Journal of Advances in Industrial Engineering
Volume:54 Issue: 3, Summer 2020

Nowadays, due to the highly competitive situation, every business organization faces many shortcomings for smoothly running of his/her own business. So, to survive in the competition, different types of business policies are required (all-unit discount, trade credit etc.). This type problem is presented mathematically as an optimization problem and solved. In this work, the advance payment facility with n equal instalment before receiving the products is introduced to formulate a two-storage inventory model. This model is studied under the assumptions of price and advertisement’s frequency-dependent customers’ demand, constant deterioration and exponential backlogging rate. Deterioration is started in both warehouse at the same time. To solve the proposed model, MATHEMATICS and MATLAB software are used. The concavity of the objective function (average profit) is shown numerically as well as graphically by using MATHEMATICA and MATLAB software for supporting a numerical example. Also, it is shown that the objective function is negative definite by using an example. Finally, sensitivity analyses is carried out pictorially with the changes of various known parameters.

Nowadays, designing the resilient natural gas transmission network is an important and essential issue for industry experts and policymakers due to great economic, health, security and social losses created by relevant disruptions. This paper develops a two-stage approach for the design of resilient natural gas (NG) transmission network. In the first stage, the risk of each pipeline route is calculated based on distances of diffusion concentration. In the second stage, a multi-objective mixed possibilistic-stochastic programming model is presented to enhance the resiliency of the natural gas network by utilizing proactive strategies such as parallel pipeline, fortification and back-up turbo compressor under demand uncertainty and disruption risks. In addition, the proposed model considers different failure modes of the pipeline. Finally, the model validation is done by the data of a real case study. Our analysis shows that the performance of natural gas transmission network is highly vulnerable to demand fluctuations. Also, results indicate that employing both pipeline fortification and back-up pipeline strategies have numerous impacts on the resiliency of NG network. Important managerial insights are obtained from the model implementation in a case study.

An appropriate facility layout is required to reduce total manufacturing cost, especially in uncertain environments. The design of a desirable facility layout is essential when the rearrangement of the facilities is expensive. Using Routing Flexibility (RF) as a principle of the Flexible Manufacturing System (FMS) can lead to the fulfillment of this need. This paper propounds two new mathematical models for the Dynamic Facility Layout Problem (DFLP) with stochastic approaches. The RF is considered when the independent parts demands follow Exponential and Normal distributions in which their parameters randomly alter from period to period. The primary nonlinear models are first linearized by the proposed innovative technique. Then, the performance of the proposed models and the linearization technique is assessed by solving two test problems. Next, the RF effect on the manufacturing system is analyzed. The obtained results verify the validity and applicability of the proposed models. It is also shown that the suggested linearization technique is an efficient technique with 99% accuracy, even if convexity conditions are not met.

In this paper, a multi-objective model based on the decision maker’s (DM) utility function is proposed to find an optimized portfolio that fits with the desires of the DM. The proposed algorithm is developed in three stages. First, a fundamental analysis on accounting criteria is done using TOPSIS-DEA method. By this method, companies’ efficiency ranks according their fundamental reporting sheets are achieved. Second, the specific utility function of DM is found using the UTASTAR method. Third, a two-objective model is solved to find the stocks’ proportion for the individual investor. In this study, different criteria and decision making tools are used to make human-like decisions that meet investor’s expectations as well as possible. This approach is illustrated in this paper by a real-world case study concerning the evaluation of stocks in the Iran stock exchange. The suggested portfolio not only made a higher level of utility with the minimum level of risk but also is consistent with investor’s interests.

The main objective of this research is to identify the most important liquidity measures and their behavior during the trading day. For this purpose, the intraday data of 7 stocks of the Tehran Stock Exchange have been used to calculate 27 liquidity measures selected from the literature. At the first step, the distribution features and the correlation structure of the liquidity measures are examined. Using the Principal Components Analysis method, these components are identified, and their intraday patterns are extracted. The results show that reducing the number of measures to four final measures that can describe all aspects of liquidity without eliminating helpful information is possible and helps reduce the complexity of studies in this area. Relative Spread with mid quoted prices can be mentioned as the most practical microstructure component affecting liquidity. Based on this measure's intraday pattern, it can be said that this measure is minimized in the middle of the day. Therefore, liquidity is high during these hours, and favorable conditions for trading are provided. In the end, stocks are ranked based on all 27 liquidity measures through two different methods, and the most liquid stock is determined.

The advent of the globalization and outsourcing of products and services has resulted in more networked, highly dependent firms in supply chains; so supply chains’ vulnerability to several risks such as equipment breakdown is increased. Although optimizing maintenance supplier selection has a vital role in supply chain performance, it has not received enough attention in the literature. Due to the effect of maintenance strategies and supplier selection approaches, in this paper, three scenarios are considered based on various strategies as follows: 1-Preventive Maintenance with single supplier selection (PMs), 2- Preventive Maintenance with multiple supplier selection (PMm), and 3-Condition Based Maintenance with multiple supplier selection (CBMm). A fuzzy goal programming approach is applied to make trade-offs between several objectives. The efficacy of the model is validated through numerical examples, and results are compared to investigate the effect of implementing each scenario. The results show that in situations that reducing unreliability cost has higher priority for a decision-maker, CBMm is more efficient than other scenarios.