{"title":"Neural Model of Conveyor Type Transport System","authors":"O. Pihnastyi, V. Khodusov","doi":"10.32782/cmis/2608-60","DOIUrl":"https://doi.org/10.32782/cmis/2608-60","url":null,"abstract":"In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system","PeriodicalId":321796,"journal":{"name":"Ross: Technology & Operations (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133561844","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}
{"title":"The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis","authors":"Zhaohui (Zoey) Jiang, Yan Huang, D. Beil","doi":"10.2139/ssrn.2884922","DOIUrl":"https://doi.org/10.2139/ssrn.2884922","url":null,"abstract":"In this paper, we empirically examine the impact of performance feedback on the outcome of crowdsourcing contests. We develop a dynamic structural model to capture the economic processes that drive contest participants’ behavior and estimate the model using a detailed data set about real online logo design contests. Our rich model captures key features of the crowdsourcing context, including a large participant pool; entries by new participants throughout the contest; exploitation (revision of previous submissions) and exploration (radically novel submissions) behaviors by contest incumbents; and the participants’ strategic choice among these entry, exploration, and exploitation decisions in a dynamic game. Using counterfactual simulations, we compare the outcome of crowdsourcing contests under alternative feedback disclosure policies and award levels. Our simulation results suggest that, despite its prevalence on many platforms, the full feedback policy (providing feedback throughout the contest) may not be optimal. The late feedback policy (providing feedback only in the second half of the contest) leads to a better overall contest outcome. This paper was accepted by Gabriel Weintraub, revenue management and market analytics department.","PeriodicalId":321796,"journal":{"name":"Ross: Technology & Operations (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182134","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}
Yixin (Iris) Wang, Jun Li, Di (Andrew) Wu, Ravi Anupindi
{"title":"When Ignorance is Not Bliss: An Empirical Analysis of Sub-tier Supply Network Structure on Firm Risk","authors":"Yixin (Iris) Wang, Jun Li, Di (Andrew) Wu, Ravi Anupindi","doi":"10.2139/ssrn.2705654","DOIUrl":"https://doi.org/10.2139/ssrn.2705654","url":null,"abstract":"Using a multitier mapping of supply-chain relationships constructed from granular global, firm-to-firm supplier–customer linkages data, we quantify the degree of financial risk propagation from the supply network beyond firms’ direct supply-chain connections and isolate structural network properties serving as significant moderators of risk propagation. We first document a baseline fact: a significant proportion of tier-2 suppliers are shared by tier-1 suppliers. We then construct two simple metrics to capture the degree of tier-2 sharing and disentangle its effect from tier-2 suppliers’ own risks. We show that the focal firms’ risk levels are significantly related to the proportion of shared tier-2 suppliers in their supply network, and the effect becomes monotonically stronger as their tier-2 suppliers become more highly shared. Finally, we uncover causal relationships behind these associations using a new source of exogenous, idiosyncratic risk events in an event study setting. We show that, as tier-2 suppliers are impacted by these events, focal firms experience negative abnormal returns, the magnitude of which is significantly larger when the impacted tier-2 suppliers are more heavily shared. Overall, our study uncovers the subtier network structure as an important risk source for the focal firm, with the degree of tier-2 sharing as the main moderator. Our results also provide the microfoundation for a common structure in idiosyncratic risks and suggest the importance of incorporating the effect of subtier supply network structure in the portfolio-optimization process. This paper was accepted by Vishal Gaur, operations management.","PeriodicalId":321796,"journal":{"name":"Ross: Technology & Operations (Topic)","volume":"91 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114037766","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}
Hyun-Soo Ahn, Stefanus Jasin, Philip M. Kaminsky, Yang Wang
{"title":"Certainty Equivalent Planning for Multi-Product Batch Differentiation: Analysis and Bounds","authors":"Hyun-Soo Ahn, Stefanus Jasin, Philip M. Kaminsky, Yang Wang","doi":"10.2139/ssrn.2704595","DOIUrl":"https://doi.org/10.2139/ssrn.2704595","url":null,"abstract":"We consider a multi-period planning problem faced by a firm that must coordinate the production and allocations of batches to end products for multiple markets. Motivated by a problem faced by a biopharmaceutical firm, we model this as a discrete-time inventory planning problem where in each period the firm must decide how many batches to produce and how to differentiate batches to meet demands for different end products. This is a challenging problem to solve optimally, so we derive a theoretical bound on the performance of a Certainty Equivalent (CE) control for this model, in which all random variables are replaced by their expected values and the corresponding deterministic optimization problem is solved. This is a variant of an approach that is widely used in practice. We show that while a CE control can perform very poorly in certain instances, a simple re-optimization of the CE control in each period can substantially improve both the theoretical and computational performance of the heuristic, and we bound the performance of this re-optimization. To address the limitations of CE control and provide guidance for heuristic design, we also derive performance bounds for two additional heuristic controls -- (1) Re-optimized Stochastic Programming (RSP), which utilizes full demand distribution but limits the adaptive nature of decision dynamics, and (2) Multi-Point Approximation (MPA), which uses limited demand information to model uncertainty but fully capture the adaptive nature of decision dynamics. We show that although RSP in general outperforms the re-optimized CE control, the improvement is limited. On the other hand, with a carefully chosen demand approximation in each period, MPA can significantly outperform RSP. This suggests that, in our setting, explicitly capturing decision dynamics adds more value than simply capturing full demand information.","PeriodicalId":321796,"journal":{"name":"Ross: Technology & Operations (Topic)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126845078","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}
{"title":"Benefits of Collaboration in Capacity Investment and Allocation","authors":"Eren Cetinkaya, Hyun-Soo Ahn, Izak Duenyas","doi":"10.2139/ssrn.2169490","DOIUrl":"https://doi.org/10.2139/ssrn.2169490","url":null,"abstract":"This paper studies capacity collaboration between two (potentially competing) firms. We explore the ways that the firms can collaborate by either building capacity together or sharing the existing capacity for production. We consider cases where the two firms' products are potential substitutes and also where the firms' products are independent. We find that a firm can benefit from collaboration even with its competitor. Moreover, the firms do not have to jointly make the production decisions to realize the benefits of collaboration. We consider a model where firms build capacity before demand is realized and make production decisions after they receive a demand signal. They can potentially collaborate in jointly building capacity and/or in exchanging capacity once they receive their demand signals. Interestingly, we find that having firms compete at the production stage can result in firms deciding to build less overall capacity than if they coordinated capacity investment and production. Also, we find that though collaboration in capacity investment is bene cial, collaboration in production using existing capacity is often more beneficial. The benefits of collaboration is largest when competition is more intense, demand is more variable and cost of investment is higher.","PeriodicalId":321796,"journal":{"name":"Ross: Technology & Operations (Topic)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114910210","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}