Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7798058
H. Khadilkar, Sudhir K. Sinha
This paper presents a simulation-based optimisation approach for planning railway hump yard operations. A hump yard is used for processing carriages (cars) brought by incoming trains, through a set of classification tracks, into newly formed outbound trains. There are specific constraints on the order in which each operation can be carried out, as well the standing order of cars in each outbound train. The set of decisions to be computed includes (i) the hump (processing) schedule of inbound trains, (ii) the assignment of cars to classification tracks, and (iii) the assembly schedule of outbound trains. The objective is to minimise the average dwell time of cars (the time spent from arrival at receiving tracks to departure). A simple set of rules is used to develop a discrete event simulator. The resulting objective function values vary between 5% and 20% of previously published optimisation formulations, depending on problem constraints. The execution time is between 3 and 5 minutes for a 42-day planning problem.
{"title":"Rule-based discrete event simulation for optimising railway hump yard operations","authors":"H. Khadilkar, Sudhir K. Sinha","doi":"10.1109/IEEM.2016.7798058","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7798058","url":null,"abstract":"This paper presents a simulation-based optimisation approach for planning railway hump yard operations. A hump yard is used for processing carriages (cars) brought by incoming trains, through a set of classification tracks, into newly formed outbound trains. There are specific constraints on the order in which each operation can be carried out, as well the standing order of cars in each outbound train. The set of decisions to be computed includes (i) the hump (processing) schedule of inbound trains, (ii) the assignment of cars to classification tracks, and (iii) the assembly schedule of outbound trains. The objective is to minimise the average dwell time of cars (the time spent from arrival at receiving tracks to departure). A simple set of rules is used to develop a discrete event simulator. The resulting objective function values vary between 5% and 20% of previously published optimisation formulations, depending on problem constraints. The execution time is between 3 and 5 minutes for a 42-day planning problem.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114608728","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7798065
R. J. Dubber, J. Pretorius
It is a setback that many projects face and senior management fear, that a well-run project heading for success can take a turn for the worst when the project manager is replaced as a result of resignation or transfer. The replacement of the leader and foundation of the project can result in poor management of the triple constraint (scope, time and budget), loss of historical project information, as well as cause a ripple effect of conflict, confusion, misunderstanding and poor team spirit within the project team. The term “replacing the project manager” or “RPM” should be easily recognized by organizations, yet, there is very little documentation available discussing this common issue. The frequency of replacement, the circumstances in which the project manager is replaced, and the effect it has on a project during execution is investigated.
{"title":"Investigating the effects of replacing the project manager during project execution","authors":"R. J. Dubber, J. Pretorius","doi":"10.1109/IEEM.2016.7798065","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7798065","url":null,"abstract":"It is a setback that many projects face and senior management fear, that a well-run project heading for success can take a turn for the worst when the project manager is replaced as a result of resignation or transfer. The replacement of the leader and foundation of the project can result in poor management of the triple constraint (scope, time and budget), loss of historical project information, as well as cause a ripple effect of conflict, confusion, misunderstanding and poor team spirit within the project team. The term “replacing the project manager” or “RPM” should be easily recognized by organizations, yet, there is very little documentation available discussing this common issue. The frequency of replacement, the circumstances in which the project manager is replaced, and the effect it has on a project during execution is investigated.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114731726","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797934
W. Wang, Q. Hu, D. Yu
The increase of high-cost and high-precision manufacturing process underlines the importance of the reliability estimation of Bogey test data. To estimate the failure probability of Bogey test, Bayesian approaches often focus on the choice of the prior distribution. However, this paper develops a new method, which making use of the concavity of lifetime's distribution function to construct a non-informative prior for the failure probability. By integrating all the test information, not only the number of effective samples but also previous test information, we explore a new form of the likelihood function for failure probability. Through updating the boundaries of the prior in each step by previous steps' estimations, we obtain the failure probability progressively. In the case study, we construct sensitivity analysis to show that our method is more robust to different lifetime distribution assumptions than other existed methods.
{"title":"Bayesian estimation for failure probability through Bogey test data","authors":"W. Wang, Q. Hu, D. Yu","doi":"10.1109/IEEM.2016.7797934","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797934","url":null,"abstract":"The increase of high-cost and high-precision manufacturing process underlines the importance of the reliability estimation of Bogey test data. To estimate the failure probability of Bogey test, Bayesian approaches often focus on the choice of the prior distribution. However, this paper develops a new method, which making use of the concavity of lifetime's distribution function to construct a non-informative prior for the failure probability. By integrating all the test information, not only the number of effective samples but also previous test information, we explore a new form of the likelihood function for failure probability. Through updating the boundaries of the prior in each step by previous steps' estimations, we obtain the failure probability progressively. In the case study, we construct sensitivity analysis to show that our method is more robust to different lifetime distribution assumptions than other existed methods.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115112313","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797921
S. Colombo
Making decisions in complex systems it is a complicated task to accomplish. As complexity and uncertainty increase, the use of scenarios to exploring that uncertainty becomes essential to support decision makers. The difficulty associated with the combinatorial need imposed by complex systems requires methods and tools to unburden analysts from the cumbersome task of manually deriving scenarios and the awkward one of properly managing them. The paper presents how the Artificial Logic Bayesian Algorithm (ALBA) method, thanks to the use of artificial logic (or, more formally, the Logic-based Artificial Intelligence), allows for analysts both to build complete partitions (i.e., complete sets of mutually exclusive choices) by “only” defining the logical and stochastic correlations amongst the selected elective random variables (leaving to the algorithm the burden to create the complete partition), and to nimbly managing scenarios.
{"title":"Risk-based decision making in complex systems: The ALBA method","authors":"S. Colombo","doi":"10.1109/IEEM.2016.7797921","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797921","url":null,"abstract":"Making decisions in complex systems it is a complicated task to accomplish. As complexity and uncertainty increase, the use of scenarios to exploring that uncertainty becomes essential to support decision makers. The difficulty associated with the combinatorial need imposed by complex systems requires methods and tools to unburden analysts from the cumbersome task of manually deriving scenarios and the awkward one of properly managing them. The paper presents how the Artificial Logic Bayesian Algorithm (ALBA) method, thanks to the use of artificial logic (or, more formally, the Logic-based Artificial Intelligence), allows for analysts both to build complete partitions (i.e., complete sets of mutually exclusive choices) by “only” defining the logical and stochastic correlations amongst the selected elective random variables (leaving to the algorithm the burden to create the complete partition), and to nimbly managing scenarios.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115119165","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797880
D. Handayani, M. K. Herliansyah, B. Hartono, B. M. Sopha
This paper explains attributes that can affect people's behavior in evacuation decision-making from Mount Merapi eruption, starting from pre-evacuation phase, vehicle selection phase, to evacuation route selection phase to reach a safe point. These factors were obtained through previous research, relevant to the characteristics of communities around Mount Merapi in facing an emergency evacuation from Mount Merapi eruption. Located in the Special Region of Yogyakarta, Indonesia, Mount Merapi is one of the most active volcanoes in the world which has a unique type of eruption and culture of surrounding communities. In the last stage, this paper also provides guidelines for analysis methods which can be used to solve emergency evacuation problems in order to minimize victims.
{"title":"Community behavior during the evacuation of Mount Merapi eruption disaster","authors":"D. Handayani, M. K. Herliansyah, B. Hartono, B. M. Sopha","doi":"10.1109/IEEM.2016.7797880","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797880","url":null,"abstract":"This paper explains attributes that can affect people's behavior in evacuation decision-making from Mount Merapi eruption, starting from pre-evacuation phase, vehicle selection phase, to evacuation route selection phase to reach a safe point. These factors were obtained through previous research, relevant to the characteristics of communities around Mount Merapi in facing an emergency evacuation from Mount Merapi eruption. Located in the Special Region of Yogyakarta, Indonesia, Mount Merapi is one of the most active volcanoes in the world which has a unique type of eruption and culture of surrounding communities. In the last stage, this paper also provides guidelines for analysis methods which can be used to solve emergency evacuation problems in order to minimize victims.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116187916","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797889
L. Chia, W. D. Lin
This paper aims to ascertain the optimal number of consultation rooms to operate so that patients with high severity medical conditions are attended to promptly, yet with capacity to spare for low severity patients but without fully providing for this latter group to be seen within a very short duration of time upon arrival. The research methodology is based on the concepts from simulation-based lean and six-sigma approach. The dynamic interactions between the fluctuation of patient arrivals and Doctor schedules are experimented through a discrete event simulation model. This paper describes the different stages of the research such as identifying the problem, analyzing the historical data, constructing the simulation model, as well as identifying the optimal Doctor schedules through simulation experiments. This paper illustrates the system dynamic behavior of the Emergency Department under study, and demonstrates the combination use of data analytics and simulation modeling in dealing with such complexities.
{"title":"Simulation study of patient arrivals and doctors scheduling in a children's emergency department","authors":"L. Chia, W. D. Lin","doi":"10.1109/IEEM.2016.7797889","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797889","url":null,"abstract":"This paper aims to ascertain the optimal number of consultation rooms to operate so that patients with high severity medical conditions are attended to promptly, yet with capacity to spare for low severity patients but without fully providing for this latter group to be seen within a very short duration of time upon arrival. The research methodology is based on the concepts from simulation-based lean and six-sigma approach. The dynamic interactions between the fluctuation of patient arrivals and Doctor schedules are experimented through a discrete event simulation model. This paper describes the different stages of the research such as identifying the problem, analyzing the historical data, constructing the simulation model, as well as identifying the optimal Doctor schedules through simulation experiments. This paper illustrates the system dynamic behavior of the Emergency Department under study, and demonstrates the combination use of data analytics and simulation modeling in dealing with such complexities.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116311670","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797948
D. Lin, C. K. Lee, Kangwei Lin
This paper investigates the effect factors in the adoption of Internet of Things (IoT) technology in the agricultural supply chain in China by constructing a Technology-Organization-Environment (TOE) framework. The data was analyzed using Structural Equation Modelling. Through statistics analysis, the effect factors were recognized and the TOE model was modified appropriately. The results indicated that resistance from employees and uncertainties are not important factors that influence the IoT adoption. Referring to those supported factors, technical factors (complexity, compatibility, perceived benefit, and cost) have a complicated influence on the technology adoption of IoT in agriculture. In addition, organizational factors (scale of enterprise, executive support, trust among the businesses in the supply chain, and technical knowledge) and environmental factors (external pressure and government support) all have positive relationships with IoT adoption.
{"title":"Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain","authors":"D. Lin, C. K. Lee, Kangwei Lin","doi":"10.1109/IEEM.2016.7797948","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797948","url":null,"abstract":"This paper investigates the effect factors in the adoption of Internet of Things (IoT) technology in the agricultural supply chain in China by constructing a Technology-Organization-Environment (TOE) framework. The data was analyzed using Structural Equation Modelling. Through statistics analysis, the effect factors were recognized and the TOE model was modified appropriately. The results indicated that resistance from employees and uncertainties are not important factors that influence the IoT adoption. Referring to those supported factors, technical factors (complexity, compatibility, perceived benefit, and cost) have a complicated influence on the technology adoption of IoT in agriculture. In addition, organizational factors (scale of enterprise, executive support, trust among the businesses in the supply chain, and technical knowledge) and environmental factors (external pressure and government support) all have positive relationships with IoT adoption.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335823","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7797834
T. Miyamoto, N. Ueno, D. Li, S. Yoon
In this research, we study a scheduling problem in mail-order pharmacy automated distribution (MOPAD) system. In MOPAD scheduling, two kinds of objective: the collation delay (CD) and makespan, should be considered and in the previous study of some of authors three kinds of genetic algorithms (GA) are applied and evaluated. In this paper, we apply constraint programming (CP) for the scheduling problem. We proposed a CP formulation of the problem and evaluated through computational experiments. The results show that the proposed method is effective for small-scale problem but further study is required to compare with GA methods in large-scale problems.
{"title":"Multi-objective constraint optimization in mail-order pharmacy automated distribution system","authors":"T. Miyamoto, N. Ueno, D. Li, S. Yoon","doi":"10.1109/IEEM.2016.7797834","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7797834","url":null,"abstract":"In this research, we study a scheduling problem in mail-order pharmacy automated distribution (MOPAD) system. In MOPAD scheduling, two kinds of objective: the collation delay (CD) and makespan, should be considered and in the previous study of some of authors three kinds of genetic algorithms (GA) are applied and evaluated. In this paper, we apply constraint programming (CP) for the scheduling problem. We proposed a CP formulation of the problem and evaluated through computational experiments. The results show that the proposed method is effective for small-scale problem but further study is required to compare with GA methods in large-scale problems.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123510509","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7798012
Tongzun Wang, Jianbiao Peng, Yi-Feng Hung
There are four major production processes in apparel manufacturing: cutting, sewing, ironing, and packing. Usually, the sewing process is the bottleneck for most apparel factories. Sufficient amount of work-in-process from cutting department must be provided in time to prevent the sewing operation from idleness. Scheduling a cutting operation problem is similar to a two-dimensional bin packing problem. The operations on cutting tables can be represented by a two-dimensional Gantt chart. The horizontal axis and vertical axis of the Gantt chart represent the time line and the location on the length of the cutting table, respectively. In addition, the cutting operation can be represented by a rectangle, which is placed on the two-dimensional Gantt chart. A mixed integer programming model is proposed in this study to solve such a problem with the objective of minimizing makespan.
{"title":"Modeling fabric cutting scheduling as mixed integer programming","authors":"Tongzun Wang, Jianbiao Peng, Yi-Feng Hung","doi":"10.1109/IEEM.2016.7798012","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7798012","url":null,"abstract":"There are four major production processes in apparel manufacturing: cutting, sewing, ironing, and packing. Usually, the sewing process is the bottleneck for most apparel factories. Sufficient amount of work-in-process from cutting department must be provided in time to prevent the sewing operation from idleness. Scheduling a cutting operation problem is similar to a two-dimensional bin packing problem. The operations on cutting tables can be represented by a two-dimensional Gantt chart. The horizontal axis and vertical axis of the Gantt chart represent the time line and the location on the length of the cutting table, respectively. In addition, the cutting operation can be represented by a rectangle, which is placed on the two-dimensional Gantt chart. A mixed integer programming model is proposed in this study to solve such a problem with the objective of minimizing makespan.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957540","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}
Pub Date : 2016-12-01DOI: 10.1109/IEEM.2016.7798114
A. Abdelhadi, T. Khreis
In this paper a methodology for the application of group technology to preventive maintenance strategy based on weighted similarity coefficients is introduced. In this methodology machines are grouped into clusters of virtual cells based on the predicted severity of failure they can encounter. These cells are used to come up with an efficient maintenance strategy such as to prioritize the execution of the preventive maintenance to certain types of machines. Numerical example is presented to illustrate the procedure.
{"title":"Preventive maintenance operations based on weighted similarity coefficient","authors":"A. Abdelhadi, T. Khreis","doi":"10.1109/IEEM.2016.7798114","DOIUrl":"https://doi.org/10.1109/IEEM.2016.7798114","url":null,"abstract":"In this paper a methodology for the application of group technology to preventive maintenance strategy based on weighted similarity coefficients is introduced. In this methodology machines are grouped into clusters of virtual cells based on the predicted severity of failure they can encounter. These cells are used to come up with an efficient maintenance strategy such as to prioritize the execution of the preventive maintenance to certain types of machines. Numerical example is presented to illustrate the procedure.","PeriodicalId":114906,"journal":{"name":"2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124514976","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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