SCIENTIA IRANICA
Year:2020 | Volume:27 | Issue:5 (Transactions E: Industrial Engineering)|Papers Count:10

As an effective method for evaluating efficiency, cross-efficiency evaluation has been widely used to assess the performance of Decision-Making Units (DMUs). However, non-uniqueness of the optimal weights problem reduces efficiency of this method. To address this problem, scholars have proposed using secondary goals and they have developed numerous models with this consideration. In this paper, two new secondary goal models are presented in order to further extend the above-mentioned existing models. The proposed altruism cross-efficiency model is speci cally applicable when the evaluated DMU cooperates with other DMUs, since it maximizes the peer-efficiency scores of other DMUs. On the other hand, the exclusiveness cross-efficiency model is utilized when the evaluated DMU is competing with other DMUs. This model minimizes the peer-efficiency scores of other DMUs. Compared with the existing approaches, our approach ensures that the peer-efficiencies generated by each DMU are maximized (or minimized) under the premise that the minimum (or maximum) peer-efficiency among the DMUs is maximized (or minimized). More importantly, our proposed algorithm guarantees the uniqueness of the optimal weights and the set of cross-efficiency scores. Two numerical examples were used to verify the effectiveness of the proposed model.

The problem of satisfying demand with available products is a complex one under consumer panic buying. 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 time periods. In the proposed model, panic behavior and interruption in supply emerged in the  first and second time periods, respectively. Under this model, retail stores were segmented into high-indexed (valuable) and low-indexed (less valuable) customers. Before meeting the demand of lowindexed customers, wholesalers attempt to respond to the panic buying behavior of highindexed customers. To ensure maximum pro t in total for a wholesaler, this study considers parameters like optimal substitution quantity, quantity to order, and leftover units. The model is investigated in two cases of with and without substitution. To gain managerial insights, the effect of both degrees of interruption in supply and substitution costs on profits and decisions was investigated. The results can help business managers improve the decision-making process.

A supply chain network system was regarded as a serial-parallel multistage process and the application of a change-point control chart based on likelihood ratio was explored to monitor this system. First, state-space modeling was used to characterize complexities of the supply chain network system. Second, a change-point control chart based on likelihood ratio was used to trigger potential tardy orders in the system. Third, a case study was carried out to prove that the change-point control chart could effectively signal mean shift in completion time of one order in one stage and could accurately estimate the change point and the out-of-control stage in terms of performance indices. In detail, when the mean shift was relatively small, the change-point control chart could effectively identify it and more accurately detect the change point and the out-of-control stage than the traditional Shewhart control chart did. We also investigated the effect of misspecified parameters of state space equations on performance of the change-point control chart. The results showed that the performance of the change-point control chart could still remain relatively stable. In general, the change-point control could effectively monitor the supply chain network system and the monitoring effect was relatively stable.

Facility location of two producers with preference of customers is discussed in this paper. Because of the differences between two producers in terms of their influence on the market, the problem was formulated as a bi-level integer mathematical programming model with binary variables. It was considered that both leader and follower had some facilities at  first and would open new facilities, which might lead to changes in the allocation of facilities and customers. To solve the problem, two metaheuristic algorithms based on Genetic Algorithm (GA) and a hybrid of GA and Ant Colony Optimization (ACO) were proposed. In the  first section of each algorithm, the location of facilities for two producers was determined; in the second section, each customer selects a facility. Upper bound of the competitive facility location problem was determined by solving the upper-level problem as an integer linear programming model without considering the follower's decision. To evaluate the efficiency of the proposed algorithms, the enumeration technique was employed to  nd an optimal solution. Computational results demonstrated that all of the developed algorithms were capable of achieving an optimal solution to small-sized problems and a high-quality solution within a reasonable computational time to medium-and large-scale problems.

In this paper, a new lifetime family of distributions called 'New Family of Kies Burr III (NFKBIII) distribution' was developed by using T-X family technique. The NFKBIII distribution is very flexible and its hazard rate function accommodates various shapes such as increasing, decreasing, increasing-decreasing-increasing, and bathtub. The density function of the NFKBIII was arc, J, reverse-J, U, bimodal, left-skewed, right-skewed and symmetrical shaped. Some structural and mathematical properties including quantiles, sub-models, ordinary moments, moments of order statistics, incomplete moments, mean deviations, inequality curves, residual life functions, and reliability measures were derived. Two characterizations for the NFKBIII distribution were studied. The Maximum Likelihood Estimates (MLEs) for unknown parameters of NFKBIII distribution were obtained. A simulation study was performed to evaluate the behavior of the maximum likelihood estimators. The NFKBIII distribution was applied to two real data sets to illustrate its potentiality and utility. The adequacy of the NFKBIII distribution was tested via different goodness of fit statistics.

Resource-Constrained Project Scheduling Problem with Multiple Routes (RCPSP-MR) for flexible project activities is a generalization of the RCPSP in which, for the implementation of each flexible activity in the main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for flexible activity. The routes considered for each flexible activity are varied in terms of: 1) the number of activities requiring to be executed; 2) the precedence relationships between activates; 3) allocation of different renewable and nonrenewable resources to each activity; and 4) effectiveness in duration and cost of project completion. In this study,  first, a new mathematical formulation for RCPSP-MR was developed. Then, two solving approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were adopted to minimize the costs of project completion. To evaluate the effectiveness of the proposed approaches, 50 problems (in very small, small, medium, and large-size test problems) were designed and solved. Finally, comparisons were made between the results. Computational results showed that the proposed GA would generate high-quality solutions in a timely fashion.

The present paper examines the problems of single-machine scheduling under disruption with uncertain processing times. The goal is to achieve schedules that are simultaneously stable and robust. To resolve such problems, in addition to exact solution approaches, a general predictive two-stage heuristic algorithm is proposed. In the  rst stage of the algorithm, the optimal robust schedule is generated only by taking into account uncertain job processing times and forgoing 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 a 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 on distinct scales of an earthquake.

Data mining is a powerful new technology to extract hidden information from data warehouses. Data mining analyzes data from different perspectives and  finds useful patterns and knowledge from large volumes of raw data. 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 so as to provide a proper description of cluster records. In some cases, choosing an 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.

Proper understanding of the conceptual and practical counterparts of Industry 4. 0 is of great importance as global competition has made the technology-based production a necessity. The aim of the present study was to propose a model that would predict the existing and future Industry 4. 0 levels for companies. The changes of the concepts were examined and interpreted for three different hypothetically developed scenarios. In the fi rst scenario, an organization that was poorly managed in terms of the development of Industry 4. 0 was considered. The Industry 4. 0 tendency was calculated at 0. 04, reaching a steady state after 12 time periods using the Fuzzy Cognitive Maps (FCMs) algorithm. Moderate and well managed organizations were considered in Scenarios 2 and 3, respectively. The Industry 4. 0 tendency reached 0. 12 after 15 time periods in Scenario 2 and 0. 95 at the end of  five iterations in the third scenario with the concept values indicating well managed situation in the latter case. In addition, strategy and organization, smart operation, and smart factory concepts were found to make the most significant contribution to the Industry 4. 0 level in the static analysis.