مجله مدیریت تولید و عملیات
سال یازدهم شماره 3 (پیاپی 22، پاییز 1399)

 This paper aims to illustrate the use of centralized data envelopment analysis with the variable return to scale in resource allocation at different wards of hospitals based on efficiency.

Design/methodology/approach: 

Data were collected from the statistics unit, accounting unit and the hospital infection unit for 31 clinical wards in Alzahra hospital, Isfahan, Iran, in the last quarter of 2011. The input sets represented by the number of beds, number of physicians, number of nurses and the physical space allocated to the ward, are used in the analysis. Three output sets consisted of the number of inpatients, beds turnover, beds occupancy ratio, and the number of consultants provided by the ward to represent the performance and activity of the ward. The radial centralized DEA based on the input-oriented and variable return to scale model was applied using MatLab software, to find out how to reallocate the hospital resources to the wards.

 The values of inputs and outputs obtained from the centralized DEA model helps the centralized decision-maker to take the right remedial actions for continuous improvement. The findings indicated that in this study the total number of active beds is proposed to be decreased from 739 to 343.99, the total number of physicians from 399 to 391.51, the total number of nurses from 1258 to 1239.21, and the total allocation of space to the wards from 19010 to 15668.69 square meters.

Research limitations/implications: 

The proposed approach is rather simple and it can easily be extended in different directions, allowing, for example, the consideration of joint constraints, goals, bounds on the changes of inputs and outputs, or on the allocated resources, but then one must be aware that such constraints are specifically defined for the case study and may be different in other hospitals. In this study, two constraints were considered to control the number of allocated nurses to two wards: ED and ICU, based on the current standards. Also, the proposed model was based on the criteria for which the data was available. It is clear that a centralized resource allocation approach implies a subordination of the behaviour of individual units to the goals of the system as a whole

Practical implications: 

The results of the proposed centralized DEA model based on the performance criteria provide useful managerial implications for the resource allocation of hospital wards. The study proves the usefulness of centralized DEA as a decision-making tool in the health sector. Also, instead of reducing the inputs of any ward, the total input consumption of the wards is reduced, and as it is guaranteed, the total output production does not decrease.

Social implications:

 Public health and way to provide health care is an important issue in all countries, and health care providers trying within an existing resource, to provide the highest quality care. Allocating the resources in hospital wards based on the proper criteria increases the quality of the care and the efficiency of the healthcare sector.

Originality/ value:

 Many prior DEA studies have focused on health care efficiency. This paper is probably one of the first attempts that use a centralized model to analysis different hospital wards efficiency. Since resource allocation mechanisms can be influenced by the behaviour of the organization, the optimal allocation of a resource based on the needs is an important task of the health system. Furthermore, selecting the most appropriate set of input and output criteria is an important step in any efficiency measurement study.

This paper aims to design a three-level sustainable dairy supply chain under uncertain conditions. Mixed-integer nonlinear programming proposed to optimize the total costs taking into account the environmental and social considerations of a multi-period and multi-product chain applied to a dairy company. The delivery time and the first-in-first-out (FIFO) policy of the products are of particular importance in the proposed model.

Design/methodology/approach: 

A dairy supply chain in Iran investigated as a real case study and the authors considered various numerical experiments, and evaluated the developed model. The primary sources of data used in this study were from the Pak Company. The suggested mathematical model coded in the GAMS 2017 software. Due to the uncertainty of economic parameters and demand, a robust-heuristic optimization approach used to solve the proposed model.

Results indicated that the values of the objective function were close together in the deterministic and robust conditions. This finding outlined the proper performance of the model under uncertainty.The outputs of the three solution methods (MINLP, MILP, and the heuristic method), indicated a higher heuristic approach performance regarding the two other approaches based on the value of the objective function and the time to solve the problem. Besides, based on the sensitivity analysis, the objective function displayed a direct relationship with the demand change, and as demand increased at different levels of uncertainty, costs increased either. Furthermore, the impact of product life cycle on cost target was different and in each scenario, increasing product lifespan led to reduced costs of the supply chain with different scenarios.

Research limitations/implications: 

The most significant research limitations were the lack of issues related to production planning such as integrated production strategies and overtime, consideration of equal volume and weight capacity for vehicles, and integration of three aspects of sustainability in a goal function. Studying issues related to production planning, including production under different strategies of integrated production and overtime; the use of diverse and heterogeneous vehicles and the selection of a suitable one for transporting raw materials and products; multi-objective mathematical modelling; and the development of meta-heuristic algorithms to solve the model are suggestions for future research.

Practical implications: 

According to the results, conducting robust models can lead managers to make integrated decisions in uncertain situations. They can decide about the optimal flow of materials in the production of flexible products in a dairy supply chain. The perishability of products (especially products with a short shelf life) adds the complexity of inventory. Therefore, managers can minimize inventory costs by considering the exit issuance system in the order of product entry. Besides, if the company attempts to move to the triple bottom line evaluation of economic, environmental, and social performance, it can use the proposed structure and research solution model.

Social implications: 

The results of the model presented to the meeting of the company's board of directors. Since the proposed model provides a framework for measuring social responsibility, highly recognized by managers, and all agreed on its applicability to the company. This model creates values that lead the company to move towards social responsibility, from which it was far in recent years. Due to the various lifespan of products and their importance in environmental pollution, production managers in the dairy supply chain should adopt appropriate decisions in the production system with highest durability and minimal environmental pollution, which will lead to economic growth in this industry.

Originality/value:

 Compared to previous studies, this study is one of the first quantitative studies wherein, a multi-level/multi-product supply chain considered in the dairy industry.In this paper, the social costs of sustainability such as job creation cost for individuals, layoffs, and lost working days simultaneously investigated concerning economic and environmental perspectives in the uncertain environment (demand, transportation costs, production costs, holding costs, and capacity of facilities considered as uncertain parameters). Since dairy products widely used in delivering food in the daily diet plan, due to the risky distribution of dairy products, the authors recognized the loss of quality and expiry date of products in the proposed model. Due to the high demand for dairy products, subjects such as time for delivery, and first-in-first-out (FIFO) policy of products were of particular importance in the proposed model.

 This study aims to propose an approach for optimizing equipment suppliers in multi-state systems. The proposed solution examined in the water supply system (FWS) of heat recovery boiler (HRSG), manufactured by Mapna Boiler Engineering and Manufacturing Company. The assumed system consists of two main subsystems, equipped with three and two pieces in the first and second subsystems. With the failure of any equipment, if spare equipment is available in the warehouse, this equipment will replace the disabled equipment. Still, the unreplaced equipment decreases the capacity of the system. The system works until the number of healthy devices in each subsystem is more than the specific limits; otherwise, the system stops completely.This research aims to minimize total cost in two phases of construction and operation of an industrial system. The cost of building the system is the same as the cost of purchasing equipment from suppliers. The operating cost included maintaining spare equipment, reducing capacity, and shutting down the system. The quality of installed and spared equipment can vary. The holding cost of spared equipment sourced from different suppliers may also vary due to the differences in value, insurance costs, and maintenance conditions. This research aims to provide an approach for selecting suppliers of leading equipment, extensions, and spare parts. It is important to note that the use of quality equipment increases the cost of the construction phase. On the other hand, it improves the probability of failure and, consequently, the cost of reducing capacity and shutting down the system during the operation phase. This model minimizes the system's total cost in two phases of construction and operation based on the availability criterion.

Design/methodology/approach: 

The initial step was drawing the block diagram of system reliability. This step resulted in the determined failure effect of each component on the state of the system. Then, the Markov-chain develops a parametric model of the probability of the system under different situations. The Markov-chain results summarized the likelihood of the system under any full-load, half-load, or failed states. The mathematical model used the results of the obtained coefficients to calculate the costs of purchasing and maintaining the major and spared equipment and the expected costs of reducing the capacity and stopping in the desired period. The final model used such results in the form of a constraint set in a sizable mathematical programming model solved by GAMS software.

 The model aims to minimize all system costs, including construction cost (purchase) and operating cost (i.e., maintenance, half-load, and shutdown). The analysis of the results indicated that considering the total cost of construction and operation phases in a single model more effectively, optimizes the overall system cost. The model’s solution results implied the cost of capacity reduction as the highest cost because the system under study would be in a semi-load condition for a long time. The results also highlighted that the change in equipment maintenance cost did not affect the other costs of the system, and it only caused a linear shift in maintenance cost and total cost. However, when the increased cost was too much, the supplier selection strategy changed. The mathematical model suggested buying some equipment from another supplier to minimize the total cost, which reduced total maintenance cost instead of increasing it. Change of the hourly cost of the system half-load working affected the consequence of system capacity reduction only. It had little effect on other expenses until the cost-per-hour of capacity reduction significantly reduced. In case of an optimal response, the suppliers of some equipment changed, which resulted in modified additional cost values. The change in the hourly cost of half-load working had little effect on the optimal response. Only a significant reduction in this cost changed some equipment suppliers and affected the other expenses. The change in the hourly cost of downtime resulted in purchasing equipment from other suppliers, which changed other expenses.

Rearch limitations/implications: 

The lack of failure information registration system in the country's power plants was the most critical limitation in collecting necessary information and developing a computational model. For future research, the resulting model can be closer to the real situation by considering equipment and uncertainty's repairability and changing the failure rate over time.

Practical implications:

 Applying the proposed approach reduced the cost of construction and operation of power plants in the country. Practitioners can use this approach in selecting the system equipment with different prices and technical specifications from the market.

 Social implications:

 Implementation of research results increases social welfare by increasing electricity production and reducing pollution.

 Originality/value: 

The proposed mathematical model innovatively integrates construction and operation phases costs using Markov-chain results and uses the results to optimize a typical industrial system.

This paper aims to propose a mixed approach for evaluating preventive maintenance performance based on anti-fragility factors in Iranian Gas Transmission Company (IGTC) district 10.

Design/methodology/approach: 

In the quantitative part of this mixed research, using the Fuzzy Delphi Method (FDM), out of 131 key performance indicators taken from different standards, 16 indicators identified suitable for the activities of IGTC district 10. In the qualitative section, using the thematic analysis method, ten organizational and academic experts participated, and anti-fragility components in the form of 254 open codes, 18 organizing codes, and two comprehensive codes identified and categorized using Maxqda2020 software. The effect of these indicators on maintenance key performance indicators investigated using the canonical correlation method.

Findings indicated that learning and growth, panarchy, heuristics, harmonization, and Barbell strategy of behavioural components, and redundancy, participation system, and high reliability of structural components had the highest impact on key maintenance performance indicators including condition-based maintenance cost, average downtime, percentage of accidents involving personnel, and the mean time between failures.

 Research limitations/implications: 

The most important limitation of this research is that due to the case study unique characteristics, the results could not be generalized to other companies. Besides, the proposed model developed based on anti-fragility factors that are not available simply or difficult to define for every company. So, it is suggested that in order to apply the proposed model, first the anti-fragility factors for the company should be localized.

 Practical implications: 

By applying the proposed model, companies can expect a significant decrease in the total preventive maintenance cost and human injuries caused by defects and breakdowns. Also, managerial planning in the preventive maintenance system toward the resource allocation to the equipment and PM personnel will be simple.

Social implications: 

Recognizing anti-fragility factors and reducing irreparable human and technical damages will lead to positive employee and social attitude towards the company. Also, the proper performance of a preventive maintenance system will result in a clean and carbon-free environment.  

Originality/value :

The proposed approach is suitable for evaluating preventive maintenance performance based on anti-fragility factors in companies. The use of quantitative and qualitative analysis in the proposed methodology was not considered in the reviewed literature, simultaneously. So this approach can be a model for other subsidiaries in the oil and gas industry to follow.

This paper aims to study the intensity effect of the significant blockchain factors in the food industry. Also, it develops the application of blockchain technology in the food industry and identifies the relationship between the influencing indicators of blockchain in this area.

 Design/methodology/approach:

 This paper is typically qualitative-descriptive research in which the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method used for analysis. For this purpose, 950 questionnaires sent to the university professors and experts of blockchain technology in Iran and abroad. Among the received answers, only 19 respondents completed the questionnaires. The study population included academic experts who were aware of the blockchain field. The selected experts were from the research and social network of Researchgate, university faculty members and researchers with at least one published paper on the blockchain application. The relationships between the indicators and the effect intensity between them measured by the fuzzy DEMATEL method.  

The literature reviewed comprehensively and resulted in 16 extracted influencing indicators of the blockchain in the food industry. After reviewing and merging, they reduced to 8. Finally, by the fuzzy DEMATEL method, their effective relationship determined. The influencing indicators identified included food waste prevention, smart contract, simplification of international transactions, rapid identification of organic products, supply chain coordination and cost reduction, tracking and prevention of fraud in the food industry, permanent and secure storage of information and balancing the pricing process. Research findings indicated that the traceability and fraud prevention index was the most influencing indicator. Also, food waste prevention had the most relationship with the other variables, and finally, the smart contract was the most influencing indicator.

 Research limitations/implications: 

Blockchain technology has recently emerged in the food industry and supply chain. Blockchain is a solution that can increase integration and productivity in the food industry by having a positive impact on indicators such as traceability, fraud prevention, supply chain coordination, and cost reduction. Due to the various features of the emerging technology, the food and agriculture industries evolute significantly. Based on a comprehensive literature review, the conceptual model proposed and the extracted indicators and their effect on each other identified. The fuzzy DEMATEL method used and based on the viewpoints of experts, the cause-and-effect relationships identified. Also, there was no evidence of implementing blockchain in the country, and therefore it was not possible to test the model. Data gathering performed by questionnaires and created a limitation in the exact evaluation of the model.

Practical implications: 

This study outlined the role of a reliable tracking system in the food industry in improving the efficiency of other indicators in this area. Blockchain is also a way to solve traceability issues while achieving transparency. This study motivates the managers and decision-makers at the macro level to take advantage of blockchain technology to have a reliable tracking system.

Social implications: 

The food industry is highly critical and significant due to its direct relationship with human health. As a result, it is necessary that the food that reaches the consumer is of the highest safety and quality and has a reliable tracking system. We introduce blockchain as a technology that has great potential to achieve such benefits.

 Originality/value: 

The literature review indicated that no study performed on blockchain technology in the food supply chain of Iran. Therefore, it is necessary to fill the existing literature gap by conducting this research and ultimately enrich the existing scientific resources in this field.

Determining preventive maintenance intervals is critically significant from a variety of aspects, such as cost, reliability, and downtime. Currently, in many industries, such intervals are determined based on the viewpoints of experts and technicians or the manufacturer's recommendations in the manual. Determining an optimal interval, by the means of scientific methods, can have many effects in terms of costs, reliability, and downtime. For example, if the interval is assumed as very short, it can increase the cost of spare parts or manpower. Also, if the interval is too long, it increases the possibility of machine failure and downtime. For this purpose, organizations that apply the approach of time-based preventive maintenance should consider the above-mentioned criteria. However, such criteria are not compatible with each other, and each of them has its behavior. Therefore, given the existence of more than one criterion in this decision-making problem and the incompatibility of the criteria, Multi-Criteria Decision-Making (MCDM) methods seem appropriate for this purpose.

 A component of a machine that is currently replaced periodically (and not necessarily at equal intervals) is considered for investigation. It is assumed that the failure distribution function of the component follows the Weibull distribution. First, using the Maximum Likelihood Estimation (MLE) technique, new and more general equations are presented to estimate the parameters of the failure distribution function. Subsequently, three important criteria are taken into account, including reliability, cost, and downtime due to failure and preventive replacement.. Given the existence of several criteria with different behaviors, as well as the vague or linguistic nature of human judgments, a hybrid MCDM model including Fuzzy Analytic Hierarchy Process (FAHP) and Viekriterijumsko kompromisno rangiranje (VIKOR) is proposed.

To indicate the applicability of the proposed model, a real case was investigated in the Isfahan Steel Company. For this purpose, a circuit breaker switch of a compressor in the oxygen workshop was studied. The component was replaced at about a 200 days interval. After implementing the FAHP method, it was found that the reliability criterion was more important than the other two criteria. This is due to the high sensitivity of this component and the consequences of its failure. After Performing the VIKOR method, the set of top alternatives included replacement intervals between 50 and 90 days. It confirms that the importance of the reliability criterion, and the replacement interval, should be reduced by at least a half. Based on the results, reliability was expected to improve by 2.6% and the cost and downtime criteria to improve by 33.7% and 28.6%, respectively.

 To implement the proposed method for each component, the following data are required:Preventive maintenance and breakdown records of the component Cost and downtime of the machine due to breakdown and preventive maintenanc.Three pairwise comparisons to implement the FAHP

If information systems exist in the relevant plant, the data for items 1 and 2 are available and do not constitute a constraint on the proposed model. The only limitation of this model is the inputs of the FAHP method for which, implementing the model depends on the humans. Doing three pairwise comparisons for each component may seem a bit difficult and time-consuming. Consequently, the removal of this limitation can be proposed as a suggestion for the development of this study. Other calculations of this model can be done automatically. Also, the study of other probability distribution functions such as gamma and lognormal can be effective in improving the results of this study.

 To the best of our knowledge, the fuzzy AHP or VIKOR method has not been used to address the subject of this study, even independently. In addition, unlike previous studies, the proposed model was implemented for a component that was undergoing preventive maintenance. In other words, in most of the data collected the component was replaced before failure, which made it impossible to:Discover the component failure distribution function by data fitting. As a result, it can be assumed that the failure distribution follows the Weibull distribution.Applying the existing equations to estimate the parameters of the failure distribution function. As a result, new equations can be proposed by implementing the MLE technique.