Pub Date : 2020-07-16DOI: 10.1504/ejie.2020.108581
Javier Panadero, A. Juan, C. Bayliss, C. Currie
We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019]
{"title":"Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem","authors":"Javier Panadero, A. Juan, C. Bayliss, C. Currie","doi":"10.1504/ejie.2020.108581","DOIUrl":"https://doi.org/10.1504/ejie.2020.108581","url":null,"abstract":"We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ejie.2020.108581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46009889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.1504/ejie.2020.105698
M. Janardhanan, Peter Nielsen
Robotic assembly lines (RALs) are utilised due to the flexibility it provides to the overall production system. Industries mainly focus on reducing the operation costs involved. From the literature survey it can be seen that only few research has been reported in the area of cost related optimisation in RALs. This paper focuses on proposing a new model in RALs with the main objective of maximising line efficiency by minimising total assembly line cost. The proposed model can be used production managers to balance a RAL in an efficient manner. Since simple assembly line balancing problem is classified as NP-hard, proposed problem due to additional constraints also falls under the same category. Particle swarm optimisation (PSO) and differential evolution (DE) are applied as the optimisation tool to solve this problem. The performances of this proposed algorithm are tested on a set of reported benchmark problems. From the comparative study, it is found that the proposed DE algorithm obtain better solutions for the majority of the problems tested. [Received: 2 July 2018; Revised: 16 December 2018; Revised: 8 May 2019; Accepted: 2 August 2019]
{"title":"Optimisation of cost efficient robotic assembly line using metaheuristic algorithms","authors":"M. Janardhanan, Peter Nielsen","doi":"10.1504/ejie.2020.105698","DOIUrl":"https://doi.org/10.1504/ejie.2020.105698","url":null,"abstract":"Robotic assembly lines (RALs) are utilised due to the flexibility it provides to the overall production system. Industries mainly focus on reducing the operation costs involved. From the literature survey it can be seen that only few research has been reported in the area of cost related optimisation in RALs. This paper focuses on proposing a new model in RALs with the main objective of maximising line efficiency by minimising total assembly line cost. The proposed model can be used production managers to balance a RAL in an efficient manner. Since simple assembly line balancing problem is classified as NP-hard, proposed problem due to additional constraints also falls under the same category. Particle swarm optimisation (PSO) and differential evolution (DE) are applied as the optimisation tool to solve this problem. The performances of this proposed algorithm are tested on a set of reported benchmark problems. From the comparative study, it is found that the proposed DE algorithm obtain better solutions for the majority of the problems tested. [Received: 2 July 2018; Revised: 16 December 2018; Revised: 8 May 2019; Accepted: 2 August 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ejie.2020.105698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44327183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.1504/ejie.2020.10027173
K. Deep
In this study, an integrated mathematical model for the cell formation problem is proposed considering the dynamic production environment. The proposed model yields, manufacturing cells, part families and worker's assignment simultaneously by allowing a cubic search space of 'machine-part-worker' in the CMS. The resources are aggregated into manufacturing cells based on the optimal process route among the user specified multiple routes. The model interprets flexibility in the processing of subsets of a part operation sequence in the different production mode (internal production/subcontracting part operation). It is a tangible advantage during unavailability of worker and unexpected machine break down occurring in the real world. The proposed cell formation problem has been solved by using a simulated annealing-based genetic algorithm (SAGA). The algorithm imparts synergy effect to improve intensification, diversification in the cubic search space and increases the possibility of achieving near-optimum solutions. To evaluate the computational performance of the proposed approach the algorithm is tested on a number of randomly generated instances. The results substantiate the efficiency of the proposed approach by minimising overall cost. [Received: 17 August 2018; Accepted: 28 July 2019]
{"title":"Machine Cell Formation for Dynamic Part Population Considering Part Operation Tradeoff and Worker Assignment Using Simulated Annealing based Genetic Algorithm","authors":"K. Deep","doi":"10.1504/ejie.2020.10027173","DOIUrl":"https://doi.org/10.1504/ejie.2020.10027173","url":null,"abstract":"In this study, an integrated mathematical model for the cell formation problem is proposed considering the dynamic production environment. The proposed model yields, manufacturing cells, part families and worker's assignment simultaneously by allowing a cubic search space of 'machine-part-worker' in the CMS. The resources are aggregated into manufacturing cells based on the optimal process route among the user specified multiple routes. The model interprets flexibility in the processing of subsets of a part operation sequence in the different production mode (internal production/subcontracting part operation). It is a tangible advantage during unavailability of worker and unexpected machine break down occurring in the real world. The proposed cell formation problem has been solved by using a simulated annealing-based genetic algorithm (SAGA). The algorithm imparts synergy effect to improve intensification, diversification in the cubic search space and increases the possibility of achieving near-optimum solutions. To evaluate the computational performance of the proposed approach the algorithm is tested on a number of randomly generated instances. The results substantiate the efficiency of the proposed approach by minimising overall cost. [Received: 17 August 2018; Accepted: 28 July 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46022488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-10DOI: 10.1504/ejie.2020.105084
A. Bayram, Xi Chen
The outcomes of robotic surgery involve nonlinear interactions of many factors, including patient-related and surgical team-related elements. In robotic surgery, not only the surgeon but also all team members play an important role in determining surgery outcomes. Therefore, it is important to study optimal surgical team configuration decisions. In this study, we investigate regression models for accurate predictions of surgical outcomes by analysing robotic surgery data. We further develop an optimisation model to investigate the optimal team configuration decisions by considering two separate objectives: 1) to minimise the maximum operating room occupation time; 2) to minimise the average operating room occupation time. In our numerical analyses, we compare the optimal team configuration decisions with the current configuration decisions and show that the optimal team allocation decision can result in a 17% decrease in operating room occupation time. Our results suggest that efforts for reducing operating room occupation time should focus on increasing the experience of surgery team members, e.g., via running training programs. [Submitted: 10 September 2018; Accepted: 31 May 2019]
{"title":"Optimising teams and the outcomes of surgery","authors":"A. Bayram, Xi Chen","doi":"10.1504/ejie.2020.105084","DOIUrl":"https://doi.org/10.1504/ejie.2020.105084","url":null,"abstract":"The outcomes of robotic surgery involve nonlinear interactions of many factors, including patient-related and surgical team-related elements. In robotic surgery, not only the surgeon but also all team members play an important role in determining surgery outcomes. Therefore, it is important to study optimal surgical team configuration decisions. In this study, we investigate regression models for accurate predictions of surgical outcomes by analysing robotic surgery data. We further develop an optimisation model to investigate the optimal team configuration decisions by considering two separate objectives: 1) to minimise the maximum operating room occupation time; 2) to minimise the average operating room occupation time. In our numerical analyses, we compare the optimal team configuration decisions with the current configuration decisions and show that the optimal team allocation decision can result in a 17% decrease in operating room occupation time. Our results suggest that efforts for reducing operating room occupation time should focus on increasing the experience of surgery team members, e.g., via running training programs. [Submitted: 10 September 2018; Accepted: 31 May 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ejie.2020.105084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43930257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ejie.2020.105081
J. C. García-Díaz, Alexander D. Pulido-Rojano
This paper highlights the benefits of multihead weighing, a packaging process based on the sum of weights of several individual hoppers wherein total weight of the packed product must be close to a specified target weight while complying with applicable regulations. The paper details into performance analysis and optimisation of new strategies for setting-up the process to achieve an optimal configuration of the machine. Three strategies, designed to optimise the packaging process, are analysed and compared in terms of supplying products to the hoppers. A factorial design of the experimental model is exploited to predict the measures of performance as a function of a variety of control settings. Results of the numerical experiments are used to analyse the sources of variability and to identify the optimum operating conditions for the multihead weigher. Therefore, the findings of this paper will benefit both manufacturer and users of the multihead weigher machine. [Received: 15 September 2017; Revised: 15 November 2018; Revised: 20 March 2019; Accepted: 14 April 2019]
{"title":"Performance analysis and optimisation of new strategies for the setup of a multihead weighing process","authors":"J. C. García-Díaz, Alexander D. Pulido-Rojano","doi":"10.1504/ejie.2020.105081","DOIUrl":"https://doi.org/10.1504/ejie.2020.105081","url":null,"abstract":"This paper highlights the benefits of multihead weighing, a packaging process based on the sum of weights of several individual hoppers wherein total weight of the packed product must be close to a specified target weight while complying with applicable regulations. The paper details into performance analysis and optimisation of new strategies for setting-up the process to achieve an optimal configuration of the machine. Three strategies, designed to optimise the packaging process, are analysed and compared in terms of supplying products to the hoppers. A factorial design of the experimental model is exploited to predict the measures of performance as a function of a variety of control settings. Results of the numerical experiments are used to analyse the sources of variability and to identify the optimum operating conditions for the multihead weigher. Therefore, the findings of this paper will benefit both manufacturer and users of the multihead weigher machine. [Received: 15 September 2017; Revised: 15 November 2018; Revised: 20 March 2019; Accepted: 14 April 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ejie.2020.105081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45823592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-07DOI: 10.1504/ejie.2020.105080
Zohra Bouzidi, L. Terrissa, N. Zerhouni, Soheyb Ayad
Recently, prognostics and health management (PHM) solutions are increasingly implemented in order to complete maintenance activities. The prognostic process in industrial maintenance is the main step to predict failures before they occur by determining the remaining useful life (RUL) of the equipment. However, it also poses challenges such as reliability, availability, infrastructure and physics servers. To address these challenges, this paper investigates a cloud-based prognostic system of an aircraft engine based on artificial intelligence methods. We design and implement an architecture that defines an approach that is prognostic as a service (Prognostic aaS) using a data-driven approach. This approach will provide a suitable and efficient PHM solution as a service via internet, on the demand of a client, in accordance with a service level agreement (SLA) contract drawn up in advance to ensure a better quality of service and pay this service per use (pay as you go). We estimated the RUL of aircraft engines fleet by implementing three techniques. Next, we studied the performance of this system; the efficient method was concluded. In addition, we discussed the quality of service (QoS) for the cloud prognostic application according to the factors of quality. [Received: 19 May 2018; Revised: 10 August 2018; Revised: 31 August 2018; Revised: 21 March 2019; Accepted: 28 March 2019]
{"title":"QoS of cloud prognostic system: application to aircraft engines fleet","authors":"Zohra Bouzidi, L. Terrissa, N. Zerhouni, Soheyb Ayad","doi":"10.1504/ejie.2020.105080","DOIUrl":"https://doi.org/10.1504/ejie.2020.105080","url":null,"abstract":"Recently, prognostics and health management (PHM) solutions are increasingly implemented in order to complete maintenance activities. The prognostic process in industrial maintenance is the main step to predict failures before they occur by determining the remaining useful life (RUL) of the equipment. However, it also poses challenges such as reliability, availability, infrastructure and physics servers. To address these challenges, this paper investigates a cloud-based prognostic system of an aircraft engine based on artificial intelligence methods. We design and implement an architecture that defines an approach that is prognostic as a service (Prognostic aaS) using a data-driven approach. This approach will provide a suitable and efficient PHM solution as a service via internet, on the demand of a client, in accordance with a service level agreement (SLA) contract drawn up in advance to ensure a better quality of service and pay this service per use (pay as you go). We estimated the RUL of aircraft engines fleet by implementing three techniques. Next, we studied the performance of this system; the efficient method was concluded. In addition, we discussed the quality of service (QoS) for the cloud prognostic application according to the factors of quality. [Received: 19 May 2018; Revised: 10 August 2018; Revised: 31 August 2018; Revised: 21 March 2019; Accepted: 28 March 2019]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ejie.2020.105080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46672103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/EJIE.2020.112481
H. Soroush, S. Al-Yakoob, F. Alqallaf
We examine a maritime transportation-inventory problem under three daily demand distributions, namely gamma, exponential and uniform. This is essentially an extension of the problem of Soroush and Al-Yakoob (2018) in which case daily demands are assumed to be normally distributed. The principle thrust of this research effort is to find an optimal vessel schedule with the objective of minimising the expected overall cost consisting of the vessels' operational expenses, expected penalties for violating some pre-specified lower and upper storage levels, and vessels' chartering expenses, while meeting the stochastic demand requirements at each destination with acceptable reliability levels. We formulate each problem scenario as a stochastic optimisation model, which using chance-constrained programming, is converted into an exact mixed-integer nonlinear program. Our results show that different demand distributions lead to significantly different vessel schedules and associated costs. Sensitivity analyses are also performed. [Received 18 November 2018; Revised 17 May 2019; Revised 7 August 2019; Revised 3 November 2019; Accepted 21 January 2020]
{"title":"A stochastic maritime transportation-inventory problem with gamma, exponential, and uniform demand distributions","authors":"H. Soroush, S. Al-Yakoob, F. Alqallaf","doi":"10.1504/EJIE.2020.112481","DOIUrl":"https://doi.org/10.1504/EJIE.2020.112481","url":null,"abstract":"We examine a maritime transportation-inventory problem under three daily demand distributions, namely gamma, exponential and uniform. This is essentially an extension of the problem of Soroush and Al-Yakoob (2018) in which case daily demands are assumed to be normally distributed. The principle thrust of this research effort is to find an optimal vessel schedule with the objective of minimising the expected overall cost consisting of the vessels' operational expenses, expected penalties for violating some pre-specified lower and upper storage levels, and vessels' chartering expenses, while meeting the stochastic demand requirements at each destination with acceptable reliability levels. We formulate each problem scenario as a stochastic optimisation model, which using chance-constrained programming, is converted into an exact mixed-integer nonlinear program. Our results show that different demand distributions lead to significantly different vessel schedules and associated costs. Sensitivity analyses are also performed. [Received 18 November 2018; Revised 17 May 2019; Revised 7 August 2019; Revised 3 November 2019; Accepted 21 January 2020]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66756242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/ejie.2020.10026810
Xi Chen, A. Bayram
{"title":"Optimizing Surgical Teams and the Outcomes of Surgery","authors":"Xi Chen, A. Bayram","doi":"10.1504/ejie.2020.10026810","DOIUrl":"https://doi.org/10.1504/ejie.2020.10026810","url":null,"abstract":"","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66756062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/EJIE.2020.112478
Jae-Dong Hong, Ki-Young Jeong
This paper considers an emergency backup supply (EBS) system from the secondary supplying facility (SSF) when the primal supplying facility (PSF) cannot satisfy the demand in case of disruptions. In this context, EBS requires each demand point to be covered by a PSF and an SSF. Using a multi-objective programming model, we propose a procedure of finding the option that would generate the most efficient EBS system and investigate the effect of backup supply from the SSFs on the facility location-allocation (FLA) design problem. We demonstrate the applicability of our proposed efficiency-driven approach and compare two cases, without and with the EBS system. From the numerical results, we observe that the FLA schemes with the EBS system perform well regarding increasing ENDS and yielding higher productivity. The proposed efficiency-driven FLA model with the EBS system would help decision-makers design and select efficient FLA schemes. [Received 29 June 2019; Revised 9 December 2019; Accepted 1 February 2020]
{"title":"Design of facility location-allocation network with an emergency backup supply system","authors":"Jae-Dong Hong, Ki-Young Jeong","doi":"10.1504/EJIE.2020.112478","DOIUrl":"https://doi.org/10.1504/EJIE.2020.112478","url":null,"abstract":"This paper considers an emergency backup supply (EBS) system from the secondary supplying facility (SSF) when the primal supplying facility (PSF) cannot satisfy the demand in case of disruptions. In this context, EBS requires each demand point to be covered by a PSF and an SSF. Using a multi-objective programming model, we propose a procedure of finding the option that would generate the most efficient EBS system and investigate the effect of backup supply from the SSFs on the facility location-allocation (FLA) design problem. We demonstrate the applicability of our proposed efficiency-driven approach and compare two cases, without and with the EBS system. From the numerical results, we observe that the FLA schemes with the EBS system perform well regarding increasing ENDS and yielding higher productivity. The proposed efficiency-driven FLA model with the EBS system would help decision-makers design and select efficient FLA schemes. [Received 29 June 2019; Revised 9 December 2019; Accepted 1 February 2020]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66756111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/EJIE.2020.112480
Maria Laura Cunico, A. Vecchietti
This article proposes a possibilistic model of production planning problem of a manufacturing company using a fuzzy representation of uncertainties in demand. An extension of chance constrained to fuzzy environments, and triangular numbers are employed to represent the variability in customers' orders. The operators required to convert the fuzzy model into an equivalent robust crisp one (RCM) are presented in the article. Moreover, the confidence levels of chance constraints are set as variables so that they are determined by the model, reducing the subjectivity in the selection of their values. The production planning problem is solved as a case study, to show the performance of the model. The results obtained are compared to two different alternative models: a deterministic one (DM) and a fuzzy approach (FeM). [Received 20 May 2018; Revised 29 May 2019; Revised 6 December 2019; Accepted 6 January 2020]
{"title":"A possibilistic model for production planning with uncertain demand","authors":"Maria Laura Cunico, A. Vecchietti","doi":"10.1504/EJIE.2020.112480","DOIUrl":"https://doi.org/10.1504/EJIE.2020.112480","url":null,"abstract":"This article proposes a possibilistic model of production planning problem of a manufacturing company using a fuzzy representation of uncertainties in demand. An extension of chance constrained to fuzzy environments, and triangular numbers are employed to represent the variability in customers' orders. The operators required to convert the fuzzy model into an equivalent robust crisp one (RCM) are presented in the article. Moreover, the confidence levels of chance constraints are set as variables so that they are determined by the model, reducing the subjectivity in the selection of their values. The production planning problem is solved as a case study, to show the performance of the model. The results obtained are compared to two different alternative models: a deterministic one (DM) and a fuzzy approach (FeM). [Received 20 May 2018; Revised 29 May 2019; Revised 6 December 2019; Accepted 6 January 2020]","PeriodicalId":51047,"journal":{"name":"European Journal of Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66756170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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