Conjugate gradient methods play a vital role in finding solutions of large-scale optimization problems due to their simplicity to implement, low memory requirements and as well as their convergence properties. In this paper, we propose a new conjugate gradient method that has a direction satisfying the sufficient descent property. We establish global convergence of the new method under the strong Wolfe line search conditions. Numerical results show that the new method performs better than other relevant methods in the literature. Furthermore, we use the new method to solve a portfolio selection problem.
{"title":"A modified nonlinear conjugate gradient algorithm for unconstrained optimization and portfolio selection problems","authors":"T. Diphofu, P. Kaelo, A. Tufa","doi":"10.1051/ro/2023037","DOIUrl":"https://doi.org/10.1051/ro/2023037","url":null,"abstract":"Conjugate gradient methods play a vital role in finding solutions of large-scale optimization problems due to their simplicity to implement, low memory requirements and as well as their convergence properties. In this paper, we propose a new conjugate gradient method that has a direction satisfying the sufficient descent property. We establish global convergence of the new method under the strong Wolfe line search conditions. Numerical results show that the new method performs better than other relevant methods in the literature. Furthermore, we use the new method to solve a portfolio selection problem.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88410069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under the mean-variance framework, we study the continuoustime optimal reinsurance and investment problem with a common shock and a random exit time. To describe the influence of the common shock, we propose a new interdependence mechanism between the insurance market and the financial market. It can reflect both the impact of the occurrence of a common shock and its influence degree on the two markets. Both the termination times of reinsurance and investment are random, and the random exit time is affected simultaneously by exogenous and endogenous random events. The insurer’s objective is to minimize the variance of her terminal wealth under a given level of expected terminal wealth. We derive the explicit optimal reinsurance-investment strategy by employing stochastic optimal control and Lagrange duality techniques. The influences of the market interdependence and the random exit time on the optimal strategy are demonstrated through numerical experiments. The results reveal some meaningful phenomena and provide insightful guidance for reinsurance and investment practice in reality.
{"title":"Optimal reinsurance and investment with a common shock and a random exit time","authors":"Zhiping Chen, Peng Yang, Y. Gan","doi":"10.1051/ro/2023036","DOIUrl":"https://doi.org/10.1051/ro/2023036","url":null,"abstract":"Under the mean-variance framework, we study the continuoustime optimal reinsurance and investment problem with a common shock and a random exit time. To describe the influence of the common shock, we propose a new interdependence mechanism between the insurance market and the financial market. It can reflect both the impact of the occurrence of a common shock and its influence degree on the two markets. Both the termination times of reinsurance and investment are random, and the random exit time is affected simultaneously by exogenous and endogenous random events. The insurer’s objective is to minimize the variance of her terminal wealth under a given level of expected terminal wealth. We derive the explicit optimal reinsurance-investment strategy by employing stochastic optimal control and Lagrange duality techniques. The influences of the market interdependence and the random exit time on the optimal strategy are demonstrated through numerical experiments. The results reveal some meaningful phenomena and provide insightful guidance for reinsurance and investment practice in reality.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79737794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Truong Ngoc Cuong, Hwan-Seong Kim, Le Ngoc Bao Long, S. You
Dynamical analysis and management optimization of supply chain system are explored by utilizing four-stage hyperchaotic Lorenz-Stenflo equation. The supply chain risks are represented by parametric variations and disturbance against disruptions. Nonlinear behaviors are intensely investigated by eigenvalue and bifurcation analysis to identify supply chain risks. Then phase portraits are presented to illustrate the bullwhip effect influencing various stages of multi-echelon supply chains. Along with dynamic identification, resilient supply chains have been developed by realizing an adaptive fractional-order controller. By employing control theory on managerial applications, efficient algorithm can be implemented for optimization problems while reducing potential volatility. Performance criteria have been exploited to validate the control methodology. Based on management algorithms, decision-makers cope with chaos suppression and synchronization problems effectively, ensuring sustainability and reliability. By utilizing control theory, the decision-making strategy can offer new insights into how to effectively manage digital supply chain networks against market volatility.
{"title":"Decision support system for managing multi-echelon supply chain networks against disruptions using adaptive fractional order control algorithm","authors":"Truong Ngoc Cuong, Hwan-Seong Kim, Le Ngoc Bao Long, S. You","doi":"10.1051/ro/2023035","DOIUrl":"https://doi.org/10.1051/ro/2023035","url":null,"abstract":"Dynamical analysis and management optimization of supply chain system are explored by utilizing four-stage hyperchaotic Lorenz-Stenflo equation. The supply chain risks are represented by parametric variations and disturbance against disruptions. Nonlinear behaviors are intensely investigated by eigenvalue and bifurcation analysis to identify supply chain risks. Then phase portraits are presented to illustrate the bullwhip effect influencing various stages of multi-echelon supply chains. Along with dynamic identification, resilient supply chains have been developed by realizing an adaptive fractional-order controller. By employing control theory on managerial applications, efficient algorithm can be implemented for optimization problems while reducing potential volatility. Performance criteria have been exploited to validate the control methodology. Based on management algorithms, decision-makers cope with chaos suppression and synchronization problems effectively, ensuring sustainability and reliability. By utilizing control theory, the decision-making strategy can offer new insights into how to effectively manage digital supply chain networks against market volatility.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89628178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to effectively obtain the electric power emergency command plan and reasonably dispatch emergency resources, the electric power emergency command system architecture based on association rule algorithm is designed according to the modularization idea. In the hardware part, the sensing layer is used to collect operation data, and the service layer is used to analyze and mine the data. The tool layer establishes the basic component library and the power industry component library. The network layer transmits relevant information to the application layer. The application layer realizes the three-dimensional simulation and visual operation of power emergency command such as resource scheduling and fault repair by calling the tool set. In the software part, the association rule algorithm combined with online analytical processing technology is used to mine the effective data in the collected data set to realize the analysis and processing of emergency command related work. Experiments show that the system can effectively command the output of the generator and obtain higher transmission margin when the fault occurs. And reasonably and evenly dispatch emergency resources to avoid resource waste.
{"title":"Architecture and modular design of power emergency command system based on association rule algorithm","authors":"Qian Wang, Mingming Liu","doi":"10.1051/ro/2023031","DOIUrl":"https://doi.org/10.1051/ro/2023031","url":null,"abstract":"In order to effectively obtain the electric power emergency command plan and reasonably dispatch emergency resources, the electric power emergency command system architecture based on association rule algorithm is designed according to the modularization idea. In the hardware part, the sensing layer is used to collect operation data, and the service layer is used to analyze and mine the data. The tool layer establishes the basic component library and the power industry component library. The network layer transmits relevant information to the application layer. The application layer realizes the three-dimensional simulation and visual operation of power emergency command such as resource scheduling and fault repair by calling the tool set. In the software part, the association rule algorithm combined with online analytical processing technology is used to mine the effective data in the collected data set to realize the analysis and processing of emergency command related work. Experiments show that the system can effectively command the output of the generator and obtain higher transmission margin when the fault occurs. And reasonably and evenly dispatch emergency resources to avoid resource waste.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77758231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Faizan Ahemad, Ahmad Zaman Khan, M. Mehlawat, Pankaj Gupta, Sankar Kumar Roy
In this paper, the COPRAS (Complex Proportional Assessment) method is ex- tended for interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs) to solve multi-attribute group decision-making (MAGDM) problems. A novel distance measure for IVq-ROFNs is proposed, and its properties are also probed. This distance measure is used in an improved weights determination method for decision-makers. A weighted projection optimization model is developed to evaluate the completely unknown attributes' weights. The projection of assessment values is defined by the positive and negative ideal solutions, which determine the resemblance between two objects by considering their directional angle. An Indian cities' ranking problem for a better solid waste management infrastructure is solved using the proposed approach based on composite indicators, like recycling waste, greenhouse gas emissions, waste generation, land filling waste, recycling rate, waste to energy rate, and composting waste. Numerical comparisons, sensitivity analysis, and other relevant analyses are performed for validation.
{"title":"Multi-attribute group decision-making for solid waste management using interval-valued q-rung orthopair fuzzy COPRAS","authors":"Faizan Ahemad, Ahmad Zaman Khan, M. Mehlawat, Pankaj Gupta, Sankar Kumar Roy","doi":"10.1051/ro/2023033","DOIUrl":"https://doi.org/10.1051/ro/2023033","url":null,"abstract":"In this paper, the COPRAS (Complex Proportional Assessment) method is ex- tended for interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs) to solve multi-attribute group decision-making (MAGDM) problems. A novel distance measure for IVq-ROFNs is proposed, and its properties are also probed. This distance measure is used in an improved weights determination method for decision-makers. A weighted projection optimization model is developed to evaluate the completely unknown attributes' weights. The projection of assessment values is defined by the positive and negative ideal solutions, which determine the resemblance between two objects by considering their directional angle. An Indian cities' ranking problem for a better solid waste management infrastructure is solved using the proposed approach based on composite indicators, like recycling waste, greenhouse gas emissions, waste generation, land filling waste, recycling rate, waste to energy rate, and composting waste. Numerical comparisons, sensitivity analysis, and other relevant analyses are performed for validation.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74720360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Corporate environmental responsibility has received considerable attention, but how different online promotion formats and pricing sequences affect enterprises’ incentives in their fulfillment of environmental responsibilities and achievement of profitability has not yet been fully understood. In our study, we consider a supply chain in which a manufacturer invests in green technology to decrease the carbon footprint. The main issue addressed here is how the manufacturer selects the optimal online selling format between agency selling and reselling to ensure profitability with a lower carbon footprint. In an uncertain market with considering the pricing sequence in traditional channel and online channel, we explore six scenarios under the dual-channel promotion, and meanwhile discuss the benchmark scenario without introducing online promotion. The results show that the pricing sequence under agency selling format will not have any impact on the equilibrium green degree and pricing strategies, whereas it may result in a improved green degree in reselling format, even higher than it under the agency selling. Further- more, under the interaction of uncertain demand, pricing sequence and green technology investment, we reveal that the agent selling is not always superior to the reselling with a double-marginalization effect, which contrary to the general intuition. Specifically, in an optimistic market, the manufacture is more profitable in reselling format when the promotion price set in online channel prior to traditional channel, and in a pessimistic market, the opposite pricing sequence formulated will bring a considerable benefit to the manufacture.
{"title":"Online promotion format selection for a supply chain with environment responsibility in an uncertain market","authors":"Qunli Wu, Xinxin Xu, Hengtian Wang, Y. Tian","doi":"10.1051/ro/2023032","DOIUrl":"https://doi.org/10.1051/ro/2023032","url":null,"abstract":"Corporate environmental responsibility has received considerable attention, but how different online promotion formats and pricing sequences affect enterprises’ incentives in their fulfillment of environmental responsibilities and achievement of profitability has not yet been fully understood. In our study, we consider a supply chain in which a manufacturer invests in green technology to decrease the carbon footprint. The main issue addressed here is how the manufacturer selects the optimal online selling format between agency selling and reselling to ensure profitability with a lower carbon footprint. In an uncertain market with considering the pricing sequence in traditional channel and online channel, we explore six scenarios under the dual-channel promotion, and meanwhile discuss the benchmark scenario without introducing online promotion. The results show that the pricing sequence under agency selling format will not have any impact on the equilibrium green degree and pricing strategies, whereas it may result in a improved green degree in reselling format, even higher than it under the agency selling. Further- more, under the interaction of uncertain demand, pricing sequence and green technology investment, we reveal that the agent selling is not always superior to the reselling with a double-marginalization effect, which contrary to the general intuition. Specifically, in an optimistic market, the manufacture is more profitable in reselling format when the promotion price set in online channel prior to traditional channel, and in a pessimistic market, the opposite pricing sequence formulated will bring a considerable benefit to the manufacture.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75215453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum error correction codes (QECC) play a fundamental role in protecting the information processed in today’s noisy quantum computers. To build good error correction schemes, it is essential to understand how noise affects the behavior of these codes. In this research paper, we analyze Steane code, a 7-qubit QECC, using a randomized benchmarking (RB) protocol. With RB protocols, we can partially characterize the quality of implementation of a set of quantum gates. We show a scenario where Steane code with one logical qubit is advantageous compared to the situation with no quantum code. We obtained our results using a quantum simulator with custom noise models considering different numbers of noisy qubits.
{"title":"Steane code analysis by randomized benchmarking","authors":"A. Barbosa, F. Marquezino, R. Portugal","doi":"10.1051/ro/2023030","DOIUrl":"https://doi.org/10.1051/ro/2023030","url":null,"abstract":"Quantum error correction codes (QECC) play a fundamental role in protecting the information processed in today’s noisy quantum computers. To build good error correction schemes, it is essential to understand how noise affects the behavior of these codes. In this research paper, we analyze Steane code, a 7-qubit QECC, using a randomized benchmarking (RB) protocol. With RB protocols, we can partially characterize the quality of implementation of a set of quantum gates. We show a scenario where Steane code with one logical qubit is advantageous compared to the situation with no quantum code. We obtained our results using a quantum simulator with custom noise models considering different numbers of noisy qubits.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86209450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation(PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction.
{"title":"The optimized gate recurrent unit based on improved evolutionary algorithm to predict stock market returns","authors":"Chao Liu, Fengfeng Gao, Qi Zhao, Mengwan Zhang","doi":"10.1051/ro/2023029","DOIUrl":"https://doi.org/10.1051/ro/2023029","url":null,"abstract":"In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation(PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83602719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This purpose of the paper is to explore the optimal price strategy for the retailers under the cross-store full-reduction promotion mode, where speculative consumers will deliberately purchase add-on items to qualify for discounts when the purchase amount is less than the “full-reduction” threshold and then return the add-on items after successful payment. With respect to the optimal decision problem consisting of two online complementary retailers and an e-commerce platform in the face of speculative consumers’ add-on items return behavior, we construct the single-cycle sales decision models based on the revenue sharing contract. Furthermore, through the derivative function analysis method, we examine the effect of the proportion of speculative consumers, the proportion of product sharing discount amount and revenue sharing coefficient on the platform’s sale strategy and the retailers’ the optimal price strategy. The results show that whether the platform implements cross-store full-reduction promotion strategy or not, the product price increases with the increase of revenue sharing coefficient. In addition, under the non-promotion sales mode, the optimal price is not affected by the speculative consumers’ behavior. Under the cross-store full-reduction promotion sales mode, the optimal price changes with the proportion of product sharing discount amount and the proportion of speculative consumers. And the price of only purchasing single product in this case is always higher than the price under the non-promotion sales mode. Finally, we compare the profits under the two scenarios, it is found that the profits under the cross-store full-reduction promotion sales mode are not always higher than that under non-promotion sales mode, and the boundary conditions for the platform to adopt different modes are further given.
{"title":"Optimal pricing strategy of retailers considering speculative customers' add-on items return behavior with cross-store full-reduction promotion","authors":"S. Song, W. Peng, Y. Zeng","doi":"10.1051/ro/2023028","DOIUrl":"https://doi.org/10.1051/ro/2023028","url":null,"abstract":"This purpose of the paper is to explore the optimal price strategy for the retailers under the cross-store full-reduction promotion mode, where speculative consumers will deliberately purchase add-on items to qualify for discounts when the purchase amount is less than the “full-reduction” threshold and then return the add-on items after successful payment. With respect to the optimal decision problem consisting of two online complementary retailers and an e-commerce platform in the face of speculative consumers’ add-on items return behavior, we construct the single-cycle sales decision models based on the revenue sharing contract. Furthermore, through the derivative function analysis method, we examine the effect of the proportion of speculative consumers, the proportion of product sharing discount amount and revenue sharing coefficient on the platform’s sale strategy and the retailers’ the optimal price strategy. The results show that whether the platform implements cross-store full-reduction promotion strategy or not, the product price increases with the increase of revenue sharing coefficient. In addition, under the non-promotion sales mode, the optimal price is not affected by the speculative consumers’ behavior. Under the cross-store full-reduction promotion sales mode, the optimal price changes with the proportion of product sharing discount amount and the proportion of speculative consumers. And the price of only purchasing single product in this case is always higher than the price under the non-promotion sales mode. Finally, we compare the profits under the two scenarios, it is found that the profits under the cross-store full-reduction promotion sales mode are not always higher than that under non-promotion sales mode, and the boundary conditions for the platform to adopt different modes are further given.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86410020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
this study considers the importance of small and medium-sized industrial units for economic growth, social cohesion, and regional and local development. It presents a merger model to use each other's capacities and facilities to achieve higher efficiency levels. First, the involved criteria have been chosen using the SCOR model, considering sustainability, resilience and agility criteria in each part of the supply chain network. Next, PCA was used to reduce the dimensionality and the efficiency of units has been determined by network DEA. A mathematical model was used to determine the best combination for the merger. The model chosen for the finalization of the merger process is inverse network DEA, which tries to determine the final inputs of the merged units for a specific target. In addition to theoretical benefits, the results have practical applications. The results can give supply chain partners a common language for better communication and help them settle on standardized definitions. The model has been implemented using real-world data from other articles on 26 stone industries in Iran. The DEA and mathematical models have been solved through GAMS and the PCA approach through MATLAB.
{"title":"An integrated approach for the merger of small and medium-sized industrial units","authors":"Haniyeh Moazeni, B. A. Shirani, Seyed Reza Hejazi","doi":"10.1051/ro/2023027","DOIUrl":"https://doi.org/10.1051/ro/2023027","url":null,"abstract":"this study considers the importance of small and medium-sized industrial units for economic growth, social cohesion, and regional and local development. It presents a merger model to use each other's capacities and facilities to achieve higher efficiency levels. First, the involved criteria have been chosen using the SCOR model, considering sustainability, resilience and agility criteria in each part of the supply chain network. Next, PCA was used to reduce the dimensionality and the efficiency of units has been determined by network DEA. A mathematical model was used to determine the best combination for the merger. The model chosen for the finalization of the merger process is inverse network DEA, which tries to determine the final inputs of the merged units for a specific target. In addition to theoretical benefits, the results have practical applications. The results can give supply chain partners a common language for better communication and help them settle on standardized definitions. The model has been implemented using real-world data from other articles on 26 stone industries in Iran. The DEA and mathematical models have been solved through GAMS and the PCA approach through MATLAB.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82652573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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