Manufacturing Operations Management Research Articles

A Conceptual Model for Operational Control in Smart Manufacturing Systems

Advancements in computer integrated manufacturing and intelligent devices have spurred a revolution in manufacturing. To maximize their potential, smart operational control mechanisms must not only integrate real-time data from system operations, but also formulate and solve a wide variety of optimal-control analyses quickly and efficiently and then translate the results into executable commands. To support design of smart operational controllers, this paper proposes a conceptual model capable of integrating a description of the control activities in the manufacturing operations management level of the enterprise with a description of the physical system and an explicit interface to optimal-control analyses.

2014 M&SOM Best Paper Award

I is a pleasure to announce that the 2014 Manufacturing & Service Operations Management Best Paper Award goes to Philipp Afeche for “IncentiveCompatible Revenue Management in Queueing Systems: Optimal Strategic Delay” for its contribution to theory and practice of operations management. The author will receive a $2,000 prize, contributed by the MSOM Society of INFORMS. Each year, all papers published in the journal during the past three years (2011–2013) are eligible for the award, and judging is done through a two-stage process. First, M&SOM Editor Steve Graves and a group of M&SOM associate editors select a small set of finalists from the nominations received. Second, an ad hoc committee, organized by the immediate past president of the MSOM Society, selects the best paper from among the finalists. This year, the ad hoc committee consisted of Charles Corbett, Jeremie Gallien, Amy Ward, and MSOM pastpresident Beril Toktay. They selected the winning paper from a set of five outstanding finalists. The winning paper studies the revenue-maximizing price and lead-time menu and scheduling policy in a queueing system when customers are heterogeneous with respect to their valuations and time sensitivity, and their type is not observable to the firm. A similar problem has been studied from the social optimality perspective. The novelty of this paper stems from the fact that it studies the problem from the point of view of revenue maximization. This makes a fundamental difference. From the social optimality perspective, it makes sense to consider only work-conserving scheduling policies (i.e., the server does not idle in the presence of a queue). This is not the case when the objective is revenue maximization: The firm may want to “strategically delay” the low-priority customers to make sure that the time-sensitive customers choose the high-price, fast-service option. The paper establishes the “strategic delay” concept and develops interesting managerial insights. The following reviewer commentary reflects points made by many of the reviewers:

Box-Cox Transformation Approach for Evaluating Non-Normal Processes Capability Based on theCpkIndex

AbstractIn manufacturing quality control and operations management, the process yield plays an important role. The capability index Cpk provides a lower bound on the process yield under the assumption that the process characteristic is normally distributed. When the normality assumption is violated, we can transform the non-normal data into normal data by using an appropriate transformation approach. In this paper, we consider the Box-Cox transformation and compare two estimation methods including the maximum likelihood estimator (MLE) and the method of percentiles (MOP). The performance comparison is based on the coverage rate, the precision, and the accuracy of the process non-conformity percentage evaluation. For various sample sizes and various distributions, several figures are presented to compare these two methods.

OR/MS journals evaluation based on a refined PageRank method: an updated and more comprehensive review

The purpose of this research is to furnish the OR/MS research community with an updated assessment of the discipline's journals set with refinements that also highlight the various characteristics of OR/MS journals. More specifically, we apply a refined PageRank method initially proposed by Xu et al. (2011) to evaluate the top 31 OR/MS journals for 2010, and report our findings. We also report the shifts in the rankings that span 5 years, from 2006 to 2010. We observe that Manufacturing and Service Operations Management, indexed by the SCI only in 2008, is a specialized journal that is consistently highly regarded within the discipline. The rankings also suggest that Management Science is more established as a generalized journal as it has more external impact. In general, our ranking results correlate with expert opinions, and we also observe, report and discuss some interesting patterns that have emerged over the past 5 years from 2006 to 2010.

Understanding environmental-operations integration: The case of pollution prevention projects

To achieve environmentally sound manufacturing processes, environmental management (EM) and operations management (OM) need to be implemented in an integrated manner. The literature characterizing the relationship between EM and OM has broadly investigated the positive effects of environmental practices on plant performance, but has paid little attention to their implementation. However, there is not a unique way to implement EM–OM, and different levels of integration might be attained. We draw on the contingency approach and project management literature to study how and under what conditions EM–OM might be integrated. Based on the analysis of six pollution prevention projects at three large multinational firms, we find that EM–OM integration is contingent upon the degree of project uncertainty and project complexity.

MSOM Society Student Paper Competition: Abstracts of 2013 Winners

The journal is pleased to publish the abstracts of the six finalists of the 2013 Manufacturing and Service Operations Management Society’s student paper competition. The 2013 prize committee was chaired by Felipe Caro (UCLA Anderson School of Management, University of California, Los Angeles), Guillaume Roels (UCLA Anderson School of Management, University of California, Los Angeles), and Gabriel Weintraub (Graduate School of Business, Columbia University). The other committee members were Philipp Afèche, Vishal Agrawal, Gad Allon, Aydın Alptekinoğlu, Atalay Atasu, Göker Aydın, Opher Baron, Achal Bassamboo, Damian Beil, Omar Besbes, Kostas Bimpikis, Robert Bray, René Caldentey, Carri Chan, Li Chen, Xin Chen, Ying-Ju Chen, Soo-Haeng Cho, Laurens Debo, Nicole DeHoratius, Sarang Deo, Lingxiu Dong, David Drake, Wedad Elmaghraby, Pnina Feldman, Qi Feng, Srinagesh Gavirneni, Karan Girotra, Ming Hu, Tim Huh, Dan Iancu, Foad Iravani, Srikanth Jagabathula, Ganesh Janakiraman, Diwas KC, Eda Kemahlıoğlu-Ziya, Saravanan Kesavan, Sang Kim, Mirko Kremer, Harish Krishnan, Mümin Kurtuluş, Guoming Lai, Jun Li, Ilan Lobel, Ruben Lobel, Elisa Long, Lauren Lu, Victor Martínez-de-Albéniz, Toni Moreno, Hamid Nazerzadeh, Rodney Parker, Ali Parlaktürk, Margaret Pierson, Ramandeep Randhawa, Paat Rusmevichientong, Soroush Saghafian, Denis Saure, Sergei Savin, Nicos Savva, Nicola Secomandi, Kevin Shang, Max Shen, Melvyn Sim, Amitabh Sinha, Larry Snyder, Greys Sošić, Brad Staats, Xuanming Su, Ravi Subramanian, Robert Swinney, Alireza Tahbaz-Salehi, Brian Tomlin, Senthil Veeraraghavan, Gustavo Vulcano, Wenqiang Xiao, Nan Yang, Fuqiang Zhang, Jiawei Zhang, Yao Zhao, Leon Zhu, and Serhan Ziya. The 2013 prize winners are as follows: Two First Prizes Song-Hee (Hailey) Kim, Columbia University “ICU Admission Control: An Empirical Study of Capacity Allocation and its Implication on Patient Outcomes” Xuan Wang, New York University “Process Flexibility: A Distribution-Free Bound on the Performance of k-Chain” Finalists Dragos Florin Ciocan, Massachusetts Institute of Technology “Model Predictive Control for Dynamic Resource Allocation” Nitish Jain, INSEAD “Managing Global Sourcing: Inventory Performance” Anyan Qi, University of Michigan “Capacity Investment with Demand Learning” John Yao, Columbia University “Optimal Price and Delay Differentiation in Queueing Systems”

Review and outlook of work breakdown structure for complex product manufacturing

One of the key problems in complex product manufacturing is the lack of support for multiple management-oriented perspectives in the design and implementation of work processes. If we do not comprehensively analyse and understand the characteristics of complex product manufacturing projects, we cannot make the decomposition results of a work breakdown structure (WBS) effectively guide the work processes. The purpose of this paper is to develop a research framework for understanding the development and usage of WBS in complex product manufacturing by synthesising theory from project and manufacturing operations management disciplines. This framework must identify how to effectively deliver the information from management requirements to the work process that supports the manufacturing process. To accomplish this purpose, we analyse research in project management and compare the results to characteristics of complex product manufacturing. From this analysis, we design a unique framework of WBS construction and usage in complex product manufacturing and suggest future research directions.

Semantics to the Shop Floor: Towards Ontology Modularization and Reuse in the Automation Domain

The traditional way of engineering production systems requires static information flows within and in between automation software, from field control software to manufacturing operations management. The common understanding of data and its containing information is realized by implicit assumptions on data semantics. In contrast, the vision of future production systems as cyber-physical systems (CPS) focuses on intelligent production facilities, which are characterized by autonomous behavior and dynamic, cooperative interactions. As a consequence, data and information that are stored and exchanged within CPS cannot rely on the assumption that other software systems are aware of data semantics. A means to solve this issue is ontologies – an approach being intensely discussed and applied for enhancing data with semantics. However, reuse of ontologies within the automation domain is hampered as ontologies are developed for specific use cases without having reusability in mind. In this paper, these drawbacks of ontology development are discussed and an approach for maximizing reusability through modularizing ontologies for different fields of the automation domain is presented.

Management Science 2012 Best Paper Awards

Starting this year, two best paper awards were established for manuscripts published in the 2010–2012 volumes of Management Science. One is sponsored by the Information Systems Society of INFORMS for the manuscript that is deemed most deserving for its contribution to the theory and practice of information systems. The second is sponsored by the Manufacturing and Service Operations Management Society of INFORMS for the manuscript judged to be most deserving for its contribution to the theory and practice of operations management. For each award, finalists were selected from among a set of nominated papers by the editorial board of Management Science, and the winners were selected by a vote of the society members. The journal is deeply proud that each of these manuscripts appeared in our pages, and we congratulate each of these authors for their fine piece of scholarship.

OM Forum—Sustainable Operations Management: An Enduring Stream or a Passing Fancy?

Paul Kleindorfer was among the first to weigh in on and nurture the stream of sustainable operations management. The thoughts laid out here are based on conversations we had with Paul relating to the drivers underlying sustainability as a management issue: population and per capita consumption growth, the limited nature of resources and sinks, and the responsibility and exposure of firms to ensuing ecological risks and costs. We then discuss how an operations management lens contributes to the issue and criteria to help the sustainable operations management perspective endure. This paper relates to a presentation delivered by Morris Cohen for Paul's Manufacturing and Service Operations Management Distinguished Fellows Award, given at Columbia University, June 18, 2012. We wrote this paper at Paul's request.

Focus on Authors

Focus on Authors

Sustainable Operations Management: An Enduring Stream, or Passing Fancy?

Paul Kleindorfer was among the first to weigh in on and nurture the stream of Sustainable Operations Management. The thoughts laid out here are based on conversations we had with Paul relating to the drivers underlying sustainability as a management issue: population and per capita consumption growth, the limited nature of resources and sinks, and the responsibility and exposure of firms to ensuing ecological risks and costs. We then discuss how an operations management lens contributes to the issue, and criteria to help the Sustainable Operations Management perspective endure. This article relates to a presentation delivered by Morris Cohen for Paul's Manufacturing and Service Operations Management Distinguished Fellows Award, given at Columbia University, June 18, 2012. We wrote this article at Paul's request.

OM Forum—Three Rs of Operations Management: Research, Relevance, and Rewards

This article presents three personal experiences and reflections on the process and objective of research in operations management. This invited OM Forum contribution is based on my Manufacturing and Service Operations Management Distinguished Fellow inaugural lecture given at Columbia University on June 18, 2012.

2012 M&SOM Best Paper Award

I is a great pleasure to announce that the 2012 Manufacturing & Service Operations Management Best Paper Award goes to Victor F. Araman and Ioana Popescu for “Media Revenue Management with Audience Uncertainty: Balancing Upfront and Spot Market Sales” (2010). This paper was chosen as most deserving for its contribution to the theory and practice of operations management. For their accomplishment, Professors Araman and Popescu will share a $2,000 prize, generously contributed by the MSOM Society of INFORMS. Each year, the award is given to one paper published in one of the prior three volumes of M&SOM and is judged through a two-stage process. First, M&SOM Editor Steve Graves and a group of M&SOM Associate Editors review nominated papers to select four finalists. Second, an ad hoc committee, organized by the immediate past president of the MSOM Society, selects the best paper from among the finalists. This year, the ad hoc committee that selected the best paper was comprised of Gad Allon, Tava Olsen, and Robert Shumsky. Noah Gans, MSOM’s past president, organized the review process but did not act as a judge. The richness of the paper’s contribution is reflected in the reviewers’ many positive comments. For example, the following comment explains the paper’s motivation and interest:

Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAX–MIN ant system

The problem of placing safety inventory over a network, which assembles a product, is a challenging issue in supply chain design (SCD) because manufacturers always want to reduce inventory all over the supply chain (SC). Moreover, the process of designing a SC and then placing inventory, to offer high service level at the lowest possible cost, across a complex SC, is not an easy task for decision makers. In this paper we use the SC representation proposed by Graves and Willems (2000), Manufacturing & Service Operations Management 2 (1), 68–83, where a SC is divided into many supplying, manufacturing, and delivering stages. Our problem is to select one resource option to perform each stage, and based on the selected options to place an amount of inventory (in-progress and on-hand) at each stage, in order to offer a satisfactory customer service level with as low as possible total supply chain cost. A resource option here represents a supplier, a manufacturing plant (production line), or a transport mode in a supplying, manufacturing, or delivering stage, respectively. We developed an approach based on ant colony optimisation (ACO) to minimise simultaneously the total supply chain cost and the products’ lead time to ensure product deliveries without delays. What are new in our approach are the bi-objective function and the computational efficiency of our ACO-based approach. In addition, ACO has not been applied to solve the inventory placement problem. As a validation of the model, we: (a) describe a successful application at CIFUNSA, one of the largest iron foundry in the world, and (b) compare different CPU time instances and metrics about multi-objective optimisation.

A linear response bandit problem

Open AccessOpen Access licenseAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Go to SectionOpen AccessOpen Access license HomeStochastic SystemsVol. 3, No. 1 A Linear Response Bandit ProblemAlexander Goldenshluger, Assaf ZeeviAlexander Goldenshluger, Assaf ZeeviPublished Online:26 Aug 2013https://doi.org/10.1287/11-SSY032AbstractWe consider a two–armed bandit problem which involves sequential sampling from two non-homogeneous populations. The response in each is determined by a random covariate vector and a vector of parameters whose values are not known a priori. The goal is to maximize cumulative expected reward. We study this problem in a minimax setting, and develop rate-optimal polices that combine myopic action based on least squares estimates with a suitable “forced sampling” strategy. It is shown that the regret grows logarithmically in the time horizon n and no policy can achieve a slower growth rate over all feasible problem instances. In this setting of linear response bandits, the identity of the sub-optimal action changes with the values of the covariate vector, and the optimal policy is subject to sampling from the inferior population at a rate that grows like n. Previous Back to Top Next FiguresReferencesRelatedInformationCited ByAdaptive Design of Personalized Dose-Finding Clinical TrialsSaeid Delshad, Amin Khademi21 July 2022 | Service Science, Vol. 0, No. 0Adaptive Sequential Experiments with Unknown Information Arrival ProcessesYonatan Gur, Ahmadreza Momeni10 June 2022 | Manufacturing & Service Operations Management, Vol. 0, No. 0Fast Rates for Contextual Linear OptimizationYichun Hu, Nathan Kallus, Xiaojie Mao29 March 2022 | Management Science, Vol. 68, No. 6Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret RegimesYichun Hu, Nathan Kallus, Xiaojie Mao7 February 2022 | Operations Research, Vol. 0, No. 0Smoothness-Adaptive Contextual BanditsYonatan Gur, Ahmadreza Momeni, Stefan Wager26 January 2022 | Operations Research, Vol. 0, No. 0Multimodal Dynamic PricingYining Wang, Boxiao Chen, David Simchi-Levi27 January 2021 | Management Science, Vol. 67, No. 10Ranking and Selection with Covariates for Personalized Decision MakingHaihui Shen, L. Jeff Hong, Xiaowei Zhang12 February 2021 | INFORMS Journal on Computing, Vol. 33, No. 4Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban WarehousesRong Jin, David Simchi-Levi, Li Wang, Xinshang Wang, Sen Yang12 January 2021 | Management Science, Vol. 67, No. 8Nonparametric Pricing Analytics with Customer CovariatesNingyuan Chen, Guillermo Gallego15 February 2021 | Operations Research, Vol. 69, No. 3Mostly Exploration-Free Algorithms for Contextual BanditsHamsa Bastani, Mohsen Bayati, Khashayar Khosravi27 July 2020 | Management Science, Vol. 67, No. 3Dynamic Assortment Personalization in High DimensionsNathan Kallus, Madeleine Udell13 May 2020 | Operations Research, Vol. 68, No. 4Online Decision Making with High-Dimensional CovariatesHamsa Bastani, Mohsen Bayati7 November 2019 | Operations Research, Vol. 68, No. 1Optimal Prescriptive TreesDimitris Bertsimas, Jack Dunn, Nishanth Mundru16 April 2019 | INFORMS Journal on Optimization, Vol. 1, No. 2 Volume 3, Issue 1June 2013Pages 1-321 Article Information Metrics Downloaded 328 times in the past 12 months Information Received:August 01, 2011Published Online:August 26, 2013 Copyright © 2013, The author(s)Cite asAlexander Goldenshluger, Assaf Zeevi (2013) A Linear Response Bandit Problem. Stochastic Systems 3(1):230-261. https://doi.org/10.1287/11-SSY032 KeywordsSequential allocationestimationbandit problemsregretminimaxrate–optimal policyPDF download

Tuning approximate dynamic programming policies for ambulance redeployment via direct search

Open AccessOpen Access licenseAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Go to SectionOpen AccessOpen Access license HomeStochastic SystemsVol. 3, No. 2 Tuning Approximate Dynamic Programming Policies for Ambulance Redeployment via Direct SearchMatthew S. Maxwell, Shane G. Henderson, Huseyin TopalogluMatthew S. Maxwell, Shane G. Henderson, Huseyin TopalogluPublished Online:15 Nov 2013https://doi.org/10.1287/10-SSY020AbstractIn this paper we consider approximate dynamic programming methods for ambulance redeployment. We first demonstrate through simple examples how typical value function fitting techniques, such as approximate policy iteration and linear programming, may not be able to locate a high-quality policy even when the value function approximation architecture is rich enough to provide the optimal policy. To make up for this potential shortcoming, we show how to use direct search methods to tune the parameters in a value function approximation architecture so as to obtain high-quality policies. Direct search is computationally intensive. We therefore use a post-decision state dynamic programming formulation of ambulance redeployment that, together with direct search, requires far less computation with no noticeable performance loss. We provide further theoretical support for the post-decision state formulation of the ambulance-deployment problem by showing that this formulation can be obtained through a limiting argument on the original dynamic programming formulation. Back to Top Next FiguresReferencesRelatedInformationCited byInpatient Overflow: An Approximate Dynamic Programming ApproachJ. G. Dai, Pengyi Shi16 May 2019 | Manufacturing & Service Operations Management, Vol. 21, No. 4A Unified Framework for Optimization Under UncertaintyWarren B. Powell4 November 2016The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance VehiclesThije van Barneveld21 April 2016 | INFORMS Journal on Computing, Vol. 28, No. 2Discrete Optimization Models for Homeland Security and Disaster ManagementLaura Albert McLay26 October 2015Simulation-Based Approximate Policy Iteration with Generalized Logistic FunctionsAntoine Sauré, Jonathan Patrick, Martin L. Puterman28 September 2015 | INFORMS Journal on Computing, Vol. 27, No. 3Technical Note—Trading Off Quick versus Slow Actions in Optimal SearchSteven M. Shechter, Farhad Ghassemi, Yasin Gocgun, Martin L. Puterman6 March 2015 | Operations Research, Vol. 63, No. 2A Bound on the Performance of an Optimal Ambulance Redeployment PolicyMatthew S. Maxwell, Eric Cao Ni, Chaoxu Tong, Shane G. Henderson, Huseyin Topaloglu, Susan R. Hunter16 July 2014 | Operations Research, Vol. 62, No. 5 Volume 3, Issue 2December 2013Pages 322-633 Article Information Metrics Information Received:November 01, 2010Published Online:November 15, 2013 Copyright © 2013, The author(s)Cite asMatthew S. Maxwell, Shane G. Henderson, Huseyin Topaloglu (2013) Tuning Approximate Dynamic Programming Policies for Ambulance Redeployment via Direct Search. Stochastic Systems 3(2):322-361. https://doi.org/10.1287/10-SSY020 PDF download

Many-server queues with customer abandonment: Numerical analysis of their diffusion model

Open AccessOpen Access licenseAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Go to SectionOpen AccessOpen Access license HomeStochastic SystemsVol. 3, No. 1 Many-server Queues with Customer Abandonment: Numerical Analysis of their Diffusion ModelJ. G. Dai, Shuangchi HeJ. G. Dai, Shuangchi HePublished Online:20 May 2013https://doi.org/10.1287/11-SSY029AbstractWe use a multidimensional diffusion process to approximate the dynamics of a queue served by many parallel servers. Waiting customers in this queue may abandon the system without service. To analyze the diffusion model, we develop a numerical algorithm for computing its stationary distribution. A crucial part of the algorithm is choosing an appropriate reference density. Using a conjecture on the tail behavior of the limit queue length process, we propose a systematic approach to constructing a reference density. With the proposed reference density, the algorithm is shown to converge quickly in numerical experiments. These experiments demonstrate that the diffusion model is a satisfactory approximation for many-server queues, sometimes for queues with as few as twenty servers. Previous Back to Top Next FiguresReferencesRelatedInformationCited BySRPT Scheduling Discipline in Many-Server Queues with Impatient CustomersJing Dong, Rouba Ibrahim21 October 2021 | Management Science, Vol. 67, No. 12Diffusion Approximation for Efficiency-Driven Queues When Customers Are PatientShuangchi He22 May 2020 | Operations Research, Vol. 68, No. 4Dynamic Scheduling in a Many-Server, Multiclass System: The Role of Customer Impatience in Large SystemsJeunghyun Kim, Ramandeep S. Randhawa, Amy R. Ward11 April 2018 | Manufacturing & Service Operations Management, Vol. 20, No. 2Stein’s Method for Steady-state Diffusion Approximations: An Introduction through the Erlang-A and Erlang-C ModelsAnton Braverman, J. G. Dai, Jiekun Feng17 February 2017 | Stochastic Systems, Vol. 6, No. 2A Unified Approach to Diffusion Analysis of Queues with General Patience-Time DistributionsJunfei Huang, Hanqin Zhang, Jiheng Zhang3 March 2016 | Mathematics of Operations Research, Vol. 41, No. 3 Volume 3, Issue 1June 2013Pages 1-321 Article Information Metrics Downloaded 129 times in the past 12 months Information Received:April 01, 2011Published Online:May 20, 2013 Copyright © 2013, The author(s)Cite asJ. G. Dai, Shuangchi He (2013) Many-server Queues with Customer Abandonment: Numerical Analysis of their Diffusion Model. Stochastic Systems 3(1):96-146. https://doi.org/10.1287/11-SSY029 KeywordsDiffusion processstationary distributionphase-type distributionmany-server queueheavy trafficcustomer abandonmentquality- and efficiency-driven regimePDF download

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Model Driven Key Performance Indicators Concepts for Manufacturing Execution Systems

The intention of this paper is to give some methodical approach in Key Performance Indicators for Manufacturing. Within this it covers the defining of Key Performance Indicators (KPI) and Manufacturing Execution Systems (MES). The purpose is to analyze the influences of equipment, personnel and inventory to the production process and how to apply them in making decisions. This paper gives an overview about definition of Manufacturing Execution Systems (MES) and the area of standardization of object models. The KPI for the manufacturing operations management include the areas personnel, equipment, inventory and production process. It contains also a collection of key performance indicators (KPI) to be used in the area of production control and monitoring to assess and define the targets of production processes.