Pub Date : 2022-06-25DOI: 10.1007/s10287-022-00428-w
S. Settepanella, Gennaro Amendola, L. Marengo, Connor Minto
{"title":"Divide and conquer: the engineering of delegation","authors":"S. Settepanella, Gennaro Amendola, L. Marengo, Connor Minto","doi":"10.1007/s10287-022-00428-w","DOIUrl":"https://doi.org/10.1007/s10287-022-00428-w","url":null,"abstract":"","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"19 1","pages":"605 - 626"},"PeriodicalIF":0.9,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47685128","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 : 2022-04-13DOI: 10.1007/s10287-021-00420-w
O. Jadidi, F. Firouzi, John S. Loucks, Y. Park
{"title":"Multi-criteria supplier selection problem with fuzzy demand: a newsvendor model","authors":"O. Jadidi, F. Firouzi, John S. Loucks, Y. Park","doi":"10.1007/s10287-021-00420-w","DOIUrl":"https://doi.org/10.1007/s10287-021-00420-w","url":null,"abstract":"","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"19 1","pages":"375 - 394"},"PeriodicalIF":0.9,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49278613","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 : 2022-01-01Epub Date: 2022-01-27DOI: 10.1007/s10287-022-00423-1
Sebastian Sund, Lars H Sendstad, Jacco J J Thijssen
Technological innovations often create new markets and this gives incentives to learn about their associated profitabilities. However, this decision depends not only on the underlying uncertain profitability, but also on attitudes towards risk. We develop a decision-support tool that accounts for the impact of learning for a potentially risk-averse decision maker. The Kalman filter is applied to derive a time-varying estimate of the process, and the option is valued as dependent on this estimation. We focus on linear stochastic processes with normally distributed noise. Through a numerical example, we find that the marginal benefit of learning decreases rapidly over time, and that the majority of investment times occur early in the option holding period, after the holder has realized the main benefits of learning, and that risk aversion leads to earlier adoption. We find that risk-aversion reduces the value of learning and thus reduces the additional value of waiting and observing noisy signals through time.
{"title":"Kalman filter approach to real options with active learning.","authors":"Sebastian Sund, Lars H Sendstad, Jacco J J Thijssen","doi":"10.1007/s10287-022-00423-1","DOIUrl":"10.1007/s10287-022-00423-1","url":null,"abstract":"<p><p>Technological innovations often create new markets and this gives incentives to learn about their associated profitabilities. However, this decision depends not only on the underlying uncertain profitability, but also on attitudes towards risk. We develop a decision-support tool that accounts for the impact of learning for a potentially risk-averse decision maker. The Kalman filter is applied to derive a time-varying estimate of the process, and the option is valued as dependent on this estimation. We focus on linear stochastic processes with normally distributed noise. Through a numerical example, we find that the marginal benefit of learning decreases rapidly over time, and that the majority of investment times occur early in the option holding period, after the holder has realized the main benefits of learning, and that risk aversion leads to earlier adoption. We find that risk-aversion reduces the value of learning and thus reduces the additional value of waiting and observing noisy signals through time.</p>","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"19 3","pages":"457-490"},"PeriodicalIF":0.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10294644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-02-28DOI: 10.1007/s10287-022-00421-3
René Y Glogg, Anna Timonina-Farkas, Ralf W Seifert
Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization framework, we minimize the total production cost for a manufacturer interested in finding optimal disruption mitigation strategies. The problem constitutes a convex network flow program under a chance constraint bounding the manufacturer's regrets in disrupted scenarios. Thus, in contrast to standard bilevel optimization schemes with two decision-makers, a leader and a follower, our model searches for the optimal production plan of a manufacturer in view of a reduction in the sequence of his own scenario-specific regrets. Defined as the difference in costs of a reactive plan, which considers the disruption as unknown until it occurs, and a benchmark anticipative plan, which predicts the disruption in the beginning of the planning horizon, the regrets allow measurement of the impact of scenario-specific production strategies on the manufacturer's total cost. For an efficient solution of the problem, we employ generalized Benders decomposition and develop customized feasibility cuts. In the managerial section, we discuss the implications for the risk-adjusted production and observe that the regrets of long disruptions are reduced in our mitigation strategy at the cost of shorter disruptions, whose regrets typically stay far below the risk threshold. This allows a decrease of the production cost under rare but high-impact disruption scenarios.
{"title":"Modeling and mitigating supply chain disruptions as a bilevel network flow problem.","authors":"René Y Glogg, Anna Timonina-Farkas, Ralf W Seifert","doi":"10.1007/s10287-022-00421-3","DOIUrl":"10.1007/s10287-022-00421-3","url":null,"abstract":"<p><p>Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization framework, we minimize the total production cost for a manufacturer interested in finding optimal disruption mitigation strategies. The problem constitutes a convex network flow program under a chance constraint bounding the manufacturer's regrets in disrupted scenarios. Thus, in contrast to standard bilevel optimization schemes with two decision-makers, a leader and a follower, our model searches for the optimal production plan of a manufacturer in view of a reduction in the sequence of his own scenario-specific regrets. Defined as the difference in costs of a <i>reactive plan</i>, which considers the disruption as unknown until it occurs, and a benchmark <i>anticipative plan</i>, which predicts the disruption in the beginning of the planning horizon, the regrets allow measurement of the impact of scenario-specific production strategies on the manufacturer's total cost. For an efficient solution of the problem, we employ generalized Benders decomposition and develop customized feasibility cuts. In the managerial section, we discuss the implications for the risk-adjusted production and observe that the regrets of long disruptions are reduced in our mitigation strategy at the cost of shorter disruptions, whose regrets typically stay far below the risk threshold. This allows a decrease of the production cost under rare but high-impact disruption scenarios.</p>","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"19 3","pages":"395-423"},"PeriodicalIF":0.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9895055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-15DOI: 10.1007/s10287-023-00463-1
Michele Azzone, R. Baviera
{"title":"A fast Monte Carlo scheme for additive processes and option pricing","authors":"Michele Azzone, R. Baviera","doi":"10.1007/s10287-023-00463-1","DOIUrl":"https://doi.org/10.1007/s10287-023-00463-1","url":null,"abstract":"","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"20 1","pages":"1-34"},"PeriodicalIF":0.9,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42254980","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 : 2021-11-15DOI: 10.1007/s10287-021-00417-5
D. Ávila, A. Papavasiliou, N. Löhndorf
{"title":"Correction to: Parallel and distributed computing for stochastic dual dynamic programming","authors":"D. Ávila, A. Papavasiliou, N. Löhndorf","doi":"10.1007/s10287-021-00417-5","DOIUrl":"https://doi.org/10.1007/s10287-021-00417-5","url":null,"abstract":"","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"19 1","pages":"227 - 228"},"PeriodicalIF":0.9,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43691204","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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