Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

On the placement of security-related Virtualised Network Functions over data center networks

Middleboxes are typically hardware-accelerated appliances such as firewalls, proxies, WAN optimizers, and NATs that play an important role in service provisioning over today's data centers. Reports show that the number of middleboxes is on par with the number of routers, and consequently represent a significant commitment from an operator's capital and operational expenditure budgets. Over the past few years, software middleboxes known as Virtual Network Functions (VNFs) are replacing the hardware appliances to reduce cost, improve the flexibility of deployment, and allow for extending network functionality in short timescales. This dissertation aims at identifying the unique characteristics of security modules implementation as VNFs in virtualised environments. We focus on the placement of the security VNFs to minimise resource usage without violating the security imposed constraints as a challenge faced by operators today who want to increase the usable capacity of their infrastructures. The work presented here, focuses on the multi-tenant environment where customised security services are provided to tenants. The services are implemented as a software module deployed as a VNF collocated with network switches to reduce overhead. Furthermore, the thesis presents a formalisation for the resource-aware placement of security VNFs and provides a constraint programming solution along with examining heuristic, meta-heuristic and near-optimal/subset-sum solutions to solve larger size problems in reduced time. The results of this work identify the unique and vital constraints of the placement of security functions. They demonstrate that the granularity of the traffic required by the security functions imposes traffic constraints that increase the resource overhead of the deployment. The work identifies the north-south traffic in data centers as the traffic designed for processing for security functions rather than east-west traffic. It asserts that the non-sharing strategy of security modules will reduce the complexity in case of the multi-tenant environment. Furthermore, the work adopts on-path deployment of security VNF traffic strategy, which is shown to reduce resources overhead compared to previous approaches

Generating cutting planes through inequality merging on multiple variables in knapsack problems

Master of ScienceIndustrial & Manufacturing Systems EngineeringTodd W. EastonInteger programming is a field of mathematical optimization that has applications across a wide variety of industries and fields including business, government, health care and military. A commonly studied integer program is the knapsack problem, which has applications including project and portfolio selection, production planning, inventory problems, profit maximization applications and machine scheduling. Integer programs are computationally difficult and currently require exponential effort to solve. Adding cutting planes is a way of reducing the solving time of integer programs. These cutting planes eliminate linear relaxation space. The theoretically strongest cutting planes are facet defining inequalities. This thesis introduces a new class of cutting planes called multiple variable merging cover inequalities (MVMCI). The thesis presents the multiple variable merging cover algorithm (MVMCA), which runs in linear time and produces a valid MVMCI. Under certain conditions, an MVMCI can be shown to be a facet defining inequality. An example demonstrates these advancements and is used to prove that MVMCIs could not be identified by any existing techniques. A small computational study compares the computational impact of including MVMCIs. The study shows that finding an MVMCI is extremely fast, less than .01 seconds. Furthermore, including an MVMCI improved the solution time required by CPLEX, a commercial integer programming solver, by 6.3% on average

Unpacking the Role of Artificial Intelligence for a Multimodal Service System Design

Since requirements of service demands are becoming increasingly complex and diversified, one of the success factors of a multimodal service system is its capability to design a specific service instance satisfying a specific set of requirements. This capability is further highlighted in Ad Hoc Multimodal Service Systems (AHMSSs), where service instances rarely follow a standard form of service delivery and exist only for a limited time. However, due to the increasing scale and frequency of services in many business and public sectors, meeting the desired level of capability has become troublesome. A well-designed Artificial Intelligence (AI) approach can be a solution to the difficulty by addressing the underlying complexity and uncertainty of the AHMSS design process. To conceptualize and foster AI applications to an AHMSS, this study identifies key decision-making problems in the AHMSS design process and discusses the role of AI in the process. The results will form the basis for AI development and implementation for an AHMSS and relevant service systems

Approximate Dynamic Programming for Military Resource Allocation

This research considers the optimal allocation of weapons to a collection of targets with the objective of maximizing the value of destroyed targets. The weapon-target assignment (WTA) problem is a classic non-linear combinatorial optimization problem with an extensive history in operations research literature. The dynamic weapon target assignment (DWTA) problem aims to assign weapons optimally over time using the information gained to improve the outcome of their engagements. This research investigates various formulations of the DWTA problem and develops algorithms for their solution. Finally, an embedded optimization problem is introduced in which optimization of the multi-stage DWTA is used to determine optimal weaponeering of aircraft. Approximate dynamic programming is applied to the various formulations of the WTA problem. Like many in the field of combinatorial optimization, the DWTA problem suffers from the curses of dimensionality and exact solutions are often computationally intractability. As such, approximations are developed which exploit the special structure of the problem and allow for efficient convergence to high-quality local optima. Finally, a genetic algorithm solution framework is developed to test the embedded optimization problem for aircraft weaponeering