Journal of Advances in Industrial Engineering
Volume:47 Issue: 1, 2013

In this study، we present a MRCPSP/max (Multi-mode Resource-Constrained Project Scheduling Problem with Minimum and Maximum time lags) model with minimization tardiness costs and maximization earliness rewards of activities as objective. The proposed model is nearby to real-world problems and has wide applications in various projects. This problem is not available in the literature exactly and we developed it for the first time. In order to solve this problem، we developed a two-stage genetic algorithm. In the first stage، the main problem is simplified، through applying a genetic algorithm، in which each activity has only one executive mode. In the second phase، with developing another genetic algorithm، the best answer of the problem is achieved. Each phase has its own codification، fitness function، crossover operator and mutation operator. Finally، the computational results obtained from the algorithms of this research، which was written in MATLAB programming language، was compared with the results existing in the project scheduling problems library (PSPLIB). The findings show that، our algorithm improved some of the best solutions، recorded in the PSPLIB.

In this paper، a new approach of modeling for Artificial Neural Networks (ANN) models based on the concepts of ANN and fuzzy regression is proposed. For this purpose، we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence، it can be applied to uncertain، ambiguous، or complex environments due to its flexibility. In addition، the case study is brought in order to clearly show the way this approach could be utilized. The price of the liquid gas in Japan’s market (the world’s largest natural gas importer) is investigated based on the proposed approach. Based on the results، it is concluded that the performance of proposed model is acceptable; moreover، it can be deal with uncertain and complex environments as a clear box model.

In this paper، we consider a multi-site production planning problem subject to uncertainty in demand and workforce expenses. In our new mathematical model، we presented a production planning system considering failure in rework and breakdown. We also survey human workforce allocation and its expenses which are considered uncertain due to some tradeoff between company’s benefits and workforce union’s advantages. We presented a new robust particle swarm optimization to propose a model with the ability of handling uncertainties. Firstly، we apply the presented robust optimization to handle demand uncertainty separately، and then we extended our model to regard both uncertainties simultaneously. To show the practicability of the proposed algorithm، we solved a real problem in an industrial case study. We also analyzed the most important parameters in the presented robust model to find out which level of uncertainty has less constraint violation and determine the maximum budget of uncertainties that could be considered in the proposed model to expect acceptable optimal objective. The results showed that the proposed model can prepare a promising approach to fulfill an efficient production planning in a multi-site production planning.

Developing countries that have almost oil reserves have no enough mega projects'' management ability، technology and finance resources to manage and finish a mega project. Host countries for achievement these technologies and resources have to cooperate with foreign and international companies. Different contractual frameworks are used in conjunction with obtaining these technologies and covering these shortages. Buy-back service contract is one of these contractual frameworks that are relatively in Iran oil industry. The article is to analyze and examine deferent scenarios according to this framework to maximizing the benefits for both parties through maximizing the produced oil. Deferent scenarios in rate of return as a very important factor in buy-back contracts is to maximize and the new solutions will be introduced.

In this paper a new method is proposed for path planning of planar manipulators amid obstacles through mathematical programming in a way that the robot’s links avoid collision with obstacles throughout their motion from an initial to a goal configuration. After inputting the workspace geometry، the shortest feasible path for the robot’s end-effector is planned toward its goal position using Generalized Visibility Graph، which is then interpolated into subgoal points that should be sequentially reached by the end-effector. Next، a Mixed Integer Nonlinear Programming (MINLP) model with the objective of minimizing the distance between the end-effector and the subgoals is successively solved and the angle of each link is determined such that it does not intersect obstacles. In order to enhance the safety of clearance from obstacles، they are enlarged by an offset. Also، the proposed method has been modified and tuned aiming to reduce the number of constraints and 0-1 variables، which led to reduced runtimes.

It is assumed in most of the existing models that the items can be stored for an unlimited time to meet the future’s demand and their quality and quantity does not change during that period. Nevertheless there are special kinds of products which deteriorate or become unusable (such as food products، alcohol، medicines، etc.). Therefore if the rate of deterioration is significantly high، the impact cannot be ignored. On the other hand، most of the times the final price of a product is dependent on the number of purchased products and with the increase of number of the orders، a lower price is paid for each item. Considering these partial rebates in the models help the increase of their usability in the real world. In this paper we develop an inventory control model for perishable items considering quantity discount from the seller’s side when the demand rate is fixed annually. In this model، inventory system is a single product، the rate of the deterioration is fixed، shortages are fully backlogged and the lead time is zero. For this model، first، a simple and efficient algorithm and solution for finding the optimal value is presented and then for describing the model and the algorithm we present numerical example and sensitivity analysis of the model.

Ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. Climate catastrophes may cause social and economic tension. Forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Rate of raining is a very important factor in weather forecasting. Different forms of weather forecasting models represent different stochastic processes. Three broad classes of time series modeling in practice are the autoregressive (AR) models، the integrated (I) models، and the moving average (MA) models. These models represent the linear dependence on previous observations. Cyclic variation known as periodic fluctuation or seasonality (S) might be dealt with in time series analysis by using a sinusoidal model. A less completely regular cyclic variation might be considered by using a special form of an auto regressive integrated moving average. In this paper، a hybrid approach based on seasonal auto regressive integrated moving average (SARIMA) method and Locally Linear Model Tree (LoLiMoT) is proposed for forecasting rate of raining. A neural network based on local linear models weighted constructed by a tree algorithm is applied in this research. Training of this network is divided into a structure and a parameter optimization part. A recursive least-squares algorithm is used for training the network since the network is linear in its parameters. A two phase model is developed based on data gathered in Zabol Synoptic Station from 1939 to 2011. In the first phase، the SARIMA model is implemented to predict the raining rate. In the second step neural network based on locally linear model tree is applied to residuals to improve the prediction result. Finally، the proposed model is compared to Sin-Cos model; Result obtained confirm the efficiency of this approach as a practical tool for forecasting the rate of raining.

Uncertainties in industrial environments can cause considerable complexities، during the implementation and management of projects. So، performing a risk management model in such environments has become inevitable. In such circumstances، many risk management models have been introduced in literature، which majorities of them are general، while some of them are developed to be applied to specific organizations. In order to develop an effective risk management model، the main characteristics of projects like types of projects، project management processes and procedures، organizational structure and organizational roles and responsibilities should be taken to the account. The aim of this paper is to develop a risk management model for project based organization. The studied organization has several subsidiary companies and is responsible for management and execution of major industrial projects. In this research feasibility of implementation of risk management in the organization is studied، considering the main characteristics of its projects. In order to conduct the research، risk management models presented in literature is reviewed and investigated. Then، the requirements of risk management processes are examined. Moreover، organizational structure and characteristics related to risk management processes are identified by documents review and interview method. Afterward، the organization''s status is evaluated and analyzed via questionnaire and interview method. In this research using existing risk management models، a risk management model which consist the processes، their details and structures is developed and verified. The proposed model has six steps consist of risk management planning، risk identification، decision making، risk response planning، and reporting system. Moreover، a risk breakdown structure (RBS) is developed in two levels. The first level consists of technical، cost and financial، organizational، procurement and contractual and external risks. The research’s findings shows that risks resulted from internal contracts are the most important ones. Furthermore، some suggestions are proposed for implementing the model، based on advantages and improvement potentials of the organization. Based on the research’s findings، organizational requirements for performing risk management processes consist of management''s belief in risk management، organizational attitude and culture، risk management background in the organization، related laws and regulations، possibility of access to data and information related to risk management processes، availability of software and its requirements، organizational capability for establishing risk management teams، accordance of managers responsibilities with their authorities. It is found that management''s belief in risk management with the score of 70%، and related regulations with the score of zero are the most and the least important requirements، respectively. Overall، this paper introduces a general method for developing a risk management model in any organization. Also، a risk breakdown structure and a risk management model for industrial project based organizations are developed، with some suggestion for execution of each process. Moreover organizational requirements for implementing the model are explained. The findings of this research can be used in similar high-tech organizations، with limited financial and human resources and vast internal and external communications.

In recent years، reducing emissions has become an important issue. Besides reducing the economic costs، reducing the fuel consumption decreases emissions، pollutant impact and increases society health as well. Green vehicle routing problem are a major key to reduce hazardous effects of transportation such as air pollution، Greenhouse Gas (GHG) emissions، noise and the like. Generally، the amount of pollution emitted by a vehicle over an arc depends on many factors like vehicle load، travel speed، travel distance، road slop and etc. Vehicle load has a major effect among other factors on amount of emissions and influences the route selection. Some works were completed on the estimation of the cost of the GHG emissions. Therefore، the effect of the carried load in fuel consumption is contributed in the model by minimizing a weighted load function. This paper presents a new method for vehicle routing problem with minimizing fuel consumption and number of vehicles. Distributing managers are often interested in minimizing fuel consumption caused by two reasons: 1) reducing fuel consumption caused to reduce the service cost، economic costs and increasing customer’s satisfaction، and 2) reducing fuel consumption is a way for reducing pollutant negative impact on our environment and increasing society health. Also، minimizing the number of vehicles is caused the reducing in fixed and other related cost. It is proven that VRPs belong to the category of NP-Hard problems thus due to the complexity of VRP with exact methods in large-scale problems، a meta-heuristic method based on particle swarm optimization is proposed، so called improved particle swarm optimization (IPSO). In addition، to show the efficiency of the proposed IPSO، a number of test problems in small and large sizes are proposed and solved by the IPSO. Then، the obtained results are evaluated with the results obtained by Lingo.