International Journal of Industrial Engineering and Productional Research
Volume:31 Issue: 1, Mar 2020

This paper deals with a multi- objective linear fractional programming problem involving probabilistic parameters in the right- hand side of the constraints. These probabilistic parameters are randomly distributed with known means and variances through the use of Uniform and Exponential Distributions. After converting the probabilistic problem into an equivalent deterministic problem, a fuzzy programming approach is applied by defining a membership function. A linear membership function is being used for obtaining an optimal compromise solution. The stability set of the first kind without differentiability corresponding to the obtained optimal compromise solution is determined. A solution procedure for obtaining an optimal compromise solution and the stability set of the first kind is presented. Finally, a numerical example is given to clarify the practically and the efficiency of the study.

In this paper, an integrated mathematical model of the dynamic cell formation and production planning, considering the pricing and advertising decision is proposed. This paper puts emphasis on the effect of demand aspects (e.g., pricing and advertising decisions) along with the supply aspects (e.g., reconfiguration, inventory, backorder and outsourcing decisions) in developed model. Due to imprecise and fuzzy nature of input data such as unit costs, capacities and processing times in practice, a fuzzy multi-objective programming model is proposed to determine the optimal demand and supply variables simultaneously. For this purpose, a fuzzy goal programming method is used to solve the equivalent defuzzified multi-objective model. The objective functions are to maximize the total profit for firm and maximize the utilization rate of machine capacity. The proposed model and solution method is verified by a numerical example.

One of the major risks that can affect supply chain design and management is the risk of facility disruption due to natural hazards, economic crises, terrorist attacks, etc. Static resiliency of the network is one of the features that is considered when designing networks to manage disruptions, which increases the network reliability. This feature refers to the ability of the network to maintain its operation and connection in the lack of some members of the chain. Facility hardening is one of the strategies used for this purpose. In this paper, different reliable capacitated fixed-charge location allocation models are developed for hedging network from failure. In these proposed models, hardening, resilience, and hardening and resilience abilities are considered respectively. These problems are formulated as a nonlinear programming models and their equivalent linear form are presented. The sensitivity analysis confirms that the proposed models construct more effective and reliable network comparing to the previous networks. A Lagrangian decomposition algorithm (LDA) is developed to solve the linear models. Computational results show that the LDA is efficient in computational time and quality of generated solutions for instances with different sizes. Moreover, the superiority of the proposed model is confirmed comparing to the classical model.

Regarding population growth and prompt development in developing countries, municipal solid waste management is always a great challenge for governments. Waste to energy conversion is an efficient approach with respect to overcoming not only the challenge of municipal solid waste management but also environmental challenges related to energy consumption like global warming and fossil fuel depletion. One of the substantial problems throughout the implementation of waste to energy approach is process selection. The selected process should be technically feasible and should have a high level of compliance with environmental standards. Owing to an inevitable significance of process selection, this paper focuses on defining the best process by relying on multi-criteria decision-making tools and network analytic process. Considering the effective parameters such as cost, efficiency in material diversity, productivity rate, energy consumption, pollutant emissions, toxic substances, and process time, the result indicates that the physico-chemical process is superior process for pretreatment of material.

In this paper, a new model of hub locating has been solved considering reliability and importance of flow congestion on hub nodes in a dynamic environment. Each of nodes considered as hubs and their communication paths with other non-hubs nodes have specific reliability. In order to reduce input flow to any hub and avoid creation unsuitable environmental and traffic conditions in that area, efficiency capacity is allocated to each hub, which is subject to a penalty in case of exceeding this amount. Another capability of this model is the ability of deciding whether hubs are active or inactive in each period, so hub facilities can be established or closed due to different conditions (such as changes in demand, legislative, etc.). The model is non-linear and bi-objective that the first goal is reducing transportation costs, hub rental fees and extra flow congestion penalties on hub nodes and the second goal is to increase the minimum designed network reliability. After linearization of the model, using ε-constraint method, optimal boundary is obtained. Also, to demonstrate the performance of the model, we use IAD dataset for solving problem. To evaluate the model, sensitivity analysis is presented for some of important parameters of the model.

Practically, Islamic banking in Iran is not much different from conventional banking principles. Many paradigms of the commercial banking are considered in the Islamic-Iranian banking. Owing to the fact that asset and liability optimization is an important issue in the banking industry, the present paper investigates the balance sheet and income statement to constitute a structure for measuring each asset’s risk. The author uses the method of multiple objective programming to solve the problem of commercial bank's diversified pursuit of low risk and high profit by considering the so-called duration constraint. To test the proposed model, the data were collected from an Iranian commercial bank named Mellat bank from June 2009 to December 2016. The results suggest that Mellat bank, as the biggest private bank in Iran, should reform its asset-liability allocation to achieve the optimal level.

In this paper, an inventory-routing problem for a network of appliance repair service is discussed including several repair depots and customers. The customer in this network makes a demand to have his/her faulty appliance repaired. Then, the repairman is assigned to the demand based on the skill needed for repairing of appliance differing for each one. The assigned repairman picks up the faulty appliance from the customer place using the vehicle for transferring faulty appliances to repair depot. The vehicle for picking up and delivering the appliances has a maximum capacity. Additionally, the repair depot needs spare parts to repair the faulty appliances that is supplied either by the supplier or lateral transshipment from the other depots. The capacitated vehicle inventory-routing problem with simultaneous pickup and delivery is NP-hard which needs special optimization procedure. Regarding the skill of repairman, it becomes more complex. Many solution approaches have been provided so far which have their pros and cons to deal with. In this study, an augmented angle-based sweep method is developed to cluster nodes for solving the problem. Finally, the heuristic is used in the main body of genetic algorithm with special representation.

Biofuels production systems are identified as a potential solution in responding to the ever-increasing energy consumption demand. The complexity of conversion process and supply chain of these systems, however, can make the commercialization of biofuels less attractive, so designing and management of an efficient biofuel supply chain network can resolve this issue. Hence, this paper proposes a multi-period hybrid generation biomass-to-biofuel supply chain considering environmental, economic and technology considerations. The objective is to maximize the total profit that biofuel producers can make with practical constraints including the biomass supply, the capacity of facilities, storage, Greenhouse Gas (GHG) emissions and transportation with limited capacity. To highlight the applicability of the proposed model, it is applied to a biomass-derived liquid fuel supply system in the southern region of Iran. In the case study, wheat and wheat stem are simultaneously considered as the first- and second-generation of feedstocks for biodiesel production. Sensitivity analyses show that available biomasses can have a significant impact on the profitability of this supply chain. The obtained results demonstrate the efficiency and performance of the proposed model in biodiesel supply chain design.

The continuous growth of healthcare and medicine costs as a strategic commodity requires tools to identify high cost populations and cost control. After the implementation of the healthcare Reform plan in Iran, a huge share of hospital funding has been spent on undesirable costs due to changes in the use of medicines and instruments.

The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.

Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions information. Then using normalized risk function to identify abnormal and high cost cases based on the distance between the input data and the mean of the data. The data used are 15078 records, including information from patients' prescriptions from Shari'ati HIS in Tehran-Iran from 2012 to 2016.

According to the obtained results, the proposed model has a positive Likehood ratio (LR+) of 6.35.

The selection of an appropriate cutting tool for the production of different jobs in a flexible manufacturing system (FMS) can play a pivotal role in the efficient utilization of the FMS. The selection procedure of a cutting tool for different production operations becomes more significant with the availability of similar types of tools in the FMS. In order to select and allocate appropriate tool for various production operations in the FMS, the tool selection rules are commonly used. The application of tool selection rules is also observed to be beneficial when a system demands two or more tools for the production operations at different work centers at the same time in the FMS. In this paper, investigations are carried out to evaluate the performance of different tool selection rules in the FMS. The performance of the tool selection rules is evaluated by simulation with respect to different performance parameters in the FMS namely makespan, mean work center utilization (%) and mean automatic tool transporter (ATT) utilization (%).

This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes.The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes.
The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.

Crude oil production output forecast is very important in the formulation of genuine and suitable production policies; it is pivotal in planning and decision making. This paper explores the use of forecasting techniques to assist the oil field manager in decision making. In this analysis, statistical models of projected trends which involves graphical, least squares, simple moving average and exponential smoothing methods were compared. The least squares method was found to be most suitable to capture the recent random nature of crude oil production output in the oilfield of the Niger Delta region of Nigeria. In addition, a multiple linear regression model was developed for predicting daily, weekly, monthly or even yearly volume of crude oil production output in the oilfield facility.