Journal of Advances in Industrial Engineering
Volume:45 Issue: 2, 2012

This paper introduces a new concept in location problems, called "Gravity-Based Equitable Location Problem". Equity is seeking for a fair distribution of demands or balancing the facility capacity to meet demands, so that the customers select them by appropriate criteria like the gravity law. The objective function defined as minimizing the maximum of facilities workload and deployment and movement costs regarding to the gravity model. Then, a heuristic algorithm designed by a problem structure and comparative analysis accomplished with the exact method on appropriate numerical examples. The proposed algorithm yields near optimal solutions for randomly generated examples. Results show that it is very efficient, with the mean difference of approximately 6 percent and acceptable computational time

Vehicle Routing Problem (VRP) is one of the major problems in the transportation and distribution planning. In the most previous studies, the objective of VRP models was distance and vehicle related costs. However in many industrial cases along with routing distance, vehicle loading amount is a factor of cost function. In this paper, we formulate a mixed integer non-linear programming (MINLP) model for heterogeneous vehicle routing problem in which problem objective has nonlinear relation with routing distance. Then by analytical methods we reformulate the model as a mixed integer programming (MIP). In this model, at the first transportation cost rate is determined by step function. Then cost of each vehicle calculated by multiplying the transportation cost rate to its loading amount. Similar to other VRP problems proposed model is also NP-hard. We develop constructive heuristic algorithm to obtain an approximate solution for this problem. This algorithm is developed based on creating a traveling salesman problem (TSP) tour and partitioning it into vehicle routs by heuristic methods. We name proposed algorithm as Salesman Rout Partitioning for Vehicles (SRPV). In order to evaluation the effectiveness of SRPV algorithm we design 54 experiments in four scenarios. In one hand, lower and upper bounds for these experiments have been obtained by commercial optimization software Cplex 12.2. Besides, proposed heuristic are programmed and compiled using Matlab 2010. Furthermore effectiveness of SRPV algorithm is investigated by two measures, difference percentage and complexity percentage. Our findings indicate that SRPV algorithm sufficiently effective as constructive heuristic for considered type of vehicle routing problem. Moreover, to demonstrate the practicality of proposed model and solution heuristic, we study an industrial case at FERGAZ Company. This company charges gas cylinders and distributes them among geographically dispersed customers. By using Cplex 12.2 we couldn’t find any feasible solution for FERGAZ’s problem, but approximate solution could be found by heuristic algorithm.

Earned Value Management (EVM) is the process of integrating the time and cost management within the framework of project scope management. The earned value has provided methods for predicting the cost for projects. In large part, these methods have not been improved upon since their beginnings and remain unsubstantiated as to accuracy. In this direction, several mathematics formulas have been developed by a number of researchers. However, there is no agreement on the usage of the particular formula for all the projects. In addition, the estimation of the final cost of project has been emphasized by all previous studies and it was no attention made to the time frames of the project. On this base, the aim of this research is to complete and expand the completion cost forecasting methods of a project and improve the capability of project managers for making informed decisions by providing a reliable forecasting method of the costs. In this paper, the cost completion forecasting methods are divided into two general categories, namely Performance Index Methods and Regression and Time Series Methods. Regression models are established on the basis of linear relationship between some EV parameters. For models comparison, forecasting errors (e.g., MAPE, MSE, MA, increasing and decreasing trends of error percentage value in different periods, R2, analysis of variance and comparative analysis) are used. Some of regression models have shown the reliable results. In order to determine the best cost forecasting method by utilizing the real data from four different projects with different criteria, the fore-mentioned methods are employed.

The planning of production and control of inventory problem is one of most important problems that companies are face with them. Some times inattention to uncertainty in these problems causes to increase of costs of inventory control systems. One of the important ways encountering to uncertainty is the widespread of fuzzy sets instead of crisp numbers because in this approach, we can determine model parameters as interval numbers. In this paper, we develop an economic order quantity (EOQ) model under a one-time-only price increasing that all variable and parameters are triangular fuzzy numbers, to find out the optimal solution of above model, we use three different methods such as α-cuts method, Vujosevic method (defuzzification of internal parameters before solving model and difuzzification of external parameter after solving model). Under first policy, we integrate α-cuts method and non-linear programming problems method to reach to optimal solution. In first methodology, we use α-cuts approach and parametric non-linear programming technique simultaneously to attain the membership function of external parameters in primary model. These parameters are reached from internal parameters in two phases maximum and minimum non-linear programming problems and this methodology represents the external parameters as an approximated fuzzy number. Under another two policies, we use defuzzification technique via centroid method to attain the crisp numbers. The optimal order policies association with three methods is compared as a benchmark approach and numerical computations shows that efficiency of first method is better than two another methods considerably. In fact the first method chooses the optimal and attractive strategies by membership function allocating to different α-cuts and gives great information to DMs to decide and select the best strategies. There methods have been validated with illustrating numerical example. The important target of this model solution is determination of special ordering range, net costs saving quantity (involving ordering, holding and purchasing cost) and finally we will calculate the time of ordering if net costs saving are positive.

This research, for the first time presents the Flexible Flow Shop model of scheduling method for considering runway assignment and operations planning together. One of the advantages of the developed model is considering the procedures of air routes in terminal airspace and separation between consecutive aircraft which is very similar to the real world condition. There are different objective functions in airport literature, then a dual criteria model offers for this problem and solve with meta-heuristics SA (simulated annealing) algorithm. This approach can be used as a decision aiding tool, delay reduction, and improving the available runway throughput.

Many researches have depicted there is no significant and positive correlation between IT and firm level performance, called productivity paradox, so as successful investment on IT depends on taking into account the role of complementary assets such as business processes and organizational infrastructures. On the other hand, since there are not enough resources to invest on all these assets, the investment priorities of this context should be determined. In this paper, a novel system for performance assessment of information technology is proposed which uses neural networks to determine the investment priorities of complementary assets. To study the proposed system in practice, it is used as a decision support system to determine the investment priorities on information technology and its complementary assets for 102 Iranian car part manufacturers, so as complementary assets are ranked based on their contributions on firm performance.

One of the most important issues in the supply chain management is reducing the Bullwhip effect. The bullwhip effect is the increasing of the fluctuation of demands from customer toward supplier in a supply chain. The reasons for this effect and the solution for its elimination are studied by many researchers up to now. One of the main known reasons is variation of the time value of money that has not been studied in the previous researches. In this paper, at first we analysis the previous works about Bullwhip effect, and then we investigate the role of time value of money an inflation on this effect.

The three methods of Design of Experiment: Taguchi, Classic and Shainin can solve quality problem, diagnosis the roots of quality problem and make improvement. To use of these methods, we should recognize the strengths and weakness of them. Recognizing notes of these methods by comparison with a case study, we will understand them deeper. In this study three mentioned methods were compared after implementing, by quantitative and conceptual criteria (those criteria were scored by experts. ) The overall results showed that Classic method has priority for implementation. Although Shainin DOE had more flexibility and less complexity than taguchi method, Taguchi method scored more because it made more improvement and needed fewer experiment so it scored more than Shainin DOE.

Clustering technique is one of the most important techniques of data mining and is the branch of multivariate statistical analysis and a method for grouping similar data in to same clusters. With the databases getting bigger, the researchers try to find efficient and effective clustering methods so that they can make fast and real decisions. Thus, in this paper, we proposed an improved ant system-based clustering algorithm (IASC) in order to providing the fast clusters with high accuracy. The goal of clustering analysis is to group similar objects together. There are many methods being applied in clustering analysis, like hierarchical clustering, partition-based clustering, density-based clustering, and artificial intelligence-based clustering. The ant colony system (ACS) is one of the newest meta-heuristics for combinatorial optimization problems, and this study uses the ant colony system to find the clusters effectively. The IASC algorithm is including four sub-procedures, that is Divide, Agglomerate_obj, Agglomerate, and Remove. First, initialize the parameters and group all the objects as a cluster. And then the sub-procedure Divide will divide the cluster into several sub-clusters and some object which does not belong to any sub-clusters through the consistency of the pheromone and some criterion. After Divide, the Agglomerate_obj is the next step at this algorithm in order to agglomerate the objects into the suitable sub-cluster. Fourth, Agglomerate is the sub-procedure to merge the similar two sub-clusters into a cluster. And then run Agglomerate_obj again. Sixth, after agglomerating the similar object into the suitable sub-cluster, the Remove sub-procedure tries to remove the un-similar from sub-cluster. Calculate the total within cluster variance (TWCV). If TWCV is not changed, stop the procedure. Otherwise, repeat the sub-procedure Divide, Agglomerate_obj, Agglomerate, Agglomerate_obj, Remove until TWCV is not changed. The implementation results on the Iran earthquake data show that the proposed method is able to provide more accurate and fast clusters and to determine the outliers. The computational time is also reduced.

According to the limit of resources in the subject of prioritization, one of the alternative methods is MCDM method. Generally, MCDM models have been developed under certainty while we confront with under uncertainty in real world. In hierarchical MCDM methods, one of the main steps is to weigh criteria and computes each alternative weight using defined criteria in the next steps. One of the easiest and most common weighting criteria methods is to apply the comparison matrices. The main approach in this paper is use of interval comparison matrices which is more realistic than classic methods. In this paper, two MCDM models are provided respectively lexicographic goal programming (LGP) and two-stage logarithmic goal programming methods (TLGP) and used to prioritize investment plans. Such models are hierarchical methods developed in under uncertainty. At the end of this paper, a numerical example solved for each method and the results are compared with analytical hierarchy process (AHP) under certainty.