sharing their experiences to increase efficiency. One of the primary concerns for developing clustering is to identify and remove important barriers. In this paper, we gather experts' feedbacks on forming clustering in Iran's industries and, using analytical network process, we prioritize the important factors and provide some necessary guidelines to develop clustering. The results of this paper indicate that the existence of a supplier network is the most important factor, followed by the existence of competition between operational units, existence of high-risk investors, existence of suitable infrastructures. There are also other less important criteria including the existence of flexibility, suitable technology and competition, governmental regularities, social background, trust, etc.

Structure analysis plays an important role on increasing the efficiency of banking industry. A change in structure of business in this industry could change the profitability, significantly. The proposed study of this paper aims at structural analysis and comparative advantages for a governmental bank in a province of Semnan, Iran called Melli bank. The proposed study considers the effectiveness of four saving plans including current, loaned with no interest, short and long term based in three periods. It applies the change of location-share model, which includes provincial growth influence, structural growth and competitive growth as the determinant variables in saving rate. Findings indicate the disproportionate growth of saving rate in the Melli bank toward the other banks of the province in every 3 terms of the study. The research results show that short time saving in 2001 and 2004, loaned without interest and short term savings in 2001 and 2008, loaned without interest and long term savings in 2004 and 2008 had negative structural changes which indicate that the combination of discussed saving wasn’t desirable, but other savings benefits of desirable combination.

The idea of Science and Technology parks has been widely used in many countries and many innovative plans are initiated in these places. In this paper, we present a multiple criteria decision making plan to prioritize important factors influencing the success of an innovative plan. The study uses fuzzy triangular numbers to gather decision makers & apos; feedback on different criteria and rank them using fuzzy VIKOR method. The results of our survey indicate that it is important to be the first who introduce the idea, the idea must be important for customers and market and it must be scientifically justified. Among other important criteria, the availability of experts to support the ideas and applicability in the area of human and social science are of important criteria. The plan also does not need to have huge amount of investment and a complicated technology and finally it should come to market in short amount of time.

Just in time is one of the most important components of having efficient production plan. The primary objective of JIT is to reduce the amount of storage as much as possible to remove the cost of inventories and work-in-progress. However, the implementation of JIT is a tedious task especially in developing countries, where there are shortages for essential infrastructures and increase the risk of production in case of JIT adaptation. In this paper, we present a survey to detect important factors preventing JIT implementation in Iran. The proposed study designs a questionnaire for assessing important factors influencing JIT adaptation, which includes inflation, political sanction, unreliable suppliers, shortage of cash flow and weak organizational culture. The results of our survey confirm that shortage of cash flow is the most important factor, follows by economical sanction and inflation.

Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Road construction projects are considered as the most important governmental issues since there are normally heavy investments required in such projects. There is also shortage of financial resources in governmental budget, which makes the asset allocation more challenging. One primary step in reducing the cost is to determine different risks associated with execution of such project activities. In this study, we present some important risk factors associated with road construction in two levels for a real-world case study of rail-road industry located between two cities of Esfahan and Deligan. The first group of risk factors includes the probability and the effects for various attributes including cost, time, quality and performance. The second group of risk factors includes socio-economical factors as well as political and managerial aspects. The study finds 21 main risk factors as well as 193 sub risk factors. The factors are ranked using groups decision-making method called linear assignment. The preliminary results indicate that the road construction projects could finish faster with better outcome should we carefully consider risk factors and attempt to reduce their impacts.

The generalized assignment problem (GAP) is a unique extended form of the Knapsack problem, which is tremendously practical in optimization fields. For instance, resource allocation, sequencing, supply chain management, etc. This paper tackles the GAP in uncertain environment in which the assignment costs and capacity of agents are fuzzy numbers. Two models are presented for this problem and a novel hybrid algorithm is offered using simulated annealing (SA) method and max-min fuzzy in order to obtain near optimal solution. Computational experiments validate the efficiency of proposed method.