Surveys in Operations Research and Management Science最新文献

{"title":"A Stochastic Approximation Approach for Trend-Following Trading","authors":"D. Nguyen, G. Yin, Qing Zhang","doi":"10.1007/978-1-4899-7442-6_7","DOIUrl":"https://doi.org/10.1007/978-1-4899-7442-6_7","url":null,"abstract":"","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"8 1","pages":"167-184"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89066626","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":"Monte Carlo sampling-based methods for stochastic optimization","authors":"Tito Homem-de-Mello ,&nbsp;Güzin Bayraksan","doi":"10.1016/j.sorms.2014.05.001","DOIUrl":"10.1016/j.sorms.2014.05.001","url":null,"abstract":"<div><p><span>This paper surveys the use of Monte Carlo sampling-based methods for stochastic optimization problems. Such methods are required when—as it often happens in practice—the model involves quantities such as expectations and probabilities that cannot be evaluated exactly. While estimation procedures via sampling are well studied in </span>statistics<span>, the use of such methods in an optimization context creates new challenges such as ensuring convergence of optimal solutions and optimal values, testing optimality conditions, choosing appropriate sample sizes to balance the effort between optimization and estimation, and many other issues. Much work has been done in the literature to address these questions. The purpose of this paper is to give an overview of some of that work, with the goal of introducing the topic to students and researchers and providing a practical guide for someone who needs to solve a stochastic optimization problem with sampling.</span></p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"19 1","pages":"Pages 56-85"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2014.05.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86796150","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":"System-oriented inventory models for spare parts","authors":"R.J.I. Basten ,&nbsp;G.J. van Houtum","doi":"10.1016/j.sorms.2014.05.002","DOIUrl":"10.1016/j.sorms.2014.05.002","url":null,"abstract":"<div><p>Stocks of spare parts, located at appropriate locations, can prevent long downtimes of technical systems that are used in the primary processes of their users. Since such downtimes are typically very expensive, generally system-oriented service measures are used in spare parts inventory control. Examples of such measures are system availability and the expected number of backorders over all spare parts. This is one of the key characteristics that distinguishes such inventory control from other fields of inventory control. In this paper, we survey models for spare parts inventory control under system-oriented service constraints. We link those models to two archetypical types of spare parts networks: networks of users who maintain their own systems, for instance in the military world, and networks of original equipment manufacturers who service the installed base of products that they have sold. We describe the characteristics of these networks and refer back to them throughout the survey. Our aim is to bring structure into the large body of related literature and to refer to the most important papers. We discuss both the single location and multi-echelon models. We further focus on the use of lateral and emergency shipments, and we refer to other extensions and the coupling of spare parts inventory control models to related problems, such as repair shop capacity planning. We conclude with a short discussion of application of these models in practice.</p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"19 1","pages":"Pages 34-55"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2014.05.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88304715","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":"A unified classification of two-stage DEA models","authors":"George E. Halkos,&nbsp;Nickolaos G. Tzeremes,&nbsp;Stavros A. Kourtzidis","doi":"10.1016/j.sorms.2013.10.001","DOIUrl":"10.1016/j.sorms.2013.10.001","url":null,"abstract":"<div><p><span><span>Standard Data Envelopment Analysis (DEA) is used to evaluate the efficiency of Decision Making Units (DMUs) and treats its internal structures as a “black box”. The aim of this paper is twofold. The first task is to survey and classify the two-stage DEA models and to present the applications of these models across the literature. The second aim is to point out the significance of these models for the </span>decision maker<span> of a supply chain. We analyze the simple case of these models which is the two-stage models and a few more general models such as network DEA models. Furthermore, we study some variations of these models such as models with only intermediate measures between the first and second stages and models with exogenous inputs in the second stage. We define four categories: </span></span><em>independent, connected, relational</em> and <em>game theoretic</em> two-stage DEA models. We present each category along with its mathematical formulations, main applications and possible connections with other categories. Finally, we present some concluding remarks and a number of policy implications and opportunities for the decision maker.</p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"19 1","pages":"Pages 1-16"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2013.10.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88450644","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":"Approximation in two-stage stochastic integer programming","authors":"Ward Romeijnders ,&nbsp;Leen Stougie ,&nbsp;Maarten H. van der Vlerk","doi":"10.1016/j.sorms.2014.04.001","DOIUrl":"10.1016/j.sorms.2014.04.001","url":null,"abstract":"<div><p>Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solution value. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly remain restricted to convergence to an optimal solution without accompanying implications on the running time of the algorithms for attaining more and more accurate solutions.</p><p>However, over the last thirty years also some studies on performance analysis of approximation algorithms for stochastic programming have appeared. In this direction we find both probabilistic analysis and worst-case analysis.</p><p><span>Recently the complexity of stochastic programming problems has been addressed, indeed confirming that these problems are harder than most deterministic combinatorial optimization problems. Polynomial-time approximation algorithms and their performance guarantees for stochastic linear and </span>integer programming problems have received increasing research attention only very recently.</p><p>Approximation in the traditional stochastic programming sense will not be discussed in this chapter. The reader interested in this issue is referred to surveys on stochastic programming, like the Handbook on Stochastic Programming by Ruszczyński and Shapiro (2003) or the textbooks by Birge and Louveaux (1997), Kall and Wallace (1994), Prékopa (1995), and Shapiro et al. (2009). We concentrate on the studies of approximation algorithms in relation to computational complexity theory.</p><p>With this survey we intend to give a flavor of the type of results existing in the literature on approximation algorithms in two-stage stochastic integer programming rather than a complete overview of the literature on the subject. We do so by exhibiting a representative selection of results, which we present in full detail. While presenting them we do not refer to the literature; these references, together with pointers to other relevant work in this field of research, are given in an extensive notes section at the end of the survey.</p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"19 1","pages":"Pages 17-33"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2014.04.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78321482","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":"EOQ Models with Supply Disruptions","authors":"Z. Atan, L. Snyder","doi":"10.1007/978-1-4614-7639-9_3","DOIUrl":"https://doi.org/10.1007/978-1-4614-7639-9_3","url":null,"abstract":"","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"29 1","pages":"43-55"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74234749","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":"Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions","authors":"A. V. D. Boer","doi":"10.1016/J.SORMS.2015.03.001","DOIUrl":"https://doi.org/10.1016/J.SORMS.2015.03.001","url":null,"abstract":"","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"29 1","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87405719","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":"Decision making with the analytic network process","authors":"T. Saaty, Luis G. Vargas","doi":"10.1007/978-1-4614-7279-7","DOIUrl":"https://doi.org/10.1007/978-1-4614-7279-7","url":null,"abstract":"","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83900651","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":"Practical guidelines for solving difficult linear programs","authors":"Ed Klotz ,&nbsp;Alexandra M. Newman","doi":"10.1016/j.sorms.2012.11.001","DOIUrl":"10.1016/j.sorms.2012.11.001","url":null,"abstract":"<div><p>The advances in state-of-the-art hardware and software have enabled the inexpensive, efficient solution of many large-scale linear programs previously considered intractable. However, a significant number of large linear programs can require hours, or even days, of run time and are not guaranteed to yield an optimal (or near-optimal) solution. In this paper, we present suggestions for diagnosing and removing performance problems in state-of-the-art linear programming solvers, and guidelines for careful model formulation, both of which can vastly improve performance.</p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"18 1","pages":"Pages 1-17"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2012.11.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81502234","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":"Practical guidelines for solving difficult mixed integer linear programs","authors":"Ed Klotz ,&nbsp;Alexandra M. Newman","doi":"10.1016/j.sorms.2012.12.001","DOIUrl":"10.1016/j.sorms.2012.12.001","url":null,"abstract":"<div><p>Even with state-of-the-art hardware and software, mixed integer programs can require hours, or even days, of run time and are not guaranteed to yield an optimal (or near-optimal, or any!) solution. In this paper, we present suggestions for appropriate use of state-of-the-art optimizers and guidelines for careful formulation, both of which can vastly improve performance.</p></div>","PeriodicalId":101192,"journal":{"name":"Surveys in Operations Research and Management Science","volume":"18 1","pages":"Pages 18-32"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sorms.2012.12.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"106567742","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}