International Journal of Industrial Engineering & Production Research

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Nowadays, interval comparison matrices (ICM) take an important role in decision making under uncertainty. So it seems that a brief review on solution methods used in ICM should be useful. In this paper, the common methods are divided into four categories that are Goal Programming Method (GPM), Linear Programming Method (LPM), Non-Linear Programming Method (NLPM) and Statistic Analysis (SA). GPM itself is divided also into three categories. This paper is a review paper and is written to introduce the mathematical methods and the most important applications of ICM in decision making techniques.

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Supplier selection,   Multi-objective decision making,   Fuzzy Compromise programming,   Supply chain management,   Quantity discount .

Supplier selection is naturally a complex multi-objective problem including both quantitative and qualitative factors. This paper deals with this issue from a new view point. A quantity discount situation, which plays a role of motivator for buyer, is considered. Moreover, in order to find a reasonable compromise solution for this problem, at first a multi-objective modeling is presented. Then a proposed fuzzy compromise programming is utilized to determine marginal utility function for each criterion. Also, group decision makers’ preferences have taken into account and the weight of each criterion has been measured by forming pair-wise comparison matrixes. Finally the proposed approach is conducted for a numerical example and its efficacy and efficiency are verified via this section. The results indicate that the proposed method expedites the generation of compromise solution .

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Fuzzy Logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. However, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the accident and the undesired results of it.

In this paper, fuzzy approach is used for risk evaluating and forecasting, in accidents caused by working with vehicles such as lift truck. Basically, by using fuzzy rules in forecasting various accident scenarios, considering all input variables of research problem, the uncertainty space in the research subject is reduced to the possible minimum state and a better capability of accident forecasting is created in comparison to the classic two-valued situations. This new approach helps the senior managers make decisions in risk and accident management with stronger scientific support and more reliably.

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In any supply chain, distribution planning of products is of great importance to managers. With effective and flexible distribution planning, mangers can increase the efficiency of time, place, and delivery utility of whole supply chain. In this paper, inventory routing problem (IRP) is applied to distribution planning of perishable products in a supply chain. The studied supply chain is composed of two levels a supplier and customers. Customers’ locations are geographically around the supplier location and their demands are uncertain and follow an independent probability distribution functions. The product has pre-determined fixed life and is to be distributed among customers via a fleet of homogenous vehicles. The supplier uses direct routes for delivering products to customers. The objective is to determine when to deliver to each customer, how much to deliver to them, and how to assign them to vehicle and routes. The mentioned problem is formulated and solved using a stochastic dynamic programming approach. Also, a numerical example is given to illustrate the applicability of proposed approach.

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Due to the particular importance of projects in human life and in organizations, proper project management has been always regarded highly by researchers and practitioners. Recent advances in technology and fundamental changes in most scientific areas have affected projects and made their nature and environmental circumstances much more complex than in the past. Fortunately, in recent years, many scholars have recognized the importance of complexity in modern project management and tried to identify its various aspects. Furthermore, one of the main factors for a project’s success is the assignment of an appropriate project manager. Many studies have been done about project managers' competencies and the selection methods of a suitable project manager. In most of these researches, the amount and type of project complexity have been explained as influential factors for determining the competent project manager. However, a specific approach for project manager selection considering the complexity of projects is not provided yet. Hence, in this paper we try to design and implement a fuzzy group decision making approach to allocate the best project manager taking into account the project complexity. Also, owing to the importance of construction projects in the development of countries' basic infrastructures, we exclusively studied this kind of projects. Finally, it should be noted that from the viewpoint of complexity theory, system complexity can exist in two forms: static and dynamic. Therefore, considering the breadth of issues related to each of these two complexity areas, just the static complexity of construction projects has been studied here.

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Recently, urbanization has been expanded rapidly in the world and a number of metropolitan areas have been appeared with a population of more than 10 million people. Because of dense population in metropolitan and consequently increasing the delivery of goods and services, there has been a lot of problems including traffic congestion, air pollution, accidents and high energy consumption. This made some complexities in distribution of urban goods; Therefore, it is essential to provide creative solutions to overcome these complexities. City logistics models can be effective in solving these complexities.

In this paper, concepts and definitions related to city logistics are explained to provide a mathematical model in order to design city logistics distribution network aim at minimizing response times. This objective is effective for goods and emergency services, especially in times of crisis and also for goods that are delivered as soon as possible. This is a three-level network and has been used in modeling of queuing theory. To validate the model, a numerical example has been established and results of the model have been explained using BARON solver in Gams software. Finally, conclusions and recommendations for future research are presented.

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In today complex worldwide supply chains, intermediary organizations like Contract manufacturers and GPOs are mostly used. Well-known OEMs delegate their purchasing and procuring to these intermediaries. Because of their positive influence on supply chain efficiency, it is very important to investigate the role of intermediaries in today competitive supply chains. One important question arising about intermediaries is the conditions that the OEM controls his procurement or delegates this task to the intermediary organization?

To answer this question, this paper studies the equilibrium for component procurement strategies of two competing OEMs that produce substitutable products. Each OEM may either directly procure the input from the component supplier, or delegate the procurement task to the contract manufacturer. We analyze the OEMs’ procurement game under two contracting power schemes in such a supply chain: the supplier Stackelberg, where the component supplier acts as the Stackelberg leader, and the OEM Stackelberg, where the OEMs are the first movers.

We show that, the smaller OEM always prefers direct control of component procurement. This is because the OEM will receive a lower component price if the component supplier can price discriminate the OEMs. In contrast, the larger OEM’s preference depends on the contracting power scheme. Under the supplier Stackelberg, the larger OEM never prefers direct procurement; however, under the OEM Stackelberg, the larger OEM may have incentives to use direct procurement under reasonable conditions. This implies that a shift of the market power from the supplier to the OEMs may lead to more OEMs deviating from delegation to direct control.

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Financial decision-making is the principal part of any decisions hence great efforts are done to improve the methods to assess and analyze the stock in financial markets as a part of the financial decision. This paper addresses the stock selection by discovering investor's utility function .Investors in the Stock Exchange consider diverse criteria to buy shares and bonds. Due to the criteria development in stock selection, understanding the investor's behavior by a consultant is a prominent issue. Recognizing an exclusive utility function according to the characteristics of the investors facilitates acquiring each share's value for the decision maker (DM) when it is required. In this study, UTASTAR method is used to estimate the marginal value function, using 3 appropriate criteria (risk, return, liquidity) and finally fit out the total utility function. It provides the opportunity to make a rational decision fit to investor's mentality and allowing their ranking, prioritization, selection or classification. The ranking of the options is as compatible as possible to the original one. The method is applied to an example from Iran Stock Exchange.

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With regard to the industry's development, occupational safety is a key factor in protecting the worker's health, achieving organizational goals and increasing productivity. Therefore, research is needed to investigate the factors affecting occupational safety. This research, based on the information gathered from the paint halts of one of the industrial units of Tehran, uses data mining technique to identify the important factors.Initially with Literature review to 2018, an insight into existing approaches and new ideas earned. Then, with a significant 5600 units of data, the results of the charts, association rules and K-means algorithm were used to extract the latent knowledge with the least error without human intervention from the six-step methodology of Crisp for data mining.The results of charts, association rules, and K-means algorithm for clustering are in a line and have been successful in determining effective factors such as important age groups and education, identifying important events, identifying the halls and finally, the root causes of major events that were the research questions.The results reveal the importance of very young and young age with often diploma education and low experience, in major accidents involving bruising, injury, and torsion, often due to self-unsafe act and unsafe conditions as slipping or collision with things. In addition, the important body members, hands and feet in the color retouching and surface color cabins are more at risk. These results help improve safety strategies. Finally, suggestions for future research were presented.

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Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMU) with multiple inputs and multiple outputs. In the case of considering a working team as a DMU, it often comprises multiple positions with several employees. However, there is no method to measure the efficiency of employees individually taking account the effect of teammates. This paper presents a model to measure the efficiency of employees in a way that they are fairly evaluated regarding their teammates’ relative performances. Moreover, the learning expectations and the effect of learning lost due to operation breaks are incorporated into the DEA model. This model is thus able to rank the employees working in each position that can then be utilized within award systems. The capabilities of the proposed model are then explored by a case study of 20 wells with 160 distinct operations in the South Pars gas field, which is the first application of DEA in the oil and gas wells drilling performance analysis.
 

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Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports.
Methodology: Due to the various symptoms and nature of these lesions, a three-phases innovative approach has been implemented. In the first phase, using Mask R-CNN, in the second phase, considering the age of each patient and comparison with the standard size of the prostate gland, and finally, using the morphology features, the presence of three common non-cancerous lesions in the prostate gland has investigated.
Findings: A hierarchical multitask approach is introduced and the final amount of classification, localization, and segmentation loss is 1%, 1%, and 7%, respectively. Eventually, the overall loss ratio of the model is about 9%.
Originality: In this study, a medical assistant approach is introduced to increase diagnosis process accuracy and reduce error using a real dataset of abdominal and pelvics’ CT scans and the physicians’ reports for each image. A multi-tasks convolutional neural network; also presented to perform localization, classification, and segmentation of the prostate gland in CT scans at the same time.

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