Abstract: Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

Abstract: Performance measurement plays essential role on improving the performance of business units and their efficiencies. During the past few years, there have been tremendous development in banking systems and the primary focus of many managers is to improve the quality of services for market retention. Performance measurement in banking industry is normally involved with various qualitative as well as quantitative criteria, which leads to the implementation of multiple criteria decision making techniques. This paper presents a hybrid grey relational analysis and K-means to cluster and measure the performance of banking system. The proposed study uses different criteria, clusters banks into various segments and ranks 43 different banks in city of Semnan, Iran.

Abstract: Today, the world facing with huge flood of data and the recent advances in computer technology have provided the capability to process significant amount of data. On the other hand, analyzing the information requires resources that most institutions do not have, independently. To handle such circumstances, grid computing has emerged as an important research area where the calculation of distributed computing and clustering are different. In this study, we propose a grid computing architecture as a set of protocols that use the cumulative knowledge of computers, networks, databases and scientific instruments based on the implementation of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. The results of the implementation of the proposed algorithm on grid systems indicate the superiority of the proposed approach in terms of validation criteria scheduling algorithms, such as task completion time and the performance compared with some alternative method.

Abstract: Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.

Abstract: Managers always look for systems with minimum hazards, which cause problems for performance of projects. The largest and the most important hazards of working underground mines can be associated with health, safety and environmental Failure mode and effects analysis (FMEA) is a widely used technique to identify the potential failure modes for measuring reliability of a product or a process. FMEA is performed by developing a risk priority number (RPN), which is the product of severity, occurrence, and detection ratings. On the other hand, with regard to uncertainty in the decision-making, fuzzy theory can help model the inherent uncertainty involved in the underground mining projects. Fuzzy FMEA provides a tool that can work in a better way with vague concepts using insufficient information compared with conventional FMEA. The comparison between the results of the conventional FMEA with those of the proposed model shows that the fuzzy model has a high potential to formulate the level of risk.

Abstract: The problem of portfolio optimization is a standard problem in financial world and it has received tremendous attentions. Portfolio optimization plays essential role in determining portfolio strategies for investors. Portfolio optimization is intrinsically a discrete optimization problem whose decision criteria are in conflict and the proposed study of this paper considers a portfolio optimization problem involving fuzzy random variables. To solve the proposed model, we first present the possibility and necessity-based model to reformulate the fuzzy random portfolio selection model into linear programming models and using the resulted linear programs, a multi-objective problem is constructed. To solve the multi-objective problem we propose some methods to consider decision makers’ optimistic and pessimistic views. A numerical example illustrates the whole idea on multiobjective fuzzy random portfolio optimization by possibility and necessity-based model.

Abstract: Clustering plays an essential role for data analysis and it has been widely used in different fields like data mining, machine learning and pattern recognition. Clustering problem divides some data, which is more similar to each other in terms of parameters under consideration. One of available methods about this area is k-means algorithm. Despite dependency of this algorithm on initial condition and convergence to local optimal points, it classifies n data to k clusters with high speed. Since we encounter a huge volume of data in clustering problems, one of suitable methods for optimal clustering is to use a meta-heuristic algorithm, which improves clustering operation. In this paper, differential evolution algorithm is utilized for solving available problems in k-means algorithm. In this paper, meta-heuristic algorithm has been used for solving data clustering problems. The applied algorithm has been compared with k-means algorithm on six known dataset from UCI database. Results show that this algorithm achieves better clustering than k-means algorithm.

Abstract: Determining the optimal inventory control and selling price for deteriorating items is of great significance. In this paper, a joint pricing and inventory control model for deteriorating items with price- and time-dependent demand rate and time-dependent deteriorating rate with partial backlogging is considered. The objective is to determine the optimal price, the replenishment time, and economic order quantity such that the total profit per unit time is maximized. After modeling the problem, an algorithm is proposed to solve the resulted problem. We also prove that the problem statement is concave function and the optimal solution is indeed global.

Abstract: This paper studies capacitated facility location problem by considering green management perspectives. The proposed study considers reverse logistic problem as an alternative strategy for facility location in an attempt to take care of environmental characteristics. The resulted problem is formulated as mixed integer programming and it is classified as an NP-Hard problem. Therefore, a Lagrangian relaxation methodology is presented to reduce the complexity of the proposed problem and the solution has been implemented for some instances to examine the performance of the proposed study.

Abstract: Electricity distribution systems are considered as the most critical sectors in countries because of the essentiality of power supplement security, socioeconomic security, and way of life. According to the central role of electricity distribution systems, risk analysis helps decision maker determine the most serious risk items to allocate the optimal amount of resources and time. Probability-impact (PI) matrix is one of the most popular methods for assessment of the risks involved in the system. However, the traditional PI matrix is criticized for its inability to take into account the inherent uncertainty imposed by real-world systems. On the other hand, fuzzy sets are capable of handling the uncertainty. Thus, in this paper, fuzzy risk assessment model is developed in order to assess risk and management for electricity distribution system asset protection. Finally, a comparison analysis is conducted to show the effectiveness and the capability of the new risk assessment model.

Abstract: One of the most important steps to build an appropriate business unit is to setup a suitable long-term strategy. A good strategy helps organization take better advantage of the existing resources and improve the performance of the firm. This paper presents a hybrid method consists of importance-performance analysis combined with fuzzy analytical hierarchy process to determine different operating strategies to increase the performance of a cement industry in Iran. The results indicate that being competitive is number one priority followed by fast delivery, quality product, dependability, cost of production and flexibility.

Abstract: Customer service is increasingly being recognized as a source of competitive advantage. The keys to provide effective customer service are determining the customer needs, accurately, and meeting and exceeding the needs in a consistent manner. Companies should adapt a strategic, proactive focus on customer service based on understanding logistic processes and designing the logistics system to meet their needs. This paper proposes an approach based on the quality function deployment (QFD), for ranking strategic actions to improve logistics service. The paper addresses the issue of how to deploy the house of quality (HOQ) to effectively and efficiently improve logistics processes and thus customer satisfaction. For data collection, fuzzy logic is used to deal with the ill-defined nature of the qualitative linguistic judgments required in the proposed HOQ. The methodology has been tested by means of a real case application, which refers to an Iranian company operating in the manufacturing industry.

Abstract: The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS) is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.

Abstract: Quadratic assignment problem (QAP) has been considered as one of the most complicated problems. The problem is NP-Hard and the optimal solutions are not available for large-scale problems. This paper presents a hybrid method using tabu search and simulated annealing technique to solve QAP called TABUSA. Using some well-known problems from QAPLIB generated by Burkard et al. (1997) [Burkard, R. E., Karisch, S. E., & Rendl, F. (1997). QAPLIB–a quadratic assignment problem library. Journal of Global Optimization, 10(4), 391-403.], two methods of TABUSA and TS are both coded on MATLAB and they are compared in terms of relative percentage deviation (RPD) for all instances. The performance of the proposed method is examined against Tabu search and the preliminary results indicate that the hybrid method is capable of solving real-world problems, efficiently.

Abstract: Since independence, India has been one of the few developing countries to invest extensively in both science and technical education. In India, technical education plays a pivotal role in human resource development while creating skilled manpower, increasing industrial productivity and enhancing the quality of life. If a technical institute means to be effective in developing learned and qualified engineers, then it would be useful to know the performance of that technical institution. However, measuring the performance of a technical institution has received very little attention because it is very difficult to measure its output. Thus, this paper focuses on assessing the performance of eight Indian Institutes of Technology (IITs) using a combined approach of data envelopment analysis (DEA) and technique of order preference by similarity to ideal solution (TOPSIS). In the first phase, DEA is applied to shortlist the efficient IITs having the desired characteristics from the stakeholders’ point of view, and TOPSIS method is then employed to rank those efficient IITs while also identifying the best performing IIT. It is observed that IIT Kharagpur outperforms all the considered IITs which exactly corroborates with the findings of the recently published surveys/reports.

Abstract: The software architecture plays essential role for the development of the complicated software systems and it is important to evaluate the software architecture efficiency. One way to evaluate the software architecture is to create an executable model from the architecture. Unified Modeling Language (UML) diagrams are used to describe the software architecture. UML has made it easy to use and to evaluate the necessary requirements at the software architecture level. It creates an executable model from these diagrams; yet, since the UML is a standard semi-formal language for describing the software architecture, evaluating the software architecture is not directly possible through it. Furthermore, in order to evaluate the software architecture, one needs to turn the actual model into the formal model. In this study, first we describe the architecture using the UML. Then, some properties of the software architecture are mentioned using the UML sequence diagram, deployment diagram, use case diagram, and component diagram. The necessary information associated with the qualitative characteristic of efficiency will be margined as clichés and labels to these diagrams. The independent and dependent components will be extracted from the component diagram. Finally, the resulted semi-formal model will be mapped into a formal model based on the colored Petri net and finally the evaluation will take place.

Abstract: The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (O), severity (S) and detectability (D) of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system’s transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry.

Abstract: During the past few years, there have been growing interests in using email for delivering various types of messages such as social, financial, etc. There are also people who use email messages to promote products and services or even to do criminal activities called Spam email. These unwanted messages are sent to different target population for different purposes and there is a growing interest to develop methods to filter such email messages. This paper presents a method to filter Spam email messages based on the keyword pattern. In this article, a multi-agent filter trade based on the Bayes rule, which has benefit of using the users’ interest, keywords and investigation the message content according to its topic, has been used. Then Nested Neural Network has been used to detect the spam messages. To check the authenticity of this proposed method, we test it for a couple of email messages, so that it could determine spams and hams from each other, effectively. The result shows the superiority of this method over the previous ones including filters with Multi-Layer Perceptron that detect spams.

Abstract: The Ad hoc network is one of the multistep-based self-organizing networks, which are dynamically changing and are taken more into account as the ways of connecting the terminals through the development of wireless communication terminals. We are faced with numerous challenges in designing a wireless network such as the dynamic topology, common and limited bandwidth, and the limited energy. The nodes are moving according to the continuous changes in the topology and the source-to-destination paths are completely broken. Therefore, the repeated route discovery enhances the delay and overload of routing. Thus, it is essential to consider the link stability while designing the path in order to choose the routing protocol. Providing the multiple paths may lead to the better performance than a path. The transmission energy control in the wireless Ad hoc networks is the option for the level of transmission energy in order to transmit each node packet in this system. Therefore, transmission energy control affects the wireless medium interface. Because of choosing the appropriate protocol, the routing operation can be improved and the energy consumption can be controlled properly as well as enhancing the durability and longevity of network. The main objective of this study is to enhance the network longevity. The proposed algorithm in this research considers the combination of 2 parameters including the rate of node energy and number of steps in Fuzzy System applied on AOMDV Protocol, which is a Multipath Routing Protocol. The results of simulation also indicate the improved performance of proposed algorithm (AOMDV-F) compared to AODV and AOMDV Protocols in NS2 simulator.