Operations Research in Healthcare: a survey

Optimisation problems in Healthcare have received considerable attention for more than three decades. More recently, however, with decreasing birth rates in nearly all of the developed countries and increasing average longevity globally, optimisation issues in Healthcare have become noticeably important and attract keen interest from the Operations Research community. Over the years, attention has gradually expanded from resource allocation and strategic planning to include operational issues such as resource scheduling and treatment planning. This paper surveys several applications of Operations Research in the domain of Healthcare. In particular, the paper reviews key contributions addressing contemporary optimisation issues in this area. It highlights current research activities, focusing on a variety of optimisation problems as well as solution techniques used for solving the optimisation problems.     

Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

Maize (Zea mays L.) is the second most commonly grown crop worldwide and number one staple food in Africa where it accounts for more than 50% of the energy requirements. However, despite its widespread cultivation and the significance of maize information in Africa, maize crop maps and yield forecasts are hardly available. Yet, systematic area, spatial distribution, and maize yield estimates are important in understanding and addressing food security in Africa. Objective monitoring of maize yield statisics in a systematic way is possible with remotely sensed data. However, absence of maize yield forecasts using remote sensing in Africa has been attributed to the cost of acquiring satellite imagery and the heterogeneity of agricultural landscapes. The recent advances in sensors technology and availability of free high-resolution (spatial and temporal) multispectral satellite images afford an opportunity to forecast maize yield as well as mapping its spatial distribution in near real-time basis. This review gives an overview of maize yield estimation using remotely sensed information and its potential application in a fragmented and highly granular agricultural landscapes in Africa, including inherent challenges and research needs. The review was motivated by challenges faced by researchers and national agricultural statistical services agents when forecasting maize yield using conventional ground-based survey methods. These problems include, but are not limited to, restricted accuracy, and cost and time spent resulting in missed opportunities in food security early warning systems and proper developmental interventions. We conclude that by picking multispectral sensors with high spatial, temporal, and spectral resolution, as well as appropriate classification techniques and accurate ground-truthing data, remote sensing can be a practical option for estimating maize grain yield and its spatio-temporal dynamics in heterogeneous African agricultural landscapes for designing appropriate developmental interventions and technological out scaling.     

Operations research and computers in law enforcement: Some case studies of american police experience

The application of Operations Research and Systems Analysis to Law Enforcement was encouraged by the President's Crime Commission in 1967. Since then there have been a number of projects. This paper describes operations research activities in Chicago and Washington, D.C. which are illustrative of the kinds of opportunities that exist in Law Enforcement.Applicable objectives and measures of effectiveness are discussed along with some of the results of a systems analysis of the Chicago Police Department that yielded in 1969 a program budget; insight on the police response function (including forecasting demands for service) and on the prevention patrol function of the police, and tactical studies of response and prevention patrol. The problem of total manpower resource allocation is discussed from various police-community standpoints.Although there are some pitfalls for the OR analyst in Law Enforcement applications, the Law Enforcement problems are sufficiently amenable to OR and Computer sciences techniques that a bright prognosis for the future of OR is made.     

Remote sensing of vegetation dynamics in West Africa

Vegetation dynamics and the lives of millions of people in West Africa are closely interlinked with each other. The high annual variability of the phenological cycle considerably affects the agricultural population with late rainfalls and droughts, often resulting in serious food crises. On the other hand, the rapidly growing population has a great need for space due to expanding cities and a low agricultural efficiency. This situation, together with a changing climate, has had a strong impact on vegetation dynamics in West Africa and will play a major role in the future. The dynamic nature of vegetation in the region has attracted a lot of remote-sensing-based research in the past 30 years and has lead to heated discussions. This review article gives a comprehensive overview of the studies on remotely sensed vegetation dynamics in West Africa. After an introduction to the specific situation for vegetation dynamics in West Africa, the applied sensors and their suitability for the region are outlined. Research on the assessment of different plant parameters, on phenological metrics as well as on the monitoring of agricultural areas is outlined and discussed. Furthermore, a major part of this review is dedicated to the analyses undertaken to assess vegetation trends in West Africa over the past 30 years and their potential human and climatic causes. Finally, identified research gaps and challenges for future studies are discussed.     

Constrained two-dimensional cutting stock problems a best-first branch-and-bound algorithm

In this paper, we develop a new version of the algorithm proposed in Hifi (Computers and Operations Research 24/8 (1997) 727–736) for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. Performance of branch-and-bound procedure depends highly on particular implementation of that algorithm. Programs of this kind are often accelerated drastically by employing sophisticated techniques. In the new version of the algorithm, we start by enhancing the initial lower bound to limit initially the space search. This initial lower bound has already been used in Fayard et al. 1998 (Journal of the Operational Research Society, 49, 1270–1277), as a heuristic for solving the constrained and unconstrained cutting stock problems. Also, we try to improve the upper bound at each internal node of the developed tree, by applying some simple and effcient combinations. Finally, we introduce some new symmetric-strategies used for neglecting some unnecessary duplicate patterns . The performance of our algorithm is evaluated on some problem instances of the literature and other hard-randomly generated problem instances.     

Towards Fast Vickrey Pricing using Constraint Programming

Ensuring truthfulness amongst self-interested agents bidding against one another in an auction can be computationally expensive when prices are determined using the Vickrey–Clarke–Groves (VCG) mechanism. This mechanism guarantees that each agent's dominant strategy is to tell the truth, but it requires solving n+ 1 optimization problems when the overall optimal solution involves n agents. This paper first examines a case-study example demonstrating how Operations Research techniques can be used to compute Vickrey prices efficiently. In particular, the case-study focuses on the Assignment Problem. We show how, in this case, Vickrey prices can be computed in the same asymptotic time complexity as that of the original optimization problem. This case-study can be seen as serving a pedagogical role in the paper illustrating how Operations Research techniques can be used for fast Vickrey pricing. We then propose a Constraint Programming approach that can be used in a more general context, where nothing is assumed about the nature of the constraints that must be satisfied or the structure of the underlying problem. In particular, we demonstrate how nogood learning can be used to improve the efficiency of constraint-based Vickrey pricing in combinatorial auctions.     

On security measures in distributed computer systems

Problems of data security are becoming increasingly acute. This study of these basic problems has been carried out in cooperation between scientists at the Department of Electrical Engineering at Linköping Institute of Technology and FOA 3 (Försvarets Forskningsanstalt Planeringsbyrån — Research Institute of National Defense, Operations Research Center).The situation considered is an office with individual work stations having desktop computers. These communicate among themselves and share a host computer of higher capacity including special purpose input/output equipment.Among questions discussed are:

• * problems related to the protection of data bases

• * ease of selectively designating addressees and authorisation to access data and messages; key distribution and protection

• * general requirements for equipment needed in such a local net environment.

No attempt has been made to predict when such distributed office computer systems will become commonly available in full scale. It does, however, seem clear that they are at least technically feasible already.     

人工智能对抗环境下的模型鲁棒性研究综述

近年来人工智能研究与应用发展迅速,机器学习模型大量应用在现实的场景中,人工智能模型的安全鲁棒性分析与评估问题已经开始引起人们的关注。最近的研究发现,对于没有经过防御设计的模型,攻击者通过给样本添加微小的人眼不可察觉的扰动,可以轻易的使模型产生误判,从而导致严重的安全性问题,这就是人工智能模型的对抗样本。对抗样本已经成为人工智能安全研究的一个热门领域,各种新的攻击方法,防御方法和模型鲁棒性研究层出不穷,然而至今尚未有一个完备统一的模型鲁棒性的度量评价标准,所以本文总结了现阶段在人工智能对抗环境下的模型鲁棒性研究,论述了当前主流的模型鲁棒性的研究方法,从一个比较全面的视角探讨了对抗环境下的模型鲁棒性这一研究方向的进展,并且提出了一些未来的研究方向。     

Operations Research and public policy for Africa: harnessing the revolution in management science instruction

Operations Research (OR) has made major contributions in the developed world to public policy domains that are of great relevance to Africa. Inasmuch as OR has failed to live up to its potential for addressing such issues in Africa, a principal barrier may have been distance between OR analysts and decision makers. However, the revolution in management science instruction and potential to train end‐user modelers has democratized OR. This makes training for policy makers and managers in the public and non‐profit sectors in Africa both feasible and highly beneficial. Existing management science courses for public and non‐profit leaders, such as those taught at Carnegie Mellon's Heinz School, could be adapted to fit the needs of educators and policy makers in Africa and disseminated via a “train the trainers” approach. A plan is sketched whereby 800,000 end‐user modelers might be trained in Africa (one for every 1000 people) at an annual cost of about $5 million/year. Such budgets are well within the range of investments in human capital formation currently being made in Africa.     

Solving Various Weighted Matching Problems with Constraints

This paper studies the resolution of (augmented) weighted matching problems within a constraint programming (CP) framework. The first contribution of the paper is a set of techniques that improves substantially the performance of branch-and-bound algorithms based on constraint propagation and the second contribution is the introduction of weighted matching as a global constraint ( WeightedMatching), that can be propagated using specialized incremental algorithms from Operations Research. We first compare programming techniques that use constraint propagation with specialized algorithms from Operations Research, such as the Busaker and Gowen flow algorithm or the Hungarian method. Although CP is shown not to be competitive with specialized polynomial algorithms for pure matching problems, the situation is different as soon as the problems are modified with additional constraints. Using the previously mentioned set of techniques, a simpler branch-and-bound algorithm based on constraint propagation can outperform a complex specialized algorithm. These techniques have been applied with success to the Traveling Salesman Problems [5], which can be seen as an augmented matching problem. We also show that an incremental version of the Hungarian method can be used to propagate a WeightedMatching constraint. This is an extension to the weighted case of the work of Régin [19], which we show to bring significant improvements on a timetabling example.     

A Two-Stage Decomposition of High School Timetabling applied to cases in Denmark

Integer Programming (IP) has been used to model educational timetabling problems since the very early days of Operations Research. It is well recognized that these IP models in general are hard to solve, and this area of research is dominated by heuristic solution approaches. In this paper a Two-Stage Decomposition of an IP model for a practical case of high school timetabling is shown. This particular timetabling problem consists of assigning lectures to both a timeslot and a classroom, which is modeled using a very large amount of binary variables. The decomposition splits this model into two separate problems (Stage I and Stage II) with far less variables. These two separate problems are solved in sequence, such that the solution for the Stage I model is given as input to the Stage II model, implying that irreversible decisions are made in Stage I. However, the objective of the Stage II model is partly incorporated in the Stage I model by exploiting that Stage II can be seen as a minimum weight maximum matching problem in a bipartite graph. This theoretically strengthens the decomposition in terms of global optimality. The approach relies on Hall's theorem for the existence of matchings in bipartite graphs, which in its basic form yields an exponential amount of constraints in the Stage I model. However, it is shown that only a small subset of these constraints is needed, making the decomposition tractable in practice for IP solvers. To evaluate the decomposition, 100 real-life problem instances from the database of the high school ERP system Lectio are used. Computational results show that the decomposition performs significantly better than solving the original IP, in terms of both found solutions and bounds.     

远程办公系统安全综述

受新型冠状病毒肺炎的影响, 远程办公这种新型办公方式在短时间内迅速发展并被社会广泛应用, 由此引发的远程办公系统的安全问题显得越发急迫和突出。目前, 远程办公系统安全的相关研究仍处于起步阶段, 其研究结果并未不足以完全解决远程办公系统发展中的安全问题。为使研究人员系统化地了解目前的研究进展, 本文首次归纳总结了远程办公系统的安全问题,并撰写了本综述。本文首先回顾了远程办公系统的发展历程, 指出了远程办公系统在不同应用场景中特有的安全需求和问题,然后根据远程办公系统的技术架构将其分为虚拟专用网络、远程桌面控制、团队协作平台三种类型。在调研了近 5 年 EI 数据库、 Web of Science 核心数据库和 CCF 推荐网络与信息安全国际学术会议中发表的与远程办公安全相关论文以及其他相关的高水平研究工作的基础上, 本文对以上三类远程办公系统中存在的安全问题进行了系统性的分析和总结, 尤其是重点分析了团队协作平台这种新型办公方式的安全问题。根据团队协作平台的架构和功能以及攻击者常用的攻击方式将团队协作平台的安全风险分为 5 类: 第三方小程序安全、通信协议安全、客户端安全、云服务端安全、侧信道分析。最后进一步指出了远程办公系统安全研究所面临的挑战和机遇, 为远程办公系统安全未来的研究指出了方向。     

A framework for designing the enterprise-wide e-commerce portal for evolving organizations

The number of organizations offering e-commerce solutions is growing exponentially each year. Without a doubt, e-commerce will no longer be a choice for organizations rather it will be a competitive necessity to ensure business prosperity. Integral to the success of e-commerce is having good e-commerce software that can enable organizations to offer online products and services as well as integrate their business processes and supply chains within and with their collaborators or partners in a perfect, seamless manner. One of the main obstacles for adoption of e-commerce faced by many organizations; however, has been the lack of such proper integrated e-commerce software. There have been few ready-made software solutions offered by vendors, which can be customized for organizations’ business models and processes, and these solutions are based on “piece meal” approaches and thus lack much of the enterprise capabilities organizations need to adopt. In this paper, we suggest a framework for developing an enterprise-wide integrated e-commerce portal for evolving organizations. Such a framework will help any organization to design a distributed, extensible, cross-platform, collaborative and integrated e-commerce portal. Sushil K. Sharma is currently an Associate Professor in the Department of Information Systems and Operations Management at Ball State University, Muncie, Indiana. Prior to joining the faculty at Ball State, Dr. Sharma held the Associate Professor position at the Indian Institute of Management in Lucknow (India) and Visiting Research Associate Professor at the Department of Management Science at the University of Waterloo, Canada. He is of two text books (Programming in C, and Understanding Unix), and Co-editor of four edited books. Dr. Sharma has published more than 100 refereed research papers in many peer-reviewed national and international journals and conference proceedings. His contributions have appeared in journals such as; International Journal of Information Management, International Journal of Healthcare Technology and Management (IJHTM), Electronic Government Journal, Journal of Electronic Commerce in Organizations (JECO), Journal of Global Information Technology Management (JGITM), The Journal of Computer Information Systems (CIS), Journal of Logistics Information Management (JLIM), and International Journal of Management. He currently serves as an Associate Editor for International Journal of Cases on Electronic Commerce (IJCEC) and is on Editorial board for the International Journal of Electronic Finance (IJEF) and the Journal of Electronic Commerce in Organizations (JECO). In the past, he has also been a guest editor for special issue of Global Journal of Flexible Systems Management and Journal of Global Information Technology Management (JGITM). Dr. Sharma’s primary teaching and research interests are in e-commerce, computer and network security, ERP Systems, database management systems, and knowledge management. Jatinder (Jeet) N. D. Gupta is currently Eminent Scholar of Management of Technology, Professor of Management Information Systems, Industrial and Systems Engineering and Engineering Management at the University of Alabama in Huntsville, Huntsville, Alabama. Most recently, he was Professor of Management, Information and Communication Sciences, and Industry and Technology at Ball State University, Muncie, Indiana. He holds a PhD in Industrial Engineering (with specialization in Production Management and Information Systems) from Texas Tech University. Co-author of a textbook in Operations Research, Dr. Gupta serves on the editorial boards of several national and international journals. Recipient of the Outstanding Faculty and Outstanding Researcher awards from Ball State University, he has published numerous papers in such journals as Journal of Management Information Systems, International Journal of Information Management, Operations Research, INFORMS Journal of Computing, Annals of Operations Research, and Mathematics of Operations Research. More recently, he served as a co-editor of several special issues including the Neural Networks in Business of Computers and Operations Research and books that included Decision Making Support Systems: Achievements and Challenges for the New Decade and Creating Knowledge-based Healthcare Organizations published by Idea Group Publishing. He is also the coeditor of the book: Managing E-Business published by Heidelberg Press, Heidelberg, Australia. His current research interests include e-Commerce, Supply Chain Management, Information and Decision Technologies, Scheduling, Planning and Control, Organizational Learning and Effectiveness, Systems Education, Knowledge Management, Information Security, and Enterprise Integration. Dr. Gupta has held elected and appointed positions in several academic and professional societies including the Association for Information Systems, Production and Operations Management Society (POMS), the Decision Sciences Institute (DSI), and the Information Resources Management Association (IRMA). Nilmini Wickramasinghe, PhD, MBA, GradDipMgtSt, BSc. Amus.A (piano) Amus.A(violin): Currently, Dr Wickramasinghe is an associate professor and the associate director of the Center for the Management of Medical Technologies at Stuart Graduate School of Business, Illinois Institute of Technology. Her teaching interests cover the areas of knowledge management as well as e-commerce and m-commerce, IT for competitive advantage, organizational impacts of technology and healthcare issues. In addition, Dr Wickramasinghe teaches and presents regularly in many universities in Europe and Australiasia. She is currently carrying out research and is well published having written numerous book chapters, refereed journal articles and some books in the areas of management of technology, in the field of healthcare as well as focusing on IS issues especially as they relate to knowledge work and e-business. Dr Wickramasinghe is honored to be able to represent the United States of America for the Health Care Technology Management (HCTM) Association (URL http://www.hctm.net/events/2005/conference_2005.html).     

Operations research in the natural resource industry

Operations research is becoming increasingly prevalent in the natural resource sector, specifically in agriculture, fisheries, forestry and mining. While there are similar research questions in these areas, e.g., how to harvest and/or extract the resources and how to account for environmental impacts, there are also differences, e.g., the length of time associated with a growth and harvesting or extraction cycle, and whether or not the resource is renewable. Research in all four areas is at different levels of advancement in terms of the methodology currently developed and the acceptance of implementable plans and policies. In this paper, we review the most recent and seminal work in all four areas, considering modeling, algorithmic developments and application.     

Optimal methods for resource allocation and scheduling: a cross-disciplinary survey

Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a far-from-trivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and OR techniques, often reporting impressive speed-ups compared to pure CP and OR methods. Motivated by the success of hybrid algorithms on resource assignment and scheduling, we provide a cross-disciplinary survey on such problems, including CP, OR and hybrid approaches. The main effort is to identify key modeling and solution techniques: they may then be applied in the construction of new hybrid algorithms, or they may provide ideas for novel filtering methods (possibly based on decomposition, or on alternative representations of the domain store). In detail, we take a constraint-based perspective and, according to the equation CP = model + propagation + search, we give an overview of state-of-art models, propagation/bounding techniques and search strategies.     

An in-depth analysis of information markets with aggregate uncertainty

The novel idea of setting up Internet-based virtual markets, information markets, to aggregate dispersed information and predict outcomes of uncertain future events has empirically found its way into many domains. But the theoretical examination of information markets has lagged relative to their implementation and use. This paper proposes a simple theoretical model of information markets to understand their information dynamics. We investigate and provide initial answers to a series of research questions that are important to understanding how information markets work, which are: (1) Does an information market converge to a consensus equilibrium? (2) If yes, how fast is the convergence process? (3) What is the best possible equilibrium of an information market? and (4) Is an information market guaranteed to converge to the best possible equilibrium? The authors acknowledge the support of the eBusiness Research Center at the Pennsylvania State University. Yiling Chen is a postdoctoral research scientist at Yahoo! Research, New York. She received her Bachelor of Economics degree in Commodity Science from Renmin University of China, in 1996, and her Master of Economics degree in Finance from Tsinghua University, China, in 1999. She worked for PriceWaterhouse Coopers China as a professional auditor from August 1999 to June 2000. From August 2000 to July 2001, she attended Iowa State University, Ames, IA, as a Ph.D. student in economics. She obtained her Ph.D. in Information Sciences and Technology from the Pennsylvania State University, University Park, PA, in 2005. Her research interests lie on the boarder of computer science, economics, and business, including information markets, auction theory, and machine learning. Tracy Mullen is an assistant professor of information sciences and technology at the Pennsylvania State University, University Park, PA. She has previously worked at Lockheed Martin, Bellcore, and NEC Research. She received her PhD in Computer Science from University of Michigan. Her research interests include information markets, multiagent systems, ecommerce, market-based resource allocation for sensor management, and supply chain simulations using intelligent agents. Her research papers have been published in Decision Support Systems, Electronic Commerce Research, IEEE Computer, ACM Transactions on Internet Technology, Mathematics and Computers in Simulation, and Operating Systems Review, among others. Chao-Hsien Chu is an associate professor of information sciences and technology and the executive director of the Center for Information Assurance at the Pennsylvania State University, University Park, PA. He was previously on the faculty at Iowa State University, Iowa and Baruch College, New York and a visiting professor at University of Tsukuba (Japan) and Hebei University of Technology (China). He is currently on leaves to the Singapore Management University (Singapore) (2005–2006). Dr. Chu received a Ph.D. in Business Administration from Penn State. His current research interests are in communication networks design, information assurance and security (especially in wireless security, intrusion detection, and cyber forensics), intelligent technologies (fuzzy logic, neural network, genetic algorithms, etc.) for data mining (e.g., bioinformatics, privacy preserving) and supply chains integration. His research papers have been published in Decision Sciences, IEEE Transactions on Evolutionary Computation, IIE Transactions, Decision Support Systems, European Journal of Operational Research, Electronic Commerce Research, Expert Systems with Applications, International Journal of Mobile Communications, Journal of Operations Management, International Journal of Production Research, among others. He is currently on the editorial review board for a number of journals.     

Optimization-Oriented Global Constraints

In this paper, we propose a way of exploiting Operations Research techniques within global constraints for cost-based domain filtering. In Constraint Programming, constraint propagation is aimed at removing from variable domains combinations of values which are proven infeasible. Pruning derives from feasibility reasoning. When coping with optimization problems, pruning can be performed also on the basis of costs, i.e., optimality reasoning. Cost-based filtering removes combination of values which are proven sub-optimal. For this purpose, we encapsulate in global constraints optimization components representing suitable relaxations of the constraint itself. These components embed efficient Operations Research algorithms computing the optimal solution of the relaxed problem and a gradient function representing the estimated cost of each variable-value assignment. We exploit these pieces of information for pruning and for guiding the search. We have applied these techniques to a couple of ILOG Solver global constraints (a constraint of difference and a path constraint) and tested the approach on a variety of combinatorial optimization problems such as Timetabling, Travelling Salesman Problems and Scheduling Problems with sequence dependent setup times. Comparisons with pure Constraint Programming approaches and related literature clearly show the benefits of the proposed approach.     

Comparison of OR and AI methods in discrete manufacturing using fuzzy logic

Since 1950s the techniques of Operations Research (OR) and Optimization have been utilized to increase the efficiency of the production systems. With the widespread use of computers, it has even become easier to deal with industrial problems. However the complexity of the problems still reveals the difficulty in providing solutions. The use of artificial intelligence (AI) seems to attract the attention of the researcher to overcome to the difficulties. This has already been realized with several successful applications. In this study, the use of AI and OR techniques is compared using fuzzy logic. The progress of manufacturing systems, characteristics of production processes, system managements and system behavior are taken into account. The study is focussed on only discrete manufacturing.     

A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows

The Vehicle Routing Problem with Time Windows (VRPTW) is a well-known and complex combinatorial problem, which has received considerable attention in recent years. This problem has been addressed using many different techniques including both exact and heuristic methods. The VRPTW benchmark problems of Solomon [Algorithms for the vehicle routing and scheduling problems with time window constraints, Operations Research 1987; 35(2): 254–65] have been most commonly chosen to evaluate and compare all algorithms. Results from exact methods have been improved considerably because of parallel implementations and modern branch-and-cut techniques. However, 24 out of the 56 high order instances from Solomon's original test set still remain unsolved. Additionally, in many cases a prohibitive time is needed to find the exact solution. Many of the heuristic methods developed have proved to be efficient in identifying good solutions in reasonable amounts of time. Unfortunately, whilst the research efforts based on exact methods have been focused on the total travel distance, the focus of almost all heuristic attempts has been on the number of vehicles. Consequently, it is more difficult to compare and take advantage of the strong points from each approach. This paper proposes a robust heuristic approach for the VRPTW using travel distance as the main objective through an efficient genetic algorithm and a set partitioning formulation. The tests were produced using real numbers and truncated data type, allowing a direct comparison of its results against previously published heuristic and exact methods. Furthermore, computational results show that the proposed heuristic approach outperforms all previously known and published heuristic methods in terms of the minimal travel distance.     

Hub location problems: A review of models,classification, solution techniques,and applications

Hub location problem (HLP) is a relatively new extension of classical facility location problems. Hubs are facilities that work as consolidation, connecting, and switching points for flows between stipulated origins and destinations. While there are few review papers on hub location problems, the most recent one (Alumur and Kara, 2008. Network hub location problems: The state of the art. European Journal of Operational Research, 190, 1–21) considers solely studies on network-type hub location models prior to early 2007. Therefore, this paper focuses on reviewing the most recent advances in HLP from 2007 up to now. In this paper, a review of all variants of HLPs (i.e., network, continuous, and discrete HLPs) is provided. In particular, mathematical models, solution methods, main specifications, and applications of HLPs are discussed. Furthermore, some case studies illustrating real-world applications of HLPs are briefly introduced. At the end, future research directions and trends will be presented.