نشریه مدیریت صنعتی
سال دوازدهم شماره 38 (پاییز 1399)

Measuring the efficiency of innovation to manage innovation investment inthe era of "knowledge economy" is being considered by more researchers every day. Theevaluation of innovation efficiency helps identify the best innovators for benchmarkingand identifies ways to improve efficiency by identifying the weaknesses. In this paper, anew formulation approach for dynamic network data envelopment analysis is presented toevaluate the efficiency of multi-period and multi-division systems (MPMDS) whilecontrolling the weights.

To prevent facing the black-box of innovation, at the first, a conceptualdynamic network structure of the universities’ innovation was developed, and then, theproposed dynamic network DEA approach is used to overcome the fundamentalshortcomings to control the weights of factors in line with enabling the desiredmanagement weights.

The findings depicted that, among 13 universities surveyed, one university(about 7%), was recognized as efficient in the total process of innovation, and the averageefficiency was equal to 0.79 for universities. In both sub-processes of R&D andapplication, one university (7%) was considered efficient and their average efficiency was0.82 and 0.47, respectively, which indicates the poor performance of universities inimplementation and ideas commercialization. Also, the changes in the average efficiencyof the sub-process of applying the results are quite the opposite of the research anddevelopment sub-process.

The results reflect that the model presented in this study, by solving theconventional DNDEA model problems in weight control, improves the discriminatingpower of efficient and inefficient units.

According to many experts, methodological weakness is one of theweaknesses of master and doctoral dissertations in Iran. A meta-methodological study isa kind of comprehensive study that examines and compares the methodological aspects ofprevious researches. Accordingly, the purpose of this study is to identify and evaluate thestatus of the methodological components of master and doctoral dissertations in the fieldof industrial management at Shiraz University from 1991 to 2019.

This research is a descriptive and analytical type that used Meta-method toreview the dissertations in the field of industrial management. VOS viewer software isused to analyze data from Excel software and keyword co-event.

The results of the data analysis showed that 89% of the dissertations wereapplied research t and the most used tool of the researchers was questionnaire (31%). Inaddition, the results indicated the dominance of quantitative methods (79%), positivismparadigm (80%) and survey strategy (57%) and Statistical test methods (30%) in theanalysis of findings in industrial management researches. Finally, by extracting 713unique keywords, 56 words were repeated at least 5 times, including applied research,descriptive, quantitative, positivism, library and field research, survey, documentationand questionnaire.

The results of this study can lead to understanding the past situation and bypresenting a picture of trends in the selection of methods used in dissertations, identifythe existing gaps and be used for future educational and research planning.

Data Envelopment Analysis (DEA) is an effective method for evaluating therelative efficiency of decision-making units (DMUs). The classical approach considerseach organizational unit as a black box and limits evaluation to primary inputs and finaloutputs and neglects internal processes. This problem with the introduction and use ofDEA in network structures for more accurate performance analysis, taking into accountits internal processes, has been resolved. In most of the proposed models, the inputs andoutputs of DMUs are definite, but in many cases, those data cannot be measured in aprecise way. Therefore, this paper seeks to introduce a new model of Network DataEnvelopment Analysis with a parallel structure by considering inputs and outputs asuncertain variables. The approach used is to develop the mathematical model from atheoretical point of view, to prove the theoretical properties of the model, themathematical validity and its application.

In this paper, the assumptions of uncertainty theory and models of NetworkData Envelopment Analysis to evaluate DMUs with parallel structure and impreciseinputs and outputs.

According to the results of the implementation of the proposed model in theSocial Security Organization, the efficiency of all DMUs and its sub-system has beenevaluated between zero and one.

Due to the multiplicity of the sub-system, none of the 12-provincial socialsecurity managing directorates as DMUs were efficient (one efficiency score), but among313 branches, three branches were efficient. The final results of the implementation of theuncertain model proved the assumptions of the definitive model.

Data envelopment analysis is a well-known method based on mathematicalprogramming to measure the efficiency of decision-making units. This approachidentifies some units as efficient units set. According to these units, it constitutes anefficient frontier. In this case, discernment between efficient decision-making units are isimpossible because several decision-makers have the same efficiency score.

This study presents a new method for ranking efficient units in fuzzy dataenvelopment analysis. In this study, using a fuzzy inference system for ranking efficientunits is proposed as a new method. In the proposed method, the efficient and inefficientunits are first separated from each other using data envelopment analysis. Then, theconcepts of fuzzy inference system are used to rank efficient units.

The information of inefficient units is in a way which the fuzzy dataenvelopment analysis fails to assign an equivalent value of one to these unit’s efficiency.According to this concept, in the proposed method, each of these inefficient units isconsidered as a rule, and the amount of these rules are fired by the efficient units, hasused as an indicator for their ranking.

Finally, a numerical example is performed to check the accuracy of themodel's performance. In this example, the data used in one of the basic articles in thisfield were used and it was found that the results obtained from the proposed method arequite similar to the results of the mentioned research.

Humanitarian operations begin quickly with disasters to save lives, reduce the suffering of the injured and meet their needs. Appropriate strategies and solutions should be used to help the injured. In this regard, this paper aims to provide a hybrid approach to select effective strategies in the humanitarian supply chain.

In this study, firstly the needs of the injured were identified and categorized, and then the weight of each one was obtained using Fuzzy SWARA technique. Then, a combination of QFD approach and multi-objective modeling was used to select strategies to meet the needs of the injured. The epsilon-constraint method and GAMS software were used to solve the multi-objective model.

The needs of earthquake victims in the country were identified and classified into five categories: food needs, hygienic, mental health, housing and living facilities. The results showed that the need for nutrition and food, evacuation of the affected groups from the accident site and access to drinking water are the most important needs of the victims during the earthquake. To help victims, 14 strategies were divided into three categories: strategies related to construction, infrastructure improvement, and strengthening relief processes.

In order to reduce the damage during an earthquake, light materials should be used in construction, the culture of lightening should be strengthened, and at the same time, the awareness of people in using durable materials should be promoted. Also, worn-out water, electricity and gas systems should be developed and integration should be between them. In addition, the improved communication system, and inventory management and evacuation location should be strengthened during relief efforts.

In Workplaces, organizations need to balance warehouse inventory and maintenance costs. Therefore, inventory control is one of the major concerns of organizations. In many organizations, they use the ABC classification method for controlling a large volume of inventory. in the traditional ABC classification, items are categorized only by a single criterion; but considering other criteria is also essential, so in such cases, the multi-criteria inventory classification is used. The purpose of this study is to present a new model in multi-criteria inventory classification.

In this study, the data envelopment analysis method has been used to present an appropriate model for inventory classification. In the literature of Radial and non-radial models of data envelopment analysis have been used in inventory classification and the comparison of the results of the models indicates that non-radial models provide a more rational classification. Hence, other types of non-radial models have been proposed in this paper to improve inventory classification.

the proposed method does not have the weakness of the radial models and improves the multi-criteria inventory classification using non-radial models.

47 items of inventory related to a common numerical example in the literature as well as 80 items of inventory items of Pars Khazar industrial company have been used in order To implement the new models and compare them with existing models. The results of comparing the proposed model with the existing ones in the literature show the superiority of the proposed model.

Many of the world's top universities have already decided to hold the next semester with e-teaching. In Iran, too, the forecasts show a red situation for some areas in terms of corona prevalence. Also, several university students live in these areas. In planning the next semester, therefore, more focus should be on e-teaching. According to the instructions of the Ministry of Science, Research and Technology in Iran, education should be implemented in two parts, including e-teaching and face-to-face training. In face-to-face education with the needs of the educational space, students are divided into disciplines so that the implementation of health protocols in the university and educational space is possible. For this purpose, in this research, a mathematical optimization model is presented to allocate semester weeks to students of different disciplines in coronary-living conditions.

In this paper, to determine specific weeks for students of different disciplines during the semester in face-to-face education, a mathematical optimization model is proposed in the form of nonlinear programming with integer variables. In the objective function of the model, the distribution of students in the educational space during consecutive weeks during the semester should be done in such a way that it has the maximum possible dispersion to prevent the spread of coronary heart disease.

This model has been implemented to allocate semester weeks to students of different majors at universities, in general, and its use has brought positive results for decision-makers, particularly at Allameh Tabataba’i University.

The results obtained from the implementation and execution of the model will bring a clear and positive perspective for decision-makers in universities. To continue this research, another optimization model should be designed and implemented for each faculty, taking into account the limitations of each department