2018
Thekinen, J; Han, Y; Panchal, J
Designing Market Thickness and Optimal Frequency of Multi-Period Stable Matching in Cloud-Based Design and Manufacturing Conference
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, Canada, 51722 , American Society of Mechanical Engineers 2018.
Abstract | BibTeX | Tags: Cloud-based Design and Manufacturing, Mechanism Design | Links:
@conference{thekinen2018designing,
title = {Designing Market Thickness and Optimal Frequency of Multi-Period Stable Matching in Cloud-Based Design and Manufacturing},
author = {Thekinen, J and Han, Y and Panchal, J},
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2018_IDETC_DESIGNING-MARKET-THICKNESS-AND-OPTIMAL-FREQUENCY-OF-MULTI-PERIOD-STABLE-MATCHING-IN-CLOUD-BASED-DESIGN-AND-MANUFACTURING.pdf},
year = {2018},
date = {2018-08-16},
urldate = {2018-08-16},
booktitle = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, Canada},
volume = {51722},
pages = {V01AT02A024},
organization = {American Society of Mechanical Engineers},
abstract = {A central issue in two-sided matching markets such as Cloud-Based Design and Manufacturing (CBDM) where agents interact over a long period of time is the design of optimal matching period during recursive implementation. Existing literature provides mechanisms that satisfy useful properties such as stability in a single matching cycle, but they lack studies on the effect of the period of matching cycle on the optimality. To address this gap, we perform simulation studies on a synthetic CBDM scenario where service seekers arrive as a Poisson process with a fixed number of service providers offering resources. We identify the optimal matching period and assess its robustness using sensitivity studies. Optimality is measured in terms of utility obtained by the agents, the number of matches and fairness of the utility distribution. We show that a matching period equal to the ratio of the number of service providers to the arrival rate of service seekers is optimal.},
keywords = {Cloud-based Design and Manufacturing, Mechanism Design},
pubstate = {published},
tppubtype = {conference}
}
A central issue in two-sided matching markets such as Cloud-Based Design and Manufacturing (CBDM) where agents interact over a long period of time is the design of optimal matching period during recursive implementation. Existing literature provides mechanisms that satisfy useful properties such as stability in a single matching cycle, but they lack studies on the effect of the period of matching cycle on the optimality. To address this gap, we perform simulation studies on a synthetic CBDM scenario where service seekers arrive as a Poisson process with a fixed number of service providers offering resources. We identify the optimal matching period and assess its robustness using sensitivity studies. Optimality is measured in terms of utility obtained by the agents, the number of matches and fairness of the utility distribution. We show that a matching period equal to the ratio of the number of service providers to the arrival rate of service seekers is optimal.
2017
Thekinen, J; Panchal, J
Resource Allocation in Cloud-based Design and Manufacturing: A Mechanism Design Approach Journal Article
In: Journal of Manufacturing Systems, 43 , pp. 327–338, 2017.
Abstract | BibTeX | Tags: Cloud-based Design and Manufacturing, Mechanism Design | Links:
@article{thekinen2017resource,
title = {Resource Allocation in Cloud-based Design and Manufacturing: A Mechanism Design Approach},
author = {Thekinen, J and Panchal, J},
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2017_JMS_Resource-Allocation-in-Cloud-based-Design-and-Manufacturing-A-Mechanism-Design-Approach.pdf},
doi = {10.1016/j.jmsy.2016.08.005},
year = {2017},
date = {2017-09-01},
urldate = {2017-09-01},
journal = {Journal of Manufacturing Systems},
volume = {43},
pages = {327--338},
publisher = {Elsevier},
abstract = {The focus of this paper is on matching service seekers and service providers, such as designers and machine owners, in Cloud-Based Design and Manufacturing (CBDM). In such decentralized scenarios the objectives and preferences of service seekers are different from those of service providers. Current resource configuration methods are unsuitable because they optimize the objectives of only one type of participants -- either service seekers or service providers. Existing marketplaces based on first-come-first-serve (FCFS) approach are inefficient because they may not result in optimal matches. To address these limitations there is a need for mechanisms that result in optimal matching considering the private preferences of all the agents. In this paper, we formulate the resource allocation problem as a bipartite matching problem. Four bipartite matching mechanisms, namely, Deferred Acceptance (DA), Top Trading Cycle (TTC), Munkres, and FCFS are analyzed with respect to desired properties of the mechanisms such as individual rationality, stability, strategy proofness, consistency, monotonicity and Pareto efficiency. Further, the performance of these mechanisms is evaluated under different levels of resource availability through simulation studies. The appropriateness of matching mechanisms for different scenarios in CBDM such as fully decentralized, partially decentralized and totally monopolistic are assessed. Based on the analysis, we conclude that DA is the best mechanism for totally decentralized scenario, TTC is most appropriate when cloud-based resources are used in an organizational scenario, and Munkres is the best mechanism when all resources are owned by a single agent.},
keywords = {Cloud-based Design and Manufacturing, Mechanism Design},
pubstate = {published},
tppubtype = {article}
}
The focus of this paper is on matching service seekers and service providers, such as designers and machine owners, in Cloud-Based Design and Manufacturing (CBDM). In such decentralized scenarios the objectives and preferences of service seekers are different from those of service providers. Current resource configuration methods are unsuitable because they optimize the objectives of only one type of participants -- either service seekers or service providers. Existing marketplaces based on first-come-first-serve (FCFS) approach are inefficient because they may not result in optimal matches. To address these limitations there is a need for mechanisms that result in optimal matching considering the private preferences of all the agents. In this paper, we formulate the resource allocation problem as a bipartite matching problem. Four bipartite matching mechanisms, namely, Deferred Acceptance (DA), Top Trading Cycle (TTC), Munkres, and FCFS are analyzed with respect to desired properties of the mechanisms such as individual rationality, stability, strategy proofness, consistency, monotonicity and Pareto efficiency. Further, the performance of these mechanisms is evaluated under different levels of resource availability through simulation studies. The appropriateness of matching mechanisms for different scenarios in CBDM such as fully decentralized, partially decentralized and totally monopolistic are assessed. Based on the analysis, we conclude that DA is the best mechanism for totally decentralized scenario, TTC is most appropriate when cloud-based resources are used in an organizational scenario, and Munkres is the best mechanism when all resources are owned by a single agent.
2016
Mandhan, N; Thekinen, J; Lo, A; Panchal, J
Matching Designers and 3D Printing Service Providers Using Gale-Shapley Algorithm Conference
Proceedings of the Eleventh International Symposium on Tools and Methods of Competitive Engineering, Aix-en-Provence, France, 2016.
Abstract | BibTeX | Tags: Cloud-based Design and Manufacturing, Mechanism Design | Links:
@conference{mandhan2016matching,
title = {Matching Designers and 3D Printing Service Providers Using Gale-Shapley Algorithm},
author = {Mandhan, N and Thekinen, J and Lo, A and Panchal, J },
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2016_TMCE_MATCHING-DESIGNERS-AND-3D-PRINTING-SERVICE-PROVIDERS-USING-GALE-SHAPLEY-ALGORITHM.pdf},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
booktitle = {Proceedings of the Eleventh International Symposium on Tools and Methods of Competitive Engineering, Aix-en-Provence, France},
abstract = {With the increasing availability of mid-to-low price 3D printers, it is increasingly possible for individuals and medium-sized enterprises to own such machines. However, such owners rarely utilize the full capacity of these machines. The excess capacity can be made available to interested designers who would like to get their designs printed, but do not own the machines. This has resulted in an emergence of online portals, where machine owners can register and advertise their printing resources, and designers can avail these resources to choose the machine that best suits their design. Presently a first-come-first-serve approach is used to match the designers with machine owners. The primary limitations of this approach are that (a) the capacity of the machines is highly under-utilized and (b) the matching is based solely on the designers’ preferences while ignoring the machine owners’ preferences. To address these limitations, we propose the use of Gale-Shapley matching algorithm after applying the utility theory to obtain the designer and manufacturer preferences for one another. The use of Gale-Shapley matching algorithm is evaluated and compared with the first-come-first-serve approach. The results of the study indicate that the approach based on Gale-Shapley matching improves the total social welfare over the present first-come-first-serve approach. While this method is slightly biased in favor of either the designers or the machine owners, given the limitations of the algorithm, both sides have improved utility and get matched to a design or machine high in their preference ordering.},
keywords = {Cloud-based Design and Manufacturing, Mechanism Design},
pubstate = {published},
tppubtype = {conference}
}
With the increasing availability of mid-to-low price 3D printers, it is increasingly possible for individuals and medium-sized enterprises to own such machines. However, such owners rarely utilize the full capacity of these machines. The excess capacity can be made available to interested designers who would like to get their designs printed, but do not own the machines. This has resulted in an emergence of online portals, where machine owners can register and advertise their printing resources, and designers can avail these resources to choose the machine that best suits their design. Presently a first-come-first-serve approach is used to match the designers with machine owners. The primary limitations of this approach are that (a) the capacity of the machines is highly under-utilized and (b) the matching is based solely on the designers’ preferences while ignoring the machine owners’ preferences. To address these limitations, we propose the use of Gale-Shapley matching algorithm after applying the utility theory to obtain the designer and manufacturer preferences for one another. The use of Gale-Shapley matching algorithm is evaluated and compared with the first-come-first-serve approach. The results of the study indicate that the approach based on Gale-Shapley matching improves the total social welfare over the present first-come-first-serve approach. While this method is slightly biased in favor of either the designers or the machine owners, given the limitations of the algorithm, both sides have improved utility and get matched to a design or machine high in their preference ordering.