Scientia Iranica
Volume:27 Issue: 5, Sep Oct 2020

s an effective method for evaluating efficiency, the cross-efficiency evaluation method has been widely used to assess the performance of decision-making units (DMUs). However, the non-uniqueness of optimal weights problem has reduced the effectiveness of this method. To address this problem, scholars proposed to use secondary goals and presented many secondary goal models. In this pa-per, two new secondary goal models are presented, in order to further extend the above-mentioned existing secondary goal models.......

Under conditions of consumer panic buying, satisfying demand with the available products is a complex problem. In reality, most retailers accept alternative products during panic situations. This study considers the case of firm-driven substitution of products (differing in weight) based on retailer preferences over two periods. In the proposed model, panic behavior emerged in the first period and supply disruption occurred in the second period. Under this model, retail stores were segmented into high index (valuable) and low index (less valuable) customers. Before meeting the demand of low-index customers, wholesalers attempt to satiate high-index customer’s panic buying behavior. We determined the optimal number of units to be substituted, order quantities, and leftover units that generated maximum total profits for the wholesaler. The performance of the model was analyzed both with and without customer-segmented substitution. To gain managerial insights, we also examined the influence of both the degree of supply disruption and substitution costs on decisions and profits. The results can assist business managers to improve the decision-making process.

In this paper, a supply chain network system is viewed as a serial-parallel multistage process; and the application of a change point control chart based on likelihood ratio is explored to monitor this system. Firstly, state-space modeling is used to characterize complexity of the supply chain network system. Then, a change point control chart based on likelihood ratio is built to trigger potential tardy orders in the system. A case study is illustrated to indicate that the change point control charts can effectively signal process mean shift, and accurately estimate the change point and the out-of-control stage in term of power of detection and the accuracy of estimation of change point. We also investigate the effect of misspecified parameters of state space equations on the performance of the change point control chart. The results show that the performance of the change point control chart is relatively stable.

Facility location of two producers with preference of customers is discussed in this paper. Because of differences between two producers in terms of their influence on the market, the problem is formulated as a bi-level integer mathematical programming model with binary variables. It is considered that both leader and follower have some facilities at first and are going to open new facilities and this may lead to make changes in allocation of facilities and customers. To solve the problem, two metaheuristics algorithm based on genetic algorithm (GA) and hybrid of genetic algorithm and ant colony optimization (ACO) are proposed. In the first section of each algorithm, the location of facilities for two producers is determined and in the second section, each customer selects a facility. Upper bound of the competitive facility location problem is determined by solving the upper-level problem as an integer linear programming model without considering the follower’s decision. To evaluate the efficiency of proposed algorithms, enumeration technique is used to find optimal solution. Computational results show that all of the developed algorithms are capable of achieving optimal solution for small size problems and high-quality solution in reasonable computational time for medium and large-scale problems.

In this paper, a flexible lifetime distribution with increasing, decreasing, increasing-decreasing-increasing and bathtub hazard function, called New Family of Kies Burr III (NFKBIII) distributions is proposed. The density function of the NFKBIII is arc, J, reverse-J, U, bimodal, left-skewed, right-skewed and symmetrical shaped. The NFKBIII distribution is developed on the basis of the T-X family technique. The NFKBIII distribution is also obtained from compounding mixture distributions. Some structural and mathematical properties including moments, inequality measures, order statistics and reliability measures are theoretically established. The NFKBIII distribution is characterized via different techniques. Parameters of the NFKBIII distribution are estimated using maximum likelihood method. A simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). The potentiality of the NFKBIII distribution is demonstrated via its application to real data sets.

Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved; Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion.

The present paper examines the problems of stable and robust scheduling under disruptions with uncertain processing times. In order to handle such problems, in addition to exact solution approaches, a general predictive two-stage heuristic algorithm is proposed. In the first stage of the algorithm, the optimal robust schedule is generated by only considering the uncertain job processing times and forgoing the breakdown disruptions. In the second stage, adequate additional times are embedded in job processing times to enhance stability. Extensive computational experiments are carried out to test the performances of the proposed methods. The achieved results show the superiority of the proposed general predictive heuristic approach over the common methods in the literature.

Earthquake is common natural disaster in Iran, often accompanied by huge damages, losses and casualties. Therefore, focusing on earthquake response management, and improving its effectiveness is an important issue for the national disaster management organization. This paper proposes a framework to improve this process in Iran. The proposed framework attempts to coordinate governmental and non-governmental organizations involved in earthquake response. It also allows governments to systematize the obligations and responsibilities of these organizations, and mitigate the earthquake fatalities and casualties. Moreover, this study discusses the key considerations for implementing the proposed framework, and analyzes it for distinct scales of earthquake.

Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. One of the challenges of clustering is to identify the appropriate label for each cluster. The selection of a label is done in such a way as to provide a proper description of the cluster records. In some cases, choosing the appropriate label is not easy due to the results and structure of each cluster. The aim of this study is to present an algorithm based on the K-means clustering in order to facilitate the allocation of labels to each cluster. Moreover, in many data mining issues, the data set contains a large number of fields and therefore, the identification of the fields and the extraction of subsets from the required fields is an important issue. With the help of the proposed algorithm, the important and influential variables of the data set would be identified and the subset of the required fields would be selected.

The correct understanding of the conceptual and practical counterpart of Industry 4.0 is of great importance because global competition has made technology-based production a necessity. However, Industry 4.0 studies have not sufficient explanatoriness in terms of their understanding. The aim of this study is to propose a model that will predict the companies' existing and predicted Industry 4.0 levels.The changes of the concepts are examined and interpreted for 3 different hypothetically prepared scenarios. In the first scenario, an organization that is poorly managed in terms of the development of Industry 4.0 is considered. The industry 4.0 tendency was obtained as 0.04 reaching steady state after 12 time periods using the FCM algorithm. Moderate and well-managed organizations are considered in Scenario 2 and 3 respectively. The industry 4.0 tendency reached 0.12 after 15 time periods for Scenario 2. The tendency is calculated as 0.95 at the end of 5 iterations in the third scenario, which has well-managed concept values in the current situation. In addition to the scenario analysis, strategy and organization, smart operation, and smart factory concepts are found to provide the most significant contribution over the industry 4.0 level as a result of static analysis section.