To cope with global warming and achieve the goal of carbon emission reduction, various carbon policies have been implemented in many countries and regions. Some studies have indicated that with the popularity of low carbon, a small proportion of consumers show a green purchasing tendency. However, for firms, it is still difficult to predict the green purchase preferences of most consumers, and firms may face greater market demand uncertainty, which will bring profit risks. We assume that a firm’s decision is subject to its behavioral considerations, that is, its risk attitude. Considering the risk preferences of decision-makers and carbon emission policies, this paper addresses decision optimization in operations management. Our purpose is to analyze the optimal production decisions of risk-averse firms under carbon cap and carbon tax policies. Adopting the CVaR (conditional value-at-risk) criterion, we construct an optimization model with the constraints of carbon policies and obtain the analytic formula of optimal solutions. Then, we compare the optimal operational decisions for risk-averse firms and risk-neutral firms and further explore the impact of risk aversion on production and the parameters of carbon policy. Finally, some numerical studies are presented to analyze the carbon policies and risk aversion on firms’ operational decisions and give some managerial insights based on the analysis results.
{"title":"A comparative study on a firm's optimal production decision considering risk aversion and carbon policies","authors":"Q. Qi, Qingguo Bai, Jianteng Xu, Qian Feng","doi":"10.1051/ro/2023059","DOIUrl":"https://doi.org/10.1051/ro/2023059","url":null,"abstract":"To cope with global warming and achieve the goal of carbon emission reduction, various carbon policies have been implemented in many countries and regions. Some studies have indicated that with the popularity of low carbon, a small proportion of consumers show a green purchasing tendency. However, for firms, it is still difficult to predict the green purchase preferences of most consumers, and firms may face greater market demand uncertainty, which will bring profit risks. We assume that a firm’s decision is subject to its behavioral considerations, that is, its risk attitude. Considering the risk preferences of decision-makers and carbon emission policies, this paper addresses decision optimization in operations management. Our purpose is to analyze the optimal production decisions of risk-averse firms under carbon cap and carbon tax policies. Adopting the CVaR (conditional value-at-risk) criterion, we construct an optimization model with the constraints of carbon policies and obtain the analytic formula of optimal solutions. Then, we compare the optimal operational decisions for risk-averse firms and risk-neutral firms and further explore the impact of risk aversion on production and the parameters of carbon policy. Finally, some numerical studies are presented to analyze the carbon policies and risk aversion on firms’ operational decisions and give some managerial insights based on the analysis results.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85537669","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}
Let $mathcal{L}$ be a set which consists of some connected graphs. Let $E$ be a spanning subgraph of graph $G$. It is called a $mathcal{L}$-factor if every component of it is isomorphic to the element in $mathcal{L}$. In this contribution, we give the lower bounds of four parameters ($t(G),$ $I(G), $ $I'(G),$ $operatorname{bind}(G)$) of $G$, which force the graph $G$ admits a $({K_{1,i}:qleq ileq 2q-1}cup {K_{2q+1}})$-factor for $qgeq 2$ and a ${P_2, P_{2q+1}}$-factor for $qgeq 3$ respectively. The tightness of the bounds are given.
{"title":"Toughness and binding number bounds of star-like and path factor","authors":"Xin Feng, Xingchao Deng","doi":"10.1051/ro/2023057","DOIUrl":"https://doi.org/10.1051/ro/2023057","url":null,"abstract":"Let $mathcal{L}$ be a set which consists of some connected graphs. Let $E$ be a spanning subgraph of graph $G$. It is called a $mathcal{L}$-factor if every component of it is isomorphic to the element in $mathcal{L}$. In this contribution, we give the lower bounds of four parameters ($t(G),$ $I(G), $ $I'(G),$ $operatorname{bind}(G)$) of $G$, which force the graph $G$ admits a $({K_{1,i}:qleq ileq 2q-1}cup {K_{2q+1}})$-factor for $qgeq 2$ and a ${P_2, P_{2q+1}}$-factor for $qgeq 3$ respectively. The tightness of the bounds are given.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81356034","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}
The multiple job class scheduling problem arises in contexts where a group of jobs belong to multiple classes and in which if all jobs in the same class are operated together, extra setup times would not be needed. On the other hand, the customer order scheduling problem focuses on finishing all jobs from the same order at the same time in order to reduce shipping costs. However, works on customer orders coupled with class setup times do not appear often in the literature. Hence we address here a bicriteria single machine customer order scheduling problem together with multiple job classes. The optimality criterion minimizes a linear combination of the sum of the ranges and sum of tardiness of all customer orders. In light of the high complexity of the concerned problem, we propose a lower bound formula and a property to be used in a branch-and-bound method for optimal solutions. To find approximate solutions, we then propose four heuristics together with a local search method, four cloudy theoretical simulated annealing and a cloudy theoretical simulated annealing hyperheuristic along with five low-level heuristics. The simulation results of the proposed heuristics and algorithms are analyzed.
{"title":"Sequencing single machine multiple-class customer order jobs using heuristics and improved simulated annealing algorithms","authors":"Win-Chin Lin, Xingong Zhang, Xinbo Liu, Kai Hu, Shuenn-Ren Cheng, Ameni Azzouz, Chin-Chia Wu","doi":"10.1051/ro/2023056","DOIUrl":"https://doi.org/10.1051/ro/2023056","url":null,"abstract":"The multiple job class scheduling problem arises in contexts where a group of jobs belong to multiple classes and in which if all jobs in the same class are operated together, extra setup times would not be needed. On the other hand, the customer order scheduling problem focuses on finishing all jobs from the same order at the same time in order to reduce shipping costs. However, works on customer orders coupled with class setup times do not appear often in the literature. Hence we address here a bicriteria single machine customer order scheduling problem together with multiple job classes. The optimality criterion minimizes a linear combination of the sum of the ranges and sum of tardiness of all customer orders. In light of the high complexity of the concerned problem, we propose a lower bound formula and a property to be used in a branch-and-bound method for optimal solutions. To find approximate solutions, we then propose four heuristics together with a local search method, four cloudy theoretical simulated annealing and a cloudy theoretical simulated annealing hyperheuristic along with five low-level heuristics. The simulation results of the proposed heuristics and algorithms are analyzed.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77758355","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}
Amir Salar Ghafari Majid, Mobina Kazemzadeh, Z. Mohammadnazari, Parastoo Maleki, A. Aghsami, F. Jolai
Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients’ safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.
{"title":"A two-stages mathematical model for a Covid-19 vaccination fuzzy scheme and resource allocation in hub locations considering transmission risk and environment factor","authors":"Amir Salar Ghafari Majid, Mobina Kazemzadeh, Z. Mohammadnazari, Parastoo Maleki, A. Aghsami, F. Jolai","doi":"10.1051/ro/2023053","DOIUrl":"https://doi.org/10.1051/ro/2023053","url":null,"abstract":"Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients’ safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74217588","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 this paper, we propose a short-step feasible full-Newton step path-following interior-point algorithm (IPA) for monotone linear complementarity problems (LCPs). The proposed IPA uses the technique of algebraic equivalent transformation (AET) induced by an univariate function to transform the centering equations which defines the central-path. By applying Newton’s method to the modified system of the central-path of LCP, a new Newton search direction is obtained. Under new appropriate defaults of the threshold τ which defines the size of the neighborhood of the central-path and of θ which determines the decrease in the barrier parameter, we prove that the IPA is well-defined and converges locally quadratically to a solution of the monotone LCPs. Moreover, we derive its iteration bound, namely, ) which coincides with the best-known iteration bound for such algorithms. Finally, some numerical results are presented to show its efficiency.
{"title":"A path-following interior-point algorithm for monotone LCP based on a modified Newton search direction","authors":"Welid Grimes, M. Achache","doi":"10.1051/ro/2023054","DOIUrl":"https://doi.org/10.1051/ro/2023054","url":null,"abstract":"In this paper, we propose a short-step feasible full-Newton step path-following interior-point algorithm (IPA) for monotone linear complementarity problems (LCPs). The proposed IPA uses the technique of algebraic equivalent transformation (AET) induced by an univariate function to transform the centering equations which defines the central-path. By applying Newton’s method to the modified system of the central-path of LCP, a new Newton search direction is obtained. Under new appropriate defaults of the threshold τ which defines the size of the neighborhood of the central-path and of θ which determines the decrease in the barrier parameter, we prove that the IPA is well-defined and converges locally quadratically to a solution of the monotone LCPs. Moreover, we derive its iteration bound, namely,\u0000) which coincides with the best-known iteration bound for such algorithms. Finally, some numerical results are presented to show its efficiency.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89451445","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}
Helmuth O. M. Silva, Diego P. Rubert, Elói Araújo, E. Steffen, Daniel Doerr, F. V. Martinez
Ancestral reconstruction is a classic task in comparative genomics. Here, we study the genome median problem, a related computational problem which, given a set of three or more genomes, asks to find a new genome that minimizes sum of pairwise distances between it and the given genomes. The distance stands for the amount of evolution observed at the genome level, for which we determine the minimum number of rearrangement operations necessary to transform one genome into the other. For almost all rearrangement operations the median problem is NP-hard, with the exception of the breakpoint median that can be constructed efficiently for multichromosomal circular and mixed genomes. In this work we study the median problem under a restricted rearrangement measure called c4-distance, which is closely related to the breakpoint and the DCJ distance. We identify tight bounds and decomposers of the c4-median and develop algorithms for its construction, one exact ILP-based and three combinatorial heuristics. Subsequently, we perform experiments on simulated data sets. Our results suggest that the c4-distance is useful for the study the genome median problem, from theoretical and practical perspectives.
{"title":"Algorithms for the genome median under a restricted measure of rearrangement","authors":"Helmuth O. M. Silva, Diego P. Rubert, Elói Araújo, E. Steffen, Daniel Doerr, F. V. Martinez","doi":"10.1051/ro/2023052","DOIUrl":"https://doi.org/10.1051/ro/2023052","url":null,"abstract":"Ancestral reconstruction is a classic task in comparative genomics. Here, we study the genome median problem, a related computational problem which, given a set of three or more genomes, asks to find a new genome that minimizes sum of pairwise distances between it and the given genomes. The distance stands for the amount of evolution observed at the genome level, for which we determine the minimum number of rearrangement operations necessary to transform one genome into the other. For almost all rearrangement operations the median problem is NP-hard, with the exception of the breakpoint median that can be constructed efficiently for multichromosomal circular and mixed genomes. In this work we study the median problem under a restricted rearrangement measure called c4-distance, which is closely related to the breakpoint and the DCJ distance. We identify tight bounds and decomposers of the c4-median and develop algorithms for its construction, one exact ILP-based and three combinatorial heuristics. Subsequently, we perform experiments on simulated data sets. Our results suggest that the c4-distance is useful for the study the genome median problem, from theoretical and practical perspectives.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78241745","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}
Fuzzy reasoning is the subject of fuzzy system where the fuzzy set is characterized by the randomization of the variable associated in the fuzzy set itself. It is the first-time application of fuzzy reasoning over the backorder economic order quantity (EOQ) inventory management problem. We first define the fuzzy reasoning membership function through the use of L-fuzzy number and possibility theory on fuzzy numbers. Considering the holding cost, set up cost, backordering cost and demand rate as reasoning based fuzzy number, we have constructed a dual fuzzy mathematical problem. Then this problem has been solved over the dual feasible space which is associated to the aspiration level and the fuzzy approximation constant. Numerical study reveals the superiority of the proposed method with respect to the crisp solution as well as general fuzzy solution. Sensitivity analysis and graphical illustrations have also been done to justify the novelty of this article.
{"title":"Decision making in fuzzy reasoning to solve a backorder economic order quantity model","authors":"S. De, G. C. Mahata","doi":"10.1051/ro/2023051","DOIUrl":"https://doi.org/10.1051/ro/2023051","url":null,"abstract":"Fuzzy reasoning is the subject of fuzzy system where the fuzzy set is characterized by the randomization of the variable associated in the fuzzy set itself. It is the first-time application of fuzzy reasoning over the backorder economic order quantity (EOQ) inventory management problem. We first define the fuzzy reasoning membership function through the use of L-fuzzy number and possibility theory on fuzzy numbers. Considering the holding cost, set up cost, backordering cost and demand rate as reasoning based fuzzy number, we have constructed a dual fuzzy mathematical problem. Then this problem has been solved over the dual feasible space which is associated to the aspiration level and the fuzzy approximation constant. Numerical study reveals the superiority of the proposed method with respect to the crisp solution as well as general fuzzy solution. Sensitivity analysis and graphical illustrations have also been done to justify the novelty of this article.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73602214","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}
We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the sum total of the rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0-1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods.
{"title":"Effective league championship algorithm and lower bound procedure for scheduling a single batch-processing machine with non-identical job sizes and job rejection","authors":"Saeedeh Afkhami, A. H. Kashan, B. Ostadi","doi":"10.1051/ro/2023050","DOIUrl":"https://doi.org/10.1051/ro/2023050","url":null,"abstract":"We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the sum total of the rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0-1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74171335","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}
The study of a variant of Roman domination was initiated by Chellali et al. (2016). Given a graph $G$ with vertex set $V$, a Roman ${2}$-dominating function $f : V rightarrow {0, 1, 2}$ has the property that for every vertex $vin V$ with $f(v) =0$, either there exists a vertex $u$ adjacent to $v$ with $f(u) = 2$, or at least two vertices $x,; y$ adjacent to $v$ with $f(x)=f(y)=1$. The weight of a Roman ${2}$-dominating function is the value $f(V) = sum_{vin V} f(v)$. The minimum weight of a Roman ${2}$-dominating function is called the Roman ${2}$-domination number and is denoted by $gamma_{{R2}}(G)$. In this work we find several NP-complete instances of the Roman ${2}$-domination problem: chordal graphs, bipartite planar graphs, chordal bipartite graphs, bipartite with maximum degree 3 graphs, among others. A result by Chellali et al. (2016) shows that $gamma_{{R2}}(G)$ and the 2-rainbow domination number of $G$ coincide when $G$ is a tree, and thus, the linear time algorithm for $k$-rainbow domination due to Bresar et al. (2008) can be followed to compute $gamma_{{R2}}(G)$. In this work we develop an efficient algorithm that is independent of $k$-rainbow domination and computes the Roman ${2}$-domination number on a subclass of trees called caterpillars.
{"title":"New complexity results on Roman {2}-domination","authors":"Lara Fernández, V. Leoni","doi":"10.1051/ro/2023049","DOIUrl":"https://doi.org/10.1051/ro/2023049","url":null,"abstract":"The study of a variant of Roman domination was initiated by Chellali et al. (2016). Given a graph $G$ with vertex set $V$, a Roman ${2}$-dominating function $f : V rightarrow {0, 1, 2}$ has the property that for every vertex $vin V$ with $f(v) =0$, either there exists a vertex $u$ adjacent to $v$ with $f(u) = 2$, or at least two vertices $x,; y$ adjacent to $v$ with $f(x)=f(y)=1$. The weight of a Roman ${2}$-dominating function is the value $f(V) = sum_{vin V} f(v)$. The minimum weight of a Roman ${2}$-dominating function is called the Roman ${2}$-domination number and is denoted by $gamma_{{R2}}(G)$. In this work we find several NP-complete instances of the Roman ${2}$-domination problem: chordal graphs, bipartite planar graphs, chordal bipartite graphs, bipartite with maximum degree 3 graphs, among others. A result by Chellali et al. (2016) shows that $gamma_{{R2}}(G)$ and the 2-rainbow domination number of $G$ coincide when $G$ is a tree, and thus, the linear time algorithm for $k$-rainbow domination due to Bresar et al. (2008) can be followed to compute $gamma_{{R2}}(G)$. In this work we develop an efficient algorithm that is independent of $k$-rainbow domination and computes the Roman ${2}$-domination number on a subclass of trees called caterpillars.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76696789","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}
Mahnaz Ebrahimi-Sadrabadi, B. Ostadi, Mohammad Mehdi Sepehri, A. H. Kashan
Organizations need to apply resilience and business continuity in industry to protect themselves against the crises and destructive events. Also, the growing expansion of competition in the global market and the increasing crisis in the world have increased the importance of optimal resource allocation. With the optimal resource allocation, huge losses and damages to organizations are prevented. The problem of resource allocation can be raised alongside the criteria of process resilience and continuity. Therefore, organizations change their perspective from focusing solely on reducing vulnerability to increasing resilience and continuity against to accidents in crises and destructive situations. The objective of this paper is proposed a mathematical model for optimal resource allocation with the aim of minimizing the lack of process resilience and maximizing the process continuity. So, the organization can continue to operate with available resources in times of crisis and lack of resources. Also, main activities and processes are not interrupted by crises and destructive events. After solving the model using a case study (textile industry), the results of the model were described and it was found that destructive events were recovered before the tolerance threshold and crisis and destructive events did not interrupt activities and processes.
{"title":"Optimal resource allocation model in disaster situations for maximizing the value of operational process resiliency and continuity","authors":"Mahnaz Ebrahimi-Sadrabadi, B. Ostadi, Mohammad Mehdi Sepehri, A. H. Kashan","doi":"10.1051/ro/2023048","DOIUrl":"https://doi.org/10.1051/ro/2023048","url":null,"abstract":"Organizations need to apply resilience and business continuity in industry to protect themselves against the crises and destructive events. Also, the growing expansion of competition in the global market and the increasing crisis in the world have increased the importance of optimal resource allocation. With the optimal resource allocation, huge losses and damages to organizations are prevented. The problem of resource allocation can be raised alongside the criteria of process resilience and continuity. Therefore, organizations change their perspective from focusing solely on reducing vulnerability to increasing resilience and continuity against to accidents in crises and destructive situations. The objective of this paper is proposed a mathematical model for optimal resource allocation with the aim of minimizing the lack of process resilience and maximizing the process continuity. So, the organization can continue to operate with available resources in times of crisis and lack of resources. Also, main activities and processes are not interrupted by crises and destructive events. After solving the model using a case study (textile industry), the results of the model were described and it was found that destructive events were recovered before the tolerance threshold and crisis and destructive events did not interrupt activities and processes.","PeriodicalId":20872,"journal":{"name":"RAIRO Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80461162","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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