INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2017 | Volume:28 | Issue:1 |Papers Count:16

Resource-constrained project scheduling problem (RCPSP) is the basis of scheduling problems in operations research. In order to have more realistic model, the problem is studied with objectives of time, cost and a modified objective of resource leveling with discounted cash flows and several modes of execution for activities. Since the problem is an NPhardness, various kinds of heuristic and meta-heuristic methods have been proposed by many researchers to present the more efficient solution. The gravitational search algorithm is one of meta-heuristics introduced in recent years. Due to single-objective and continuity of the decision space, this algorithm has not been used to the multi-objective discrete RCPSPs. In this paper, multi-objective gravitational search algorithm (MOGSA) is proposed for solving the given problem. The performance of the proposed MOGSA is compared with a well-known NSGA-II algorithm in terms of some indices for several small and large sized PSPLIB test problems. The results show the proposed MOGSA outperforms the NSGA-II.

Since the production scheduling is covered the wide range of manufacturing and services systems, the types of related issues are highly diverse. In advanced manufacturing environments, because the production flexibility property is taking into account as a competitive advantage, in this paper a special kind of flexibility in the job shop scheduling problem is defined in which, for jobs processing, each workstation have multiple parallel machines. Processing speed of each machine can also be different. The objective of this problem is to minimize the maximum completion time (makespan). Due to NPhardness of problem, we proposed a metaheuristic algorithm. In the proposed approach, due to the structure of the problem and its discrete enviorment, we modified a particle swarm optimization as a new discrete algorithm. Finally to evaluate the performance of the algorithm, the proposed algorithm has been compared with several heuristics existing in the literature.

Prediction of equipment remaining useful life (RUL) is essential for efficient maintenance decision making to decrease the maintenance cost. The failure history data and the expert knowledge are two important information sources for RUL prediction. Although there are lots of methods in literature that have used the history data to predict the equipment RUL, the hybrid methods has received less attention in this field. Therefore, this paper aims to present a new method based on a Takagi-Sugeno-Kang (TSK) inference system combined with information gathered from both condition monitoring process and expert knowledge to predict RUL of the equipment. In this paper the rule base for fuzzy inference system is prepared in two stages. At the first stage three basic rules are tuned with history data using a neuro-fuzzy (NF) network and in the second stage the rule base is completed by rules extracted under experts’ supervision. The performance of new hybrid method is evaluated in different real conditions to compare with traditional data dependent methods. Also, in this work a simulating algorithm is presented in order to generate different conditions that really could happen. Simulating parameters are estimated from real data related to bearing failures. The experimental results show that the efficiency of proposed method is higher than traditional data-dependent method.

In addition to the factors such as the efficiency, size and technology of decision making units (DMUs), the balance of DMU also affects the total factor productivity (TFP). The effect of efficiency, scale, and technology on the total factor productivity growth have been provided by a three-component decomposition of Malmquist index. In this paper, the fourth factor called the balanced factor of DMUs is defined using DEABSC method. This new concept leads to provide an extended Malmquist index and its four-component decomposition in which the effect of balanced factor on the total factor productivity growth can be detected. In fact, the balanced factor measures how much a DMU is align with some defined strategies. The proposed method in this paper has been implemented on the real data of specialized bank branches, and the results show that productivity growth has been affected by the changes in balanced factor and size of branches during the period under evaluation. In the case of positive effect of these changes, strengthen the activities is recommended, otherwise the activities should be changed or modified. Furthermore, providing such a precise and scientific improvement solutions considerably will help managers in order to adopt more constructive decisions.

In the past two decades, natural disasters cause considerable human and financial losses. Since such disasters occur unexpectedly, presenting quick and proper plans, is essential. Transporting evacuees from the affected areas to shelters is among the vital measures in the response phase to the disaster. In this paper, a novel mathematical programming model has been proposed for simultaneous routing and scheduling of relief vehicles with considering relationship between shelters. In evacuation operations, multiple depots for heterogeneous fleet of relief vehicles, split delivery and time window constraints have been considered. To show the efficiency of the proposed model, we select a random example, run the model on it and do different sensitivity analysis on the important parameters. These results show that relationship between facilities and capacity of relief vehicles and shelters affect total time of servicing.

Warehouses are used in many factories from the moment of receiving raw materials to the moment of transmitting final products. In this paper, a bi-objective mathematical model is proposed for order picking problem in warehouses and delivery of orders to production/assembly lines. The first considered objective is to minimize the total cost of order picking in warehouse, and the second objective is to minimize the average tardiness of delivering orders to production/assembly lines. The proposed model is a mixed integer linear programming problem. Since the under study problem is proved in literature to be a NP-Hard problem, two multi-objective meta-heuristic algorithms are proposed entitled non-dominated sorting genetic algorithm (NSGA-II), and nondominated ranking genetic algorithm (NRGA). Since the optimality of solutions for meta-heuristic algorithms depends on the parameters of algorithms, the Taguchi method is utilized to tune the parameters of algorithms. Finally, computational results from solving different numerical examples with different sizes illustrate the performance of the proposed method.

One of the important risk management tools in financial markets, is financial derivatives. In this study, we investigate the problem of European option pricing. The most key input to option pricing models is volatility. For accurate modeling of volatility, we use three famous GARCH type models including GARCH, EGARCH, GJR-GARCH. With using the results of the best GARCH-type model, we develop two nonparametric models based on Neural Networks and Neuro-Fuzzy Networks to price call options for S&P 500 index. We compare the obtained results with those of Black-Scholes model and show that the Black-Scholes model in not appropriate for at-the-money options. Furthermore, by comparing the Neural Network and Neuro-Fuzzy approaches with Black-Scholes model, we observe that the accuracy of non-parametric models are better than the Black-Scholes model.

Supply chains' optimal performance needs to obtain a part of the activities, but these activities have not always been favored chain members and in fact each of the involved members in the chain decides independently to increase their profits and not necessarily profit chain and this leads to poor performance of the entire chain. Coordinating is such an important issue that it is widely considered in the supply chain. Naturally, participatory or non-participatory games theory is one of the most prominent tools for analyzing this type of competition and cooperation issues in supply chain. This paper discusses to coordinate of two-level supply chain consisting of a manufacturer and a retailer with using cooperative advertising along with pricing decisions and Manufacturer offer prices discount to retailer where demand is influenced by both prices and advertising. Cooperative advertising is a concerted effort by channel members that occurs to increase customer demand. By using game theory we consider two models of the relationship between manufacturer and retailer which consists of noncooperative nash game and cooperative game and bargaining model is discussed to share the extra joint profit in cooperative game based on of players’ risk attitude and bargaining power.

We investigate a closed-loop supply chain in this article that manufacturer has two channels for satisfying the demand: producing brand-new products or remanufacturing returned items. There is no difference between brand-new products and returned one so they can be sold at the same market with the same price. In this article, demand has price sensitive uncertainty. Also returns are stochastic and price sensitive too. In addition we define acceptance quality level for acquiring returned products. Therefor a mathematical model developed to maximize total profit of system that determines selling price, production quantity of brand-new products and remanufactured one, acquisition price and minimum acceptance quality level for returned items.

For this purpose, the financial and non-financial data of drug distribution companies were investigated and the risks threatening the companies were identified and evaluated using literature review, expert opinion and the FMEA technique. Two models were developed using available variables from the qualitative (Likert scale) risk management questionnaire. The first model takes manpower as input and sale and market share as output. In the second model, the manpower and risk management were fed as inputs into the Data Envelopment Analysis method. The two models were used to calculate the efficiency score of drug distribution companies. According to the results, some companies were on the efficiency boundary and some others were inefficient. For inefficient units, virtual efficient unit was formed and indicated that whenever the risk management variable was respected by these units, their efficiency was grown and taking this variable into consideration increases the number of efficient units.

In recent years, willingness to invest and trade of securities has been grown by retail investors. This group of investors, mainly trade in average or low volume levels, therefor they should consider some restrictions which have not been added to most classic financial portfolio optimization models such as Markowitz. Some of these limitations are transaction costs and the number of assets in the portfolio. In this paper, the constraints of model have been modified to maximizing the portfolio return and achieve better risk estimation and also five activated industrial indexes has been chosen since 2009 up to 2012 and optimal investment portfolio was formed with the noticed model. We hired, multivariate GARCH family models (MGARCH) as Vech, BEKK, CCC and DCC to reach conditional covariance’s matrix and then we calculate, the optimal portfolio weights for each group of industries.

Credit risk in bank industry is the probability of non-repayment of obligations by customers at specific time. It is one of the most important hazards for banks and private institutes. Due to huge bulk of banks’ overdue receivables, establishment of a Credit Scoring (CS) system is one of the most important means of controlling such a risk. This paper uses the powerful Neural Networks in predicting and mixing them, which can classify customers in two groups of customers who pays their debts on time and customers who don’t. The used model, which has modular based structure and training, is named Hierarchical Mixture of Neural Networks (HMNN). In mentioned model, for the decomposition of problem among networks and combining results to achieve the final prediction and also the method of training of it uses new approach. The purposed approach applies Binary Particle Swarm Optimization (BPSO) for dimension reduction and decomposing the problem among modules at first, then using the modulation of the training rules specific to each module and the general training rule of this network. Results are achieved in compersion with Multi-Layer Perceptron and Laterally Connected Neural Network. Based on observed results, the suggested model could predict customers’ behaviour with punctuality.

Natural disasters cause deaths of thousands and affect millions of people every year. Thus, we should think about disaster preparedness planning to reduce the impact of them. Location of distribution centers and allocation of affected people to these centers are very important in emergency management, because they cause to reduce the relief time and damages caused by the disasters. This paper aims to locations of distribution centers and allocation of affected area to these centers by considering dynamic and uncertain demands, to reduce cost and increase reliability. At this article the reliability is considered as the backup depots, probability of failures for the routes and depots, and as a separate objective. Also uncertainty is displayed as a scenario and allocation of established depots are done according to different scenarios. A case study about earthquake in Tehran city is selected and implemented in the model to evaluate and validate the proposed model. Result shows that we can increase the satisfied demands and reliably by using the proposed model which has a significant important in emergency management.

Proficiency testing is one of the external quality control techniques that can determine quality control program efficiency in each laboratory. The purpose of this paper was studying laboratories’ skill in measurement of conductor electrical resistance and determining factors to improve the performance of laboratories. In this paper, according to the results of implementation of the standard harmonized project (Taha) in Iran in 1392, number of standard Co. laboratories and also the importance of safety in electricity industry, Solid conductor cable is selected as proficiency test item. In this article, some significant factors affecting in the test results like complying with isothermal conditions, type and calibration status of instruments and implementing laboratory quality management system based on international standard ISO/IEC17025 were studied. This process was done with 19 participants based on sequential participation scheme. According to the statistical analysis, laboratories were ranked based on Z-score and the difference, D. 63.15% of participants obtained acceptable Z-score and D in measurement of electrical resistance parameter in the conductor. Statistical analysis shows that 60% of participants who achieved “A” quality level, have appropriate and calibrated instruments and isothermal condition and implement ISO / IEC 17025 on their management system.