Invasive Weed Optimization Algorithm for Minimizing Total Weighted Earliness and Tardiness Penalties on a Single Machine under Aging Effect

Yazdani, Maziar; Ghodsi, Reza

doi:10.15406/iratj.2017.02.00006

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…Even though there is a clear potential for Operations Research (OR) to assist health decision-makers [ 8 ], as evidenced by numerous successful OR applications to the other domains, there is a clear research gap in employing OR techniques in providing more reliable services for stroke patients. Operation Research is a field of study that uses advanced analytical methods to understand complex systems and make the right decisions [ 9 , 10 , 11 ]. Simulation techniques [ 12 , 13 , 14 , 15 ], mathematical optimisation [ 16 , 17 ], and decision analysis [ 18 ] are just a few of the ways OR helps enterprises and organisations improve their operations.…”

Section: Introductionmentioning

confidence: 99%

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

et al. 2021

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Stroke is the biggest cause of adult disability and the third biggest cause of death in the US. Stroke is a medical emergency, and the treatment given in the early hours is important in shaping the patient’s long-term recovery and prognosis. Despite the fact that substantial attention has been dedicated to this complex and difficult issue in healthcare, novel strategies such as operation research-based approaches have hardly been used to deal with the difficult challenges associated with stroke. This study proposes a novel approach with data envelopment analysis (DEA) and multi-objective linear programming (MOLP) in hospitals that provide stroke care services to select the most efficient approach, which will be a new experiment in literature perception. DEA and MOLP are widely used for performance evaluation and efficiency measurement. Despite their similarities and common concepts, the two disciplines have evolved separately. The generalised DEA (GDEA) cannot incorporate the preferences of decision-makers (DMs) preferences and historical efficiency data. In contrast, MOLP can incorporate the DM’s preferences into the decision-making process. We transform the GDEA model into MOLP through the max-ordering approach to (i) solve the problem interactively; (ii) use the step method (STEM) and consider DM’s preferences; (iii) eliminate the need for predetermined preference information; and (iv) apply the most preferred solution (MPS) to identify the most efficient approach. A case study of hospitals that provide stroke care services is taken as an example to illustrate the potential application of the proposed approach method.

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Section: Introductionmentioning

confidence: 99%

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

et al. 2021

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“…Searching for a novel machine learning and optimization approaches to find the most appropriate model is still an open problem (Yazdani and Ghodsi, 2017; Yazdani and Jolai, 2016; Yazdani et al , 2017a). Thus, in responding to this research gap, in the current study, the combination of the above-mentioned machine learning and optimization approaches/methods will be a new experiment in literature perception.…”

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

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Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

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“…Searching for a novel machine learning and optimization approaches to find the most appropriate model is still an open problem [7][8][9]. Thus, in responding to this research gap, in current study the combination of the above-mentioned machine learning and optimization will be a new experiment in literature perception.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

et al. 2020

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Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.

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Invasive Weed Optimization Algorithm for Minimizing Total Weighted Earliness and Tardiness Penalties on a Single Machine under Aging Effect

Cited by 13 publications

References 14 publications

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

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