รศ. ดร.นระเกณฑ์ พุ่มชูศรี
- 5th Floor of Engineering 4 Bldg., Room 504
- +66-2218-6822
- naragain.p@chula.ac.th
Overview
Dr. Naragain Phumchusri is an associate professor at the Department of Industrial Engineering, Chulalongkorn University, Thailand. She received her Ph.D. in Industrial Engineering from Georgia Institute of Technology, Atlanta, GA, USA in 2010. Her current research interests include stochastic models for revenue management, machine learning for demand forecasting, inventory optimization, warehouse & supply chain management, data analysis for tourism industry and promotion optimization in retails.
Education
Ph.D. in Industrial Engineering
Georgia Institute of Technology, United States, 2010
Master of Science in Industrial Engineering
Georgia Institute of Technology, United States, 2006
B.Eng. in Industrial Engineering
Chulalongkorn University, Thailand, 2004
Expertise
Statistics & Data Analysis
Publications
2019
Chariya Paveenchana, Naragain Phumchusri
Optimal storage locations for warehouse efficiency improvement in a haircare manufacturer Journal Article
In: Engineering Journal, vol. 23, no. 5, pp. 141 – 168, 2019, (Cited by: 4; All Open Access, Gold Open Access, Green Open Access).
@article{Paveenchana2019141,
title = {Optimal storage locations for warehouse efficiency improvement in a haircare manufacturer},
author = {Chariya Paveenchana and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073503041&doi=10.4186%2fej.2019.23.5.141&partnerID=40&md5=19933f11c34586e323ac49cd572fe426},
doi = {10.4186/ej.2019.23.5.141},
year = {2019},
date = {2019-01-01},
journal = {Engineering Journal},
volume = {23},
number = {5},
pages = {141 – 168},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study aimed to enhance efficiency of a raw materials warehouse in a case study haircare manufacturer in Thailand by identifying the optimal storage locations for materials locating at this warehouse. At the same time, it was our goal to increase capacity and improve utilization at this warehouse in order to store some finished products here instead of storing all of them at an external public warehouse. As a result, the case study company can save storage cost of finished products at the external public warehouse. The key methodologies used to improve current raw materials warehouse were removing obsolete materials from warehouse, regrouping materials according to types, sizes, and turnover rate, reallocating space for each group of materials, considering beam height adjustment, and reassigning locations for each group of materials using optimization model. The results showed that capacity of the studied warehouse was enhanced by 12.65%, picking distance was reduced by 51.9% compared to current situation, utilization was more balanced throughout warehouse and annual cost saving of 874,800 THB was obtained from locating some finished products at the internal raw materials warehouse. In addition, robustness of the proposed model was analyzed and contingency plan was developed for handling with over flow materials when over utilization occurs. © 2019, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 4; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study aimed to enhance efficiency of a raw materials warehouse in a case study haircare manufacturer in Thailand by identifying the optimal storage locations for materials locating at this warehouse. At the same time, it was our goal to increase capacity and improve utilization at this warehouse in order to store some finished products here instead of storing all of them at an external public warehouse. As a result, the case study company can save storage cost of finished products at the external public warehouse. The key methodologies used to improve current raw materials warehouse were removing obsolete materials from warehouse, regrouping materials according to types, sizes, and turnover rate, reallocating space for each group of materials, considering beam height adjustment, and reassigning locations for each group of materials using optimization model. The results showed that capacity of the studied warehouse was enhanced by 12.65%, picking distance was reduced by 51.9% compared to current situation, utilization was more balanced throughout warehouse and annual cost saving of 874,800 THB was obtained from locating some finished products at the internal raw materials warehouse. In addition, robustness of the proposed model was analyzed and contingency plan was developed for handling with over flow materials when over utilization occurs. © 2019, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
2018
S. Jongcheveevat, N. Phumchusri, A. Vilasdaechanont
vol. 2019-December, IEEE Computer Society, 2018, (Cited by: 1).
@conference{Jongcheveevat20181451,
title = {Optimal Overbooking Decision for Perishable Resources with Jointly Stochastic Booking and Show-up Requests},
author = {S. Jongcheveevat and N. Phumchusri and A. Vilasdaechanont},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061768426&doi=10.1109%2fIEEM.2018.8607490&partnerID=40&md5=fe970afe131699b980872dc984321749},
doi = {10.1109/IEEM.2018.8607490},
year = {2018},
date = {2018-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
volume = {2019-December},
pages = {1451 – 1455},
publisher = {IEEE Computer Society},
abstract = {Overbooking is a methodology in revenue management to optimize important decision making for perishable resources or services with uncertain demand. Overbooking allows an incoming booking to be accepted in exceedance of an available capacity because it is believed that some booking will be cancelled later. It is a complicated and risky decision since the decision maker needs to minimize both outsourcing cost and opportunity-lost cost simultaneously. When there are two classes of resources, it is not necessary to always outsource the insufficient and Iow-priced resources. Upgrading customers to high-priced resources is possible. The objective of this research is to develop overbooking models for (1) one class of resources and (2) two classes of resources (ie, high and low price) to minimize total cost i. e., opportunity cost, cost of upgrading and outsource cost). The main contribution of this research is that, unlike other existing literatures, the opportunity cost considered is specifically identified in the situation where too much booking request rejection of each type of resources is present. Sensitivity analysis of our model is also shown for managerial insights. © 2018 IEEE.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Overbooking is a methodology in revenue management to optimize important decision making for perishable resources or services with uncertain demand. Overbooking allows an incoming booking to be accepted in exceedance of an available capacity because it is believed that some booking will be cancelled later. It is a complicated and risky decision since the decision maker needs to minimize both outsourcing cost and opportunity-lost cost simultaneously. When there are two classes of resources, it is not necessary to always outsource the insufficient and Iow-priced resources. Upgrading customers to high-priced resources is possible. The objective of this research is to develop overbooking models for (1) one class of resources and (2) two classes of resources (ie, high and low price) to minimize total cost i. e., opportunity cost, cost of upgrading and outsource cost). The main contribution of this research is that, unlike other existing literatures, the opportunity cost considered is specifically identified in the situation where too much booking request rejection of each type of resources is present. Sensitivity analysis of our model is also shown for managerial insights. © 2018 IEEE.
N. Phumchusri, S. Tangsiriwattana, P. Luangiarmekorn
Supplier Selection Method: A Case-study on a Car Seat Manufacturer in Thailand Conference
vol. 2019-December, IEEE Computer Society, 2018, (Cited by: 1).
@conference{Phumchusri201846,
title = {Supplier Selection Method: A Case-study on a Car Seat Manufacturer in Thailand},
author = {N. Phumchusri and S. Tangsiriwattana and P. Luangiarmekorn},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061814138&doi=10.1109%2fIEEM.2018.8607515&partnerID=40&md5=4324c5ed644fbcdfa3aa7c55dd800eaf},
doi = {10.1109/IEEM.2018.8607515},
year = {2018},
date = {2018-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
volume = {2019-December},
pages = {46 – 50},
publisher = {IEEE Computer Society},
abstract = {The objective of this research is to develop a model for a case-study car seat manufacturer for evaluating steel pipe and steel sheet suppliers, known as raw material suppliers, by applying Analytic Hierarchy Process (AHP), and a model for selecting the suitable raw material supplier for each part. These models aim to maximize overall part makers' satisfaction. The evaluators are chosen from purchasing management team from 10 part makers. These assessors will evaluate 8 raw material suppliers. This research is divided into 2 parts. The first part is the evaluation of raw material suppliers using Analytic Hierarchy Process. This part weights the importance of decision criteria complying with part makers' satisfaction. The second part proposes a decision model for supplier selection using integer programming. The weight of each criterion from the first part will be considered along with raw material consumption to select the suitable raw material suppliers that maximize overall part makers' satisfaction. The results from the first part of this study show that the most important criterion is cost which is about 41%. Quality, Delivery, Service, and Risk factors are 24%, 14%, 12% and 9%, respectively. The second part shows that the model can match material suppliers to part makers according to their preference. Comparing with current situation, the satisfaction is increased by 26% with this proposed model. It means that the proposed model can help increase satisfactions between car seat makers and their suppliers, which benefit the parts supplied to the case-study company. © 2018 IEEE.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to develop a model for a case-study car seat manufacturer for evaluating steel pipe and steel sheet suppliers, known as raw material suppliers, by applying Analytic Hierarchy Process (AHP), and a model for selecting the suitable raw material supplier for each part. These models aim to maximize overall part makers’ satisfaction. The evaluators are chosen from purchasing management team from 10 part makers. These assessors will evaluate 8 raw material suppliers. This research is divided into 2 parts. The first part is the evaluation of raw material suppliers using Analytic Hierarchy Process. This part weights the importance of decision criteria complying with part makers’ satisfaction. The second part proposes a decision model for supplier selection using integer programming. The weight of each criterion from the first part will be considered along with raw material consumption to select the suitable raw material suppliers that maximize overall part makers’ satisfaction. The results from the first part of this study show that the most important criterion is cost which is about 41%. Quality, Delivery, Service, and Risk factors are 24%, 14%, 12% and 9%, respectively. The second part shows that the model can match material suppliers to part makers according to their preference. Comparing with current situation, the satisfaction is increased by 26% with this proposed model. It means that the proposed model can help increase satisfactions between car seat makers and their suppliers, which benefit the parts supplied to the case-study company. © 2018 IEEE.
2017
Naragain Phumchusri, Phuntira Kitpipit
Warehouse layout design for an automotive raw material supplier Journal Article
In: Engineering Journal, vol. 21, no. 7, pp. 361 – 387, 2017, (Cited by: 8; All Open Access, Gold Open Access, Green Open Access).
@article{Phumchusri2017361,
title = {Warehouse layout design for an automotive raw material supplier},
author = {Naragain Phumchusri and Phuntira Kitpipit},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039915342&doi=10.4186%2fej.2017.21.7.361&partnerID=40&md5=8cdd42b269ccc01c5adf4c14c2910551},
doi = {10.4186/ej.2017.21.7.361},
year = {2017},
date = {2017-01-01},
journal = {Engineering Journal},
volume = {21},
number = {7},
pages = {361 – 387},
publisher = {Chulalongkorn University},
abstract = {The case-study company faces the limited space situation. Thus, the company decides to uninstall their temporary warehouses and re-locate products in two permanent warehouses. The objective of this research is to design the layouts of the two permanent warehouses so that the spaces can be efficiently used and the total picking distance is low. The past data, Invoices and Stock Data, are used for developing layouts designing processes. This research involves the collecting of Product Size Data to calculate the required space for the products. There are two phases in layout designing process. The first phase is the product categories grouping. This phase categorizes product categories into two groups for the two warehouses. The second phase is the layouts designing. In this phase, the layouts of the two warehouses and the locations of the products are designed. According to the company requirements and policies, the Adapted Class-Based Turnover Assignment is adopted in order to design the layouts for the two warehouses. Layouts of the warehouses are designed, analyzed, and evaluated. The best layouts give the best trade-off between quantitative results, i.e., the total picking distance and the remaining space, and qualitative results, i.e., the usability and the product suitability. The designed layouts are applied in the case-study company. This research develops a systematic and practical layout designing method which is flexible and can be adopted in other warehouses. © 2017, Chulalongkorn University. All rights reserved.},
note = {Cited by: 8; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The case-study company faces the limited space situation. Thus, the company decides to uninstall their temporary warehouses and re-locate products in two permanent warehouses. The objective of this research is to design the layouts of the two permanent warehouses so that the spaces can be efficiently used and the total picking distance is low. The past data, Invoices and Stock Data, are used for developing layouts designing processes. This research involves the collecting of Product Size Data to calculate the required space for the products. There are two phases in layout designing process. The first phase is the product categories grouping. This phase categorizes product categories into two groups for the two warehouses. The second phase is the layouts designing. In this phase, the layouts of the two warehouses and the locations of the products are designed. According to the company requirements and policies, the Adapted Class-Based Turnover Assignment is adopted in order to design the layouts for the two warehouses. Layouts of the warehouses are designed, analyzed, and evaluated. The best layouts give the best trade-off between quantitative results, i.e., the total picking distance and the remaining space, and qualitative results, i.e., the usability and the product suitability. The designed layouts are applied in the case-study company. This research develops a systematic and practical layout designing method which is flexible and can be adopted in other warehouses. © 2017, Chulalongkorn University. All rights reserved.
Paveena Chaovalitwongse, Krongsin Somprasonk, Naragain Phumchusri, Joseph Heim, Zelda B. Zabinsky, W. Art Chaovalitwongse
A decision support model for staff allocation of mobile medical service Journal Article
In: Annals of Operations Research, vol. 249, no. 1-2, pp. 433 – 448, 2017, (Cited by: 2).
@article{Chaovalitwongse2017433,
title = {A decision support model for staff allocation of mobile medical service},
author = {Paveena Chaovalitwongse and Krongsin Somprasonk and Naragain Phumchusri and Joseph Heim and Zelda B. Zabinsky and W. Art Chaovalitwongse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940704057&doi=10.1007%2fs10479-015-1991-5&partnerID=40&md5=bd0507a4b6febe0357508fd405821c02},
doi = {10.1007/s10479-015-1991-5},
year = {2017},
date = {2017-01-01},
journal = {Annals of Operations Research},
volume = {249},
number = {1-2},
pages = {433 – 448},
publisher = {Springer New York LLC},
abstract = {Princess Mother’s Medical Volunteer (PMMV) Foundation is the most recognized and significant free-of-charge mobile medical service (MMS) provider in Thailand. They require volunteers from partner hospitals to give medical care to poor populations residing in remote areas of the country where access to general medical services is limited. Volunteers usually include four types of staff: doctors, dentists, nurses, and pharmacists. According to their operational plan, the PMMV and their working partners need to properly allocate/assign volunteer medical staff to operation sites according to site requirements. In current planning process, the PMMV has to organize massive amounts of data from different organizations in the country, resulting in a long processing time for allocation decisions. In addition, the current process does not allow decision makers to efficiently allocate medical staff with acceptable transportation cost. There is a significant opportunity to improve this process by using analytical models to support this decision making. Thus, this paper proposes a decision support model for staff allocation. The proposed model is in a form of computer information system (CIS) that is carefully developed to facilitate the access to heterogeneous data and ease of use by decision makers. The proposed CIS will assist the PMMV central offices and partners to manage massive data more efficiently and effectively, while the decision algorithm can facilitate planners to achieve the lowest possible cost associated with their decisions. The outcomes of this research were verified by potential users through a focus group manner. The result showed that the potential users were very satisfied with the overall performance of this system. © 2015, Springer Science+Business Media New York.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Princess Mother’s Medical Volunteer (PMMV) Foundation is the most recognized and significant free-of-charge mobile medical service (MMS) provider in Thailand. They require volunteers from partner hospitals to give medical care to poor populations residing in remote areas of the country where access to general medical services is limited. Volunteers usually include four types of staff: doctors, dentists, nurses, and pharmacists. According to their operational plan, the PMMV and their working partners need to properly allocate/assign volunteer medical staff to operation sites according to site requirements. In current planning process, the PMMV has to organize massive amounts of data from different organizations in the country, resulting in a long processing time for allocation decisions. In addition, the current process does not allow decision makers to efficiently allocate medical staff with acceptable transportation cost. There is a significant opportunity to improve this process by using analytical models to support this decision making. Thus, this paper proposes a decision support model for staff allocation. The proposed model is in a form of computer information system (CIS) that is carefully developed to facilitate the access to heterogeneous data and ease of use by decision makers. The proposed CIS will assist the PMMV central offices and partners to manage massive data more efficiently and effectively, while the decision algorithm can facilitate planners to achieve the lowest possible cost associated with their decisions. The outcomes of this research were verified by potential users through a focus group manner. The result showed that the potential users were very satisfied with the overall performance of this system. © 2015, Springer Science+Business Media New York.
2016
Anupong Wannakrairot, Naragain Phumchusri
Two-dimensional air cargo overbooking models under stochastic booking request level, show-up rate and booking request density Journal Article
In: Computers and Industrial Engineering, vol. 100, pp. 1 – 12, 2016, (Cited by: 18).
@article{Wannakrairot20161,
title = {Two-dimensional air cargo overbooking models under stochastic booking request level, show-up rate and booking request density},
author = {Anupong Wannakrairot and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981537857&doi=10.1016%2fj.cie.2016.08.001&partnerID=40&md5=e1e301f2824abcdcea723d22d06de83e},
doi = {10.1016/j.cie.2016.08.001},
year = {2016},
date = {2016-01-01},
journal = {Computers and Industrial Engineering},
volume = {100},
pages = {1 – 12},
publisher = {Elsevier Ltd},
abstract = {Overbooking is a revenue management technique which offers products more than the amount that is available because there is a chance that some purchasers may withdraw their bookings. For air cargo industry, overbooking decision is more complex to be made because of the two-dimensional characteristic: volume and weight of the booking requests. This paper develops two-dimensional air cargo overbooking models to find the optimal overbooking level in order to minimize the total cost, which consists of spoilage and offloading costs. Booking request level, show-up rate, and booking request density are random variables with known distributions. Computational experiments are conducted to explore the impact of model parameters (such as the ratio between spoilage and offloading costs, capacities and booking requests) on the optimal overbooking level. As the optimal overbooking level finding is complicated, we present simplified methods to estimate the solutions of the problem. It is found that the overbooking level obtained from the simplified method using regression model is very close to the optimal solutions (with R-sq(adj) value of 98.3%). A naïve method to find solution is also presented in this paper and we identify situations when it is appropriate to use each method. © 2016 Elsevier Ltd},
note = {Cited by: 18},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Overbooking is a revenue management technique which offers products more than the amount that is available because there is a chance that some purchasers may withdraw their bookings. For air cargo industry, overbooking decision is more complex to be made because of the two-dimensional characteristic: volume and weight of the booking requests. This paper develops two-dimensional air cargo overbooking models to find the optimal overbooking level in order to minimize the total cost, which consists of spoilage and offloading costs. Booking request level, show-up rate, and booking request density are random variables with known distributions. Computational experiments are conducted to explore the impact of model parameters (such as the ratio between spoilage and offloading costs, capacities and booking requests) on the optimal overbooking level. As the optimal overbooking level finding is complicated, we present simplified methods to estimate the solutions of the problem. It is found that the overbooking level obtained from the simplified method using regression model is very close to the optimal solutions (with R-sq(adj) value of 98.3%). A naïve method to find solution is also presented in this paper and we identify situations when it is appropriate to use each method. © 2016 Elsevier Ltd
2015
Naragain Phumchusri, Thanit Panyavai
Electronic kanban system for rubber seals production Journal Article
In: Engineering Journal, vol. 19, no. 1, pp. 38 – 49, 2015, (Cited by: 10; All Open Access, Bronze Open Access, Green Open Access).
@article{Phumchusri201538,
title = {Electronic kanban system for rubber seals production},
author = {Naragain Phumchusri and Thanit Panyavai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922567266&doi=10.4186%2fej.2015.19.1.37&partnerID=40&md5=780e249031ddf8a3f13b5e8f5ba1ba87},
doi = {10.4186/ej.2015.19.1.37},
year = {2015},
date = {2015-01-01},
journal = {Engineering Journal},
volume = {19},
number = {1},
pages = {38 – 49},
publisher = {Chulalongkorn University 1},
abstract = {A key success for automotive suppliers relies on its on-time delivery and efficient manufacturing process. A case-study rubber seal manufacturer is using just in time (JIT) techniques, requiring efficient process flow with low wastes. However, the majority of the current processes still rely on workers’ expertise that can lead to process errors. In 2011, 699 events of rubber supplying delays were found and 525 events or 75.11% of these delays are due to inappropriate ordering and management of the Kanban system in the rubber preparation process. The objective of this research is, therefore, to improve the rubber preparation process by developing a logical method to identify appropriate times for each step and designing information management for the Electronic Kanban System (E-Kanban) to be able to deliver effective and accurate signaling. This E-Kanban system must be able to automatically indicate when each step in the rubber preparation should start so that the rubbers are prepared and ready for the next moulding process by the time they are needed. We also design user interface for an effective use of the system in actual operation. After the developed E-Kanban system was implemented in the factory from July to October 2012, we found that the number of rubber supplying delays could be reduced to 86 events and only 11 events or 12.79% of these delays are from the rubber preparation process. © 2014, ENGINEERING JOURNAL. All rights reserved.},
note = {Cited by: 10; All Open Access, Bronze Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A key success for automotive suppliers relies on its on-time delivery and efficient manufacturing process. A case-study rubber seal manufacturer is using just in time (JIT) techniques, requiring efficient process flow with low wastes. However, the majority of the current processes still rely on workers’ expertise that can lead to process errors. In 2011, 699 events of rubber supplying delays were found and 525 events or 75.11% of these delays are due to inappropriate ordering and management of the Kanban system in the rubber preparation process. The objective of this research is, therefore, to improve the rubber preparation process by developing a logical method to identify appropriate times for each step and designing information management for the Electronic Kanban System (E-Kanban) to be able to deliver effective and accurate signaling. This E-Kanban system must be able to automatically indicate when each step in the rubber preparation should start so that the rubbers are prepared and ready for the next moulding process by the time they are needed. We also design user interface for an effective use of the system in actual operation. After the developed E-Kanban system was implemented in the factory from July to October 2012, we found that the number of rubber supplying delays could be reduced to 86 events and only 11 events or 12.79% of these delays are from the rubber preparation process. © 2014, ENGINEERING JOURNAL. All rights reserved.
Naragain Phumchusri, Yanipa Chinsuwan
Decision support system for open vehicle routing with transhipments and stopovers Journal Article
In: Journal of Computer Science, vol. 11, no. 1, pp. 241 – 253, 2015, (Cited by: 1; All Open Access, Green Open Access, Hybrid Gold Open Access).
@article{Phumchusri2015241,
title = {Decision support system for open vehicle routing with transhipments and stopovers},
author = {Naragain Phumchusri and Yanipa Chinsuwan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923080165&doi=10.3844%2fjcssp.2015.241.253&partnerID=40&md5=9204b68d3fa2dc80110c57a7aa179eb8},
doi = {10.3844/jcssp.2015.241.253},
year = {2015},
date = {2015-01-01},
journal = {Journal of Computer Science},
volume = {11},
number = {1},
pages = {241 – 253},
publisher = {Science Publications},
abstract = {Transshipments and stopovers are considered to be an effective method to reduce traveling distance where a transportation job can be served by two vehicles: One picks up a load and drops it at a transshipment point and then another vehicle carries that load to the final delivery place. The goal of this study is to develop a decision support system for open vehicle routing with transshipments and stopovers. We propose a heuristic to find transshipments and stopovers opportunities from an initial routing. Decision methods consist of four main processes: (1) Searching jobs that allow transshipment opportunity, (2) searching paths that allow transshipment opportunity, (3) matching paths and (4) selecting jobs to create new paths with transshipment. The output is the improved routing with transshipments and stopovers, resulting lower total costs. From computational experiments, our proposed method could reduce the system's total cost up to 12.42 percent as compared to the typical routing without transshipments and stopovers. We design system database and user interfaces, considering all input requirement entering and result displays that are easily used, so that the system can be effectively applied in actual working environments. © 2015 The Naragain Phumchusri and Yanipa Chinsuwan.},
note = {Cited by: 1; All Open Access, Green Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Transshipments and stopovers are considered to be an effective method to reduce traveling distance where a transportation job can be served by two vehicles: One picks up a load and drops it at a transshipment point and then another vehicle carries that load to the final delivery place. The goal of this study is to develop a decision support system for open vehicle routing with transshipments and stopovers. We propose a heuristic to find transshipments and stopovers opportunities from an initial routing. Decision methods consist of four main processes: (1) Searching jobs that allow transshipment opportunity, (2) searching paths that allow transshipment opportunity, (3) matching paths and (4) selecting jobs to create new paths with transshipment. The output is the improved routing with transshipments and stopovers, resulting lower total costs. From computational experiments, our proposed method could reduce the system’s total cost up to 12.42 percent as compared to the typical routing without transshipments and stopovers. We design system database and user interfaces, considering all input requirement entering and result displays that are easily used, so that the system can be effectively applied in actual working environments. © 2015 The Naragain Phumchusri and Yanipa Chinsuwan.
2014
Naragain Phumchusri, Panaratch Maneesophon
Optimal overbooking decision for hotel rooms revenue management Journal Article
In: Journal of Hospitality and Tourism Technology, vol. 5, no. 3, pp. 261 – 277, 2014, (Cited by: 28).
@article{Phumchusri2014261,
title = {Optimal overbooking decision for hotel rooms revenue management},
author = {Naragain Phumchusri and Panaratch Maneesophon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915798672&doi=10.1108%2fJHTT-03-2014-0006&partnerID=40&md5=a40f7cda7f7800356459353b2219bcd8},
doi = {10.1108/JHTT-03-2014-0006},
year = {2014},
date = {2014-01-01},
journal = {Journal of Hospitality and Tourism Technology},
volume = {5},
number = {3},
pages = {261 – 277},
publisher = {Emerald Group Holdings Ltd.},
abstract = {Purpose – This paper aims to develop overbooking models to determine the optimal number of overbooking for hotels having one and two different types of rooms.; Design/methodology/approach – This paper presents mathematical modeling to find the optimal solutions of overbooking for stochastic cancellation.; Findings – The authors prove that for hotels with only one type of room, there exists a closed form solution to guarantee the optimal number of overbooking, depending on the cost of walking customers to other hotels, the cost of unsold rooms and cancellation distribution observed in the past. For hotels with two types of room, they prove the convexity structure and identify equations to seek the number of overbooking for low-price and high-price rooms. The authors also provide key comparative statics on how model parameters impact the optimal decisions under different scenarios.; Practical implications – Overbooking decision is one of important and complicated decision-makings, which is related directly to the yield of hotel revenue management. It is necessary for a hotel manager to observe cancellation pattern in the history to make a reliable decision. This paper presents a method that can help hotel manager make this decision in practice.; Originality/value – This paper is one of the first articles in the hotel industry that considers the marginal cost for each room unsold caused by no shows and the marginal cost for each walking guest in a comprehensive perspective. © Emerald Group Publishing Limited.},
note = {Cited by: 28},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose – This paper aims to develop overbooking models to determine the optimal number of overbooking for hotels having one and two different types of rooms.; Design/methodology/approach – This paper presents mathematical modeling to find the optimal solutions of overbooking for stochastic cancellation.; Findings – The authors prove that for hotels with only one type of room, there exists a closed form solution to guarantee the optimal number of overbooking, depending on the cost of walking customers to other hotels, the cost of unsold rooms and cancellation distribution observed in the past. For hotels with two types of room, they prove the convexity structure and identify equations to seek the number of overbooking for low-price and high-price rooms. The authors also provide key comparative statics on how model parameters impact the optimal decisions under different scenarios.; Practical implications – Overbooking decision is one of important and complicated decision-makings, which is related directly to the yield of hotel revenue management. It is necessary for a hotel manager to observe cancellation pattern in the history to make a reliable decision. This paper presents a method that can help hotel manager make this decision in practice.; Originality/value – This paper is one of the first articles in the hotel industry that considers the marginal cost for each room unsold caused by no shows and the marginal cost for each walking guest in a comprehensive perspective. © Emerald Group Publishing Limited.
Naragain Phumchusri, Julie Swann
Dynamic pricing with updated demand for the sports and entertainment ticket industry Journal Article
In: Journal of Computer Science, vol. 10, no. 11, pp. 2240 – 2252, 2014, (Cited by: 1; All Open Access, Hybrid Gold Open Access).
@article{Phumchusri20142240,
title = {Dynamic pricing with updated demand for the sports and entertainment ticket industry},
author = {Naragain Phumchusri and Julie Swann},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919722745&doi=10.3844%2fjcssp.2014.2240.2252&partnerID=40&md5=ed1fac895c6524159b30de20e0201fee},
doi = {10.3844/jcssp.2014.2240.2252},
year = {2014},
date = {2014-01-01},
journal = {Journal of Computer Science},
volume = {10},
number = {11},
pages = {2240 – 2252},
publisher = {Science Publications},
abstract = {Revenue Management (RM) helped increase profitability for many travel industries. Selling perishable products with a fixed event date, the Sports and Entertainment (S&E) ticket industry can potentially benefit from RM ideas but has received less attention in the literature. In this study we develop dynamic pricing models for stochastic S&E demand in a discrete finite time setting, where demand depends not only on ticket prices but also on remaining times until the show dates. We assume the show popularity is uncertain to the seller, but this information can be learned via Bayesian updates as early sales are revealed. We present stochastic dynamic programs for Sports and Entertainment tickets pricing decisions. We test the models using real data obtained from a major performance venue in the U.S. to understand properties of the model solutions and performance under different scenarios. Our results show that demand learning is most beneficial when the initial estimates are incorrect. In addition, we found it is less necessary for the seller to vary price every period if demand variation is low and/or a large amount of demand arrives close to the show dates. Overall, we found that the benefits from having flexibility of price changes and demand learning can complement each other to achieve as much as 8.15% revenue increase on average, as compared to static pricing. © 2014 Phumchusri and Swann.},
note = {Cited by: 1; All Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Revenue Management (RM) helped increase profitability for many travel industries. Selling perishable products with a fixed event date, the Sports and Entertainment (S&E) ticket industry can potentially benefit from RM ideas but has received less attention in the literature. In this study we develop dynamic pricing models for stochastic S&E demand in a discrete finite time setting, where demand depends not only on ticket prices but also on remaining times until the show dates. We assume the show popularity is uncertain to the seller, but this information can be learned via Bayesian updates as early sales are revealed. We present stochastic dynamic programs for Sports and Entertainment tickets pricing decisions. We test the models using real data obtained from a major performance venue in the U.S. to understand properties of the model solutions and performance under different scenarios. Our results show that demand learning is most beneficial when the initial estimates are incorrect. In addition, we found it is less necessary for the seller to vary price every period if demand variation is low and/or a large amount of demand arrives close to the show dates. Overall, we found that the benefits from having flexibility of price changes and demand learning can complement each other to achieve as much as 8.15% revenue increase on average, as compared to static pricing. © 2014 Phumchusri and Swann.