{"title":"Continuous Assortment Optimization with Logit Choice Probabilities and Incomplete Information","authors":"Yannik Peeters, A. V. D. Boer, M. Mandjes","doi":"10.1287/opre.2021.2235","DOIUrl":"https://doi.org/10.1287/opre.2021.2235","url":null,"abstract":"","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"23 1","pages":"1613-1628"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72761426","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}
In many supply chains, the brand-owning retailer designs product quality and decides the retail price but often outsources its production to suppliers. For products with a short selling season, low reactive capacity in the supply chain requires the supplier to carry out production before the selling season;but the uncertain market demand creates risks of stockout or excess inventory. Suppliers' reactive capacity and demand uncertainty can influence brand owners' product pricing and quality decisions. For example, during the COVID-19 pandemic, automakers faced supply shortages for automotive chips because of the upstream suppliers' limited parts inventory and production capacities, which have prompted the automakers to increase the quality (e.g., producing higher trims with more optional upgrade features) and price of their products to target fewer (high-valuation) consumers. This paper studies the impacts of the supplier's reactive capacity and demand uncertainty on product quality and firm profitability under pull contracts in the supply chain. We find that an increase in the supplier's reactive capacity can lead to higher or lower equilibrium product quality, benefiting the retailer but potentially reducing the supplier's profit. Interestingly, both the retailer and the supplier can be worse off with a higher probability for the high market state (with more high-valuation consumers). Further, a higher probability for the high market state can lead to lower product quality.
{"title":"Effects of Reactive Capacity on Product Quality and Firm Profitability in an Uncertain Market","authors":"Baojun Jiang, Lin Tian","doi":"10.1287/opre.2022.2310","DOIUrl":"https://doi.org/10.1287/opre.2022.2310","url":null,"abstract":"In many supply chains, the brand-owning retailer designs product quality and decides the retail price but often outsources its production to suppliers. For products with a short selling season, low reactive capacity in the supply chain requires the supplier to carry out production before the selling season;but the uncertain market demand creates risks of stockout or excess inventory. Suppliers' reactive capacity and demand uncertainty can influence brand owners' product pricing and quality decisions. For example, during the COVID-19 pandemic, automakers faced supply shortages for automotive chips because of the upstream suppliers' limited parts inventory and production capacities, which have prompted the automakers to increase the quality (e.g., producing higher trims with more optional upgrade features) and price of their products to target fewer (high-valuation) consumers. This paper studies the impacts of the supplier's reactive capacity and demand uncertainty on product quality and firm profitability under pull contracts in the supply chain. We find that an increase in the supplier's reactive capacity can lead to higher or lower equilibrium product quality, benefiting the retailer but potentially reducing the supplier's profit. Interestingly, both the retailer and the supplier can be worse off with a higher probability for the high market state (with more high-valuation consumers). Further, a higher probability for the high market state can lead to lower product quality.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"4 1","pages":"2619-2636"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90749390","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":"Technical Note - Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation","authors":"Meichun Lin, W. T. Huh, H. Krishnan, J. Uichanco","doi":"10.1287/opre.2022.2307","DOIUrl":"https://doi.org/10.1287/opre.2022.2307","url":null,"abstract":"","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"107 1","pages":"1996-2012"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85564537","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}
Many systems arising in applications from engineering design, manufacturing, and healthcare require the use of simulation optimization (SO) techniques to improve their performance. In “Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization,” Q. Zhang and J. Hu propose a randomized approach that integrates ideas from actor-critic reinforcement learning within a class of adaptive search algorithms for solving SO problems. The approach fully retains the previous simulation data and incorporates them into an approximation architecture to exploit knowledge of the objective function in searching for improved solutions. The authors provide a finite-time analysis for the method when only a single simulation observation is collected at each iteration. The method works well on a diverse set of benchmark problems and has the potential to yield good performance for complex problems using expensive simulation experiments for performance evaluation.
{"title":"Actor-Critic-Like Stochastic Adaptive Search for Continuous Simulation Optimization","authors":"Qi Zhang, Jiaqiao Hu","doi":"10.1287/opre.2021.2214","DOIUrl":"https://doi.org/10.1287/opre.2021.2214","url":null,"abstract":"Many systems arising in applications from engineering design, manufacturing, and healthcare require the use of simulation optimization (SO) techniques to improve their performance. In “Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization,” Q. Zhang and J. Hu propose a randomized approach that integrates ideas from actor-critic reinforcement learning within a class of adaptive search algorithms for solving SO problems. The approach fully retains the previous simulation data and incorporates them into an approximation architecture to exploit knowledge of the objective function in searching for improved solutions. The authors provide a finite-time analysis for the method when only a single simulation observation is collected at each iteration. The method works well on a diverse set of benchmark problems and has the potential to yield good performance for complex problems using expensive simulation experiments for performance evaluation.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"41 13 1","pages":"3519-3537"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89249182","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}
A new study in the INFORMS journal Operations Research proposes a data-driven model for conducting strategic workforce planning in organizations. The model optimizes for recruitment and promotions by balancing the risks of not meeting headcount, budget, and productivity constraints, while keeping within a prescribed organizational structure. Analysis using the model indicates that there are increased workforce risks faced by organizations that are not in a state of growth or organizations that face limitations to organizational renewal (such as bureaucracies).
{"title":"Strategic Workforce Planning Under Uncertainty","authors":"Patrick Jaillet","doi":"10.1287/opre.2021.2183","DOIUrl":"https://doi.org/10.1287/opre.2021.2183","url":null,"abstract":"A new study in the INFORMS journal Operations Research proposes a data-driven model for conducting strategic workforce planning in organizations. The model optimizes for recruitment and promotions by balancing the risks of not meeting headcount, budget, and productivity constraints, while keeping within a prescribed organizational structure. Analysis using the model indicates that there are increased workforce risks faced by organizations that are not in a state of growth or organizations that face limitations to organizational renewal (such as bureaucracies).","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"23 2 1","pages":"1042-1065"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75364180","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}
Borzou Rostami, Masoud Chitsaz, O. Arslan, G. Laporte, Andrea Lodi
The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through consolidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.
{"title":"Single Allocation Hub Location with Heterogeneous Economies of Scale","authors":"Borzou Rostami, Masoud Chitsaz, O. Arslan, G. Laporte, Andrea Lodi","doi":"10.1287/opre.2021.2185","DOIUrl":"https://doi.org/10.1287/opre.2021.2185","url":null,"abstract":"The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through consolidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"60 1","pages":"766-785"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73684830","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}
For nearly 60 years, operations research techniques have assisted in the creation of political districting plans, beginning with an integer programming model. This model, which seeks compactness as its objective, tends to generate districts that are contiguous, or nearly so, but provides no guarantee of contiguity. In the paper “Imposing contiguity constraints in political districting models” by Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd, the authors consider and analyze four different contiguity models (two old and two new). Their computer implementation can handle redistricting instances as large as Indiana (1,511 census tracts). Their fastest approach uses a branch-and-cut algorithm, where contiguity constraints are added in a callback. Critically, many variables can be fixed to zero a priori by Lagrangian arguments. All test instances and source code are publicly available.
{"title":"Imposing Contiguity Constraints in Political Districting Models","authors":"Hamidreza Validi, Austin Buchanan, Eugene Lykhovyd","doi":"10.1287/opre.2021.2141","DOIUrl":"https://doi.org/10.1287/opre.2021.2141","url":null,"abstract":"For nearly 60 years, operations research techniques have assisted in the creation of political districting plans, beginning with an integer programming model. This model, which seeks compactness as its objective, tends to generate districts that are contiguous, or nearly so, but provides no guarantee of contiguity. In the paper “Imposing contiguity constraints in political districting models” by Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd, the authors consider and analyze four different contiguity models (two old and two new). Their computer implementation can handle redistricting instances as large as Indiana (1,511 census tracts). Their fastest approach uses a branch-and-cut algorithm, where contiguity constraints are added in a callback. Critically, many variables can be fixed to zero a priori by Lagrangian arguments. All test instances and source code are publicly available.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"93 1","pages":"867-892"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90426339","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}
Replicating cash flows of multiple agents in game-theoretic settings tends to be a challenging task. In this paper, we consider the competitive newsvendor game where multiple newsvendors choose inventory levels before demand arrival and the unmet demand of each newsvendor spills over to multiple other newsvendors. We show that this spillover behavior and the resulting cash flows of each newsvendor can be replicated within a transportation problem after assigning artificial costs on spillover behavior. This replication provides an opportunity to study structural properties of the problem, as well as determine the equilibrium of the game. This paradigm of using artificial costs within an optimization framework to replicate agents’ cash flows can be used in many other games as well.
{"title":"Technical Note - A Monge Sequence-Based Approach to Characterize the Competitive Newsvendor Problem","authors":"S. Bansal, M. Nagarajan","doi":"10.1287/opre.2021.2189","DOIUrl":"https://doi.org/10.1287/opre.2021.2189","url":null,"abstract":"Replicating cash flows of multiple agents in game-theoretic settings tends to be a challenging task. In this paper, we consider the competitive newsvendor game where multiple newsvendors choose inventory levels before demand arrival and the unmet demand of each newsvendor spills over to multiple other newsvendors. We show that this spillover behavior and the resulting cash flows of each newsvendor can be replicated within a transportation problem after assigning artificial costs on spillover behavior. This replication provides an opportunity to study structural properties of the problem, as well as determine the equilibrium of the game. This paradigm of using artificial costs within an optimization framework to replicate agents’ cash flows can be used in many other games as well.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"46 10 1","pages":"805-814"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88515737","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}
Transshipment in retailing is a practice where one outlet ships its excess inventory to another outlet with inventory shortages. By balancing inventories, transshipment can reduce waste and increase fill rate at the same time. In “Separation of Perishable Inventories in Offline Retailing Through Transshipment,” Li, Yu, and Du explore the idea of transshipping perishable goods with a fixed finite lifetime in offline grocery retailing. In the offline retailing of perishable goods, customers typically choose the newest items first, which can lead to substantial waste. They show that, in this context, transshipment plays two roles. One is inventory balancing, which is well known in the literature. The other is inventory separation, which is new to the literature. That is, transshipment allows a retailer to put newer inventory in one outlet and older inventory in the other. This makes it easier to sell older inventory and reduces waste as a result.
{"title":"Separation of Perishable Inventories in Offline Retailing Through Transshipment","authors":"Qing Li, Peiwen Yu, Lilun Du","doi":"10.1287/opre.2021.2144","DOIUrl":"https://doi.org/10.1287/opre.2021.2144","url":null,"abstract":"Transshipment in retailing is a practice where one outlet ships its excess inventory to another outlet with inventory shortages. By balancing inventories, transshipment can reduce waste and increase fill rate at the same time. In “Separation of Perishable Inventories in Offline Retailing Through Transshipment,” Li, Yu, and Du explore the idea of transshipping perishable goods with a fixed finite lifetime in offline grocery retailing. In the offline retailing of perishable goods, customers typically choose the newest items first, which can lead to substantial waste. They show that, in this context, transshipment plays two roles. One is inventory balancing, which is well known in the literature. The other is inventory separation, which is new to the literature. That is, transshipment allows a retailer to put newer inventory in one outlet and older inventory in the other. This makes it easier to sell older inventory and reduces waste as a result.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"41 1","pages":"666-689"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88374635","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}
Modeling Crew Assignments for Urban Transport Services Using Differentiated Flows Public transit agencies need to judiciously deploy their limited crew members to operate numerous daily scheduled services, while meeting duty and working time regulations for each crew member. Since crew costs account for a large portion of the organizations’ operating expenses, minimizing the total crew and transfer costs is very important. But, with hundreds of daily trips and millions of possible crew itineraries, optimizing trip-to-crew assignment decisions is challenging. In “Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models,” Balakrishnan, Mirchandani, and Lin propose a novel integer optimization model that represents itineraries as multicommodity flows, differentiated by first trip and depot, to capture the duty time limits and incorporate additional requirements such as selecting equitable schedules. The authors show that this compact model can be tighter than previous formulations, further strengthen the model, and propose a restricted optimization approach combined with an optimality test to generate near-optimal solutions quickly. Extensive computational tests using well-known and real-life problem instances show that the proposed model and solution approach can be very effective in practice.
{"title":"Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models","authors":"A. Balakrishnan, P. Mirchandani, Sifeng Lin","doi":"10.1287/opre.2021.2155","DOIUrl":"https://doi.org/10.1287/opre.2021.2155","url":null,"abstract":"Modeling Crew Assignments for Urban Transport Services Using Differentiated Flows Public transit agencies need to judiciously deploy their limited crew members to operate numerous daily scheduled services, while meeting duty and working time regulations for each crew member. Since crew costs account for a large portion of the organizations’ operating expenses, minimizing the total crew and transfer costs is very important. But, with hundreds of daily trips and millions of possible crew itineraries, optimizing trip-to-crew assignment decisions is challenging. In “Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models,” Balakrishnan, Mirchandani, and Lin propose a novel integer optimization model that represents itineraries as multicommodity flows, differentiated by first trip and depot, to capture the duty time limits and incorporate additional requirements such as selecting equitable schedules. The authors show that this compact model can be tighter than previous formulations, further strengthen the model, and propose a restricted optimization approach combined with an optimality test to generate near-optimal solutions quickly. Extensive computational tests using well-known and real-life problem instances show that the proposed model and solution approach can be very effective in practice.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"98 1","pages":"690-714"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73839592","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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