Today, due to rapid changes in the world, the infrastructure should be designed to produce goods in world class. Dynamic layout planning increases flexibility and reduces the cost of new products. Investigating the essential factors to achieve world-class shows that dynamic layout planning plays essential role on flexibility, cost reduction, implementation of world-class producing techniques, reduction of product delivery time, customer satisfaction, and management in multiple and worldwide locations. The purpose of this study was to investigate the role of dynamic layout planning on production of world class products in some firms located in province of Zanjan, Iran. Using Spearman correlation test, the study indicated that dynamic layout planning played an important role in delivering the products to the world-class level.

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.

Energy crisis in recent decades has demonstrated strong interdependence between national security and energy security. We are also witness of sever conflicts in oil-rich zones such as Middle-East and West of Suez. This study is the first attempt to provide a flexible multi-objective mathematical model which not only mitigates catastrophic risks by filtering and taking plausible oil-supply disruption scenarios into account, but also reduces oil-supply disruption probability by considering and optimizing political, economic and financial dimensions of oil procurement. Mentioned model determines a resilient portfolio of oil suppliers under each scenario and decides which ports or pipelines must be prepared for receiving oil. Furthermore, the proposed model in the second phase enhances oil-availability in crisis time by storing strategic oil stocks in appropriate geographic points. Also regarding to complexity of the second phase model, a meta-heuristic algorithm has been provided to solve the mentioned model. Finally validity of proposed model is checked by solving it for Greece case problem; sensitivity analysis shows that provided model significantly mitigates catastrophic risks threating energy security by balancing political affairs and reinforcing infrastructural facilities with the least possible cost.

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.

One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.

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.

In this paper, we evaluate the performance of a supply chains (SCs) under uncertainty with different components such as direct costs, operational costs, transaction expenses, order lead time, product flexibility and net profit. Data Envelopment Analysis (DEA) can be used for measuring the performance of supply chain problems. On the other hand, robust optimization approach is a powerful technique for handling problems faced with various environmental uncertainties. This paper combines these two concepts and proposes a method to evaluate SCs performance. The results of the proposed method, under any different environmental situation, show which ranking of SC’s performance is better in a network. The preliminary results of the implementation of a real-world case study indicates that the method could be successfully used for performance measurement.

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.

This paper considers preemption and idle time are allowed in a single machine scheduling problem with just-in-time (JIT) approach. It incorporates Earliness/Tardiness (E/T) penalties, interruption penalties and holding cost of jobs which are waiting to be processed as work-in-process (WIP). Generally in non-preemptive problems, E/T penalties are a function of the completion time of the jobs. Then, we introduce a non-linear preemptive scheduling model where the earliness penalty depends on the starting time of a job. The model is liberalized by an elaborately–designed procedure to reach the optimum solution. To validate and verify the performance of proposed model, computational results are presented by solving a number of numerical examples.

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.