Operational Research Practitioners Research Articles

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2,000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

Statistical analyses of solution methods for the multiple-choice knapsack problem with setups: Implications for OR practitioners

An interesting extension of the classic Knapsack Problem (KP) is the Multiple-Choice Knapsack Problem with Setups (MCKS) which is focused on solving practical applications that involve both multiple periods and setups. Sophisticated solution methods for the MCKS that are presented in the operations research (OR) literature are not readily available for use by OR practitioners. Using MCKS test instances that appear in the literature, we demonstrate that the general-purpose integer programming software Gurobi sometimes used in an iterative manner can efficiently solve these MCKS instances using all default parameter values on a standard PC. It is shown both empirically and statistically that these Gurobi solutions are competitive with solution approaches from the literature. Hence, our approach using Gurobi is both easy for the OR practitioner to use and gives results competitive with the best specialized MCKS solution methods in the literature without the need to generate algorithm-specific code. Furthermore, this paper presents significant concerns regarding the solutions stated in the literature by the approximate solution method that reports the best results on 120 MCKS test instances. Specifically, 26% of this method’s solutions violate Gurobi upper bounds and an additional 33% of its solutions, on average, exceed the known guaranteed optimums by a value of 12,510.

A practical approach for dealing with hard knapsack problems using general‐purpose integer programming software

AbstractSeveral articles dealing with the generation of hard 0‐1 knapsack problems (KPs) have given rise to 13,940 KP instances being made available for empirical testing. These KPs are typically solved using algorithms specifically designed to solve hard KPs. The goal of this paper is to demonstrate that two general‐purpose integer programming software packages using default parameter settings and a standard PC can successfully be used to generate optimal or guaranteed near‐optimal solutions for these 13,940 KP instances. Over 97% of the 13,940 instances have solutions generated that are guaranteed to be within 0.01% of optimum in less than 1 minute and a practical strategy for dealing with the other 3% is presented. Additionally, 3240 of the most difficult KPs were generated based on 6 parameters. Statistical analyses for predicting which values for these six parameters result in difficult‐to‐solve KPs based on the software used will be discussed. These results will be of particular interest to operations research practitioners who need to efficiently solve hard KPs related to real‐world applications.

Combining optimisation and simulation using logic-based Benders decomposition

Operations Research practitioners often want to model complicated functions that are difficult to encode in their underlying optimisation framework. A common approach is to solve an approximate model, and then use a simulation to evaluate the true objective value of one or more solutions. We propose a new approach to integrating simulation into the optimisation model itself. The idea is to run the simulation at each incumbent solution to a master problem. The simulation results are then used to guide the trajectory of the optimisation model itself using logic-based Benders cuts. We test the approach on a class of stochastic resource allocation problems with monotonic performance measures. We derive strong novel Benders cuts that are provably valid for all problems of the given form. We consider two concrete examples: a nursing home shift scheduling problem, and an airport check in counter allocation problem. While previous papers on these applications could only approximately solve realistic instances, we are able to solve them exactly within a reasonable amount of time. Moreover, while those papers account for the inherent variance of the problem by including estimates of the underlying random variables as model parameters, we are able to compute sample-average approximations to optimality with up to 100 scenarios.

Multi-Objective Simulation Optimization Integrated With Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution for Pollution Routing Problem

Routing is one of the most important components of logistics and has a vital role in economic growth. Inefficient routing has also contributed to troubling pollutants that have adverse environmental effects worldwide. Over the years, operations research practitioners have therefore drawn considerable attention to the routing problem. In this paper, a pollution routing problem (PRP) with time window constraints and capacitated vehicles is considered to integrate environmental issues in routing for pollution reduction. This work introduces a simulation optimization methodology that includes the realization of important parameters in real-world applications to minimize the total lateness cost and fuel consumption. The proposed method was developed with Simio simulation software and non-dominated sorting genetic algorithm-II (NSGA-II). It evaluates the dynamic system operations over time and performs a comprehensive analysis. In addition, a two-step hierarchical multi-criteria decision-making (MCDM) method is employed to choose the best strategy for PRP. Results of the two-step hierarchical MCDM method demonstrated that the strategy considering the total cost of supply chain is the best alternative for the proposed system. The use of MCDM methods after simulation can be a more robust and effective way to find solutions to such complex problems.

Generating bounded solutions for multi-demand multidimensional knapsack problems: a guide for operations research practitioners

A generalization of the 0-1 knapsack problem that is hard-to-solve both theoretically (NP-hard) and in practice is the multi-demand multidimensional knapsack problem (MDMKP). Solving an MDMKP can be difficult because of its conflicting knapsack and demand constraints. Approximate solution approaches provide no guarantees on solution quality. Recently, with the use of classification trees, MDMKPs were partitioned into three general categories based on their expected performance using the integer programming option of the CPLEX® software package on a standard PC: Category A—relatively easy to solve, Category B—somewhat difficult to solve, and Category C—difficult to solve. However, no solution methods were associated with these categories. The primary contribution of this article is that it demonstrates, customized to each category, how general-purpose integer programming software (CPLEX in this case) can be iteratively used to efficiently generate bounded solutions for MDMKPs. Specifically, the simple sequential increasing tolerance (SSIT) methodology will iteratively use CPLEX with loosening tolerances to efficiently generate these bounded solutions. The real strength of this approach is that the SSIT methodology is customized based on the particular category (A, B, or C) of the MDMKP instance being solved. This methodology is easy for practitioners to use because it requires no time-consuming effort of coding problem specific-algorithms. Statistical analyses will compare the SSIT results to a single-pass execution of CPLEX in terms of execution time and solution quality.

Simple strategies that generate bounded solutions for the multiple‐choice multi‐dimensional knapsack problem: a guide for OR practitioners

AbstractThe multiple‐choice multi‐dimensional knapsack problem (MMKP) is an NP‐hard generalization of the classic 0–1 knapsack problem. The MMKP has a variety of important industrial and business applications. Approximate solution approaches characteristically give no guarantees on solution quality. Exact solution approaches usually generate solutions that are guaranteed to be close to the optimum but typically take excessive computation times—one to several hours have been recorded in the literature. In this article, we use both simple single‐step and multiple step strategies to demonstrate how a commercial software package (Gurobi in this case) can easily be used to generate guaranteed tightly bounded solutions for 293 MMKP instances commonly tested in the literature. These four simple strategies that require no problem‐specific algorithms are shown to perform competitively with the five best published algorithms for the MMKP. Since no problem‐specific coding is required, these simple strategies are easy for operations research practitioners to use.

Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures

Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly computing a posterior quantity of interest—for example, the posterior probability of good selection or the posterior expected opportunity cost—and terminating when this quantity crosses some threshold. Computing these posterior quantities entails nontrivial numerical computation. Thus, rather than exactly check such posterior-based stopping rules, it is common practice to use cheaply computable bounds on the posterior quantity of interest, for example, those based on Bonferroni’s or Slepian’s inequalities. The result is a conservative procedure that samples more simulation replications than are necessary. We explore how the time spent simulating these additional replications might be better spent computing the posterior quantity of interest via numerical integration, with the potential for terminating the procedure sooner. To this end, we develop several methods for improving the computational efficiency of exactly checking the stopping rules. Simulation experiments demonstrate that the proposed methods can, in some instances, significantly reduce a procedure’s total sample size. We further show these savings can be attained with little added computational effort by making effective use of a Monte Carlo estimate of the posterior quantity of interest. Summary of Contribution: The widespread use of commercial simulation software in industry has made ranking-and-selection (R&S) algorithms an accessible simulation-optimization tool for operations research practitioners. This paper addresses computational aspects of R&S procedures delivering finite-time Bayesian statistical guarantees, primarily the decision of when to terminate sampling. Checking stopping rules entails computing or approximating posterior quantities of interest perceived as being computationally intensive to evaluate. The main results of this paper show that these quantities can be efficiently computed via numerical integration and can yield substantial savings in sampling relative to the prevailing approach of using conservative bounds. In addition to enhancing the performance of Bayesian R&S procedures, the results have the potential to advance other research in this space, including the development of more efficient allocation rules.

Structured dialogical design as a problem structuring method illustrated in a Re-invent democracy project

This article proposes the importance of admitting into the repertoire of Problem Structuring Methods for (Community) Operational Research, the methodology called Structured Dialogical Design (SDD). Problem Structuring Methods are described in the literature as facilitating transparent and participative ways of formulating and systemically modelling problems with a view to participants’ co-defining alternative futures. We reflect upon the contribution of SDD as lying in its appreciation of “third phase science” and discuss links to other deliberative processes. We indicate why SDD can be classed as “problem structuring” despite the near absence of publicisation in the Operational Research (OR) literature to date. We discuss distinct contributions that the SDD offers to the OR world and indicate how it strengthens and extends Community OR, contributes to Critical Systems Thinking in OR, and offers new mathematical approaches that the Community OR practitioners may wish to consider using. By way of illustration, we showcase the “European Initiative” as an aspect of a large-scale project across five geographical regions funded by the United Nations Democracy Fund, in conjunction with the Future Worlds centre (2016–2018). It engaged as stakeholders five cohorts of youth pioneers concerned with formulating options for Re-inventing democracy in the digital age.

When to use Integer Programming Software to solve large multi-demand multidimensional knapsack problems: a guide for operations research practitioners

An important generalization of the classic 0-1 knapsack problem is the multi-demand multidimensional knapsack problem (MDMKP). In addition to being theoretically difficult to solve (it is NP-hard), it can be in practice difficult to solve because of its conflicting knapsack and demand constraints. Since there are significant large-scale applications of the MDMKP, approximate solution approaches are commonly used to solve these problems. However, using 1620 MDMKPs discussed in the literature, this article demonstrates which types of large MDMKPs can be solved efficiently by operations research practitioners using general purpose integer programming software on a standard personal computer within 0.1% of optimum. Statistical analyses are used to determine which problem parameters significantly impact solution time. Finally, based on these 1620 MDMKP instances, a classification tree is generated. This tree can be used to guide practitioners in solving MDMKPs that arise in business and industry.

Praxis in healthcare OR: An empirical behavioural OR study

Operational researchers working in academia commonly struggle in attempts to influence practice and decision making in healthcare amid a growing recognition that behaviour is key to effective operational research (OR). To further our understanding of the behavioural factors that operational researchers working in healthcare consider influence their work’s impact, we interviewed 24 OR practitioners working in academia and with experience of working with the UK National Health Service (NHS). The semi-structured interviews were consented, recorded, transcribed, and analysed thematically using a framework approach. Five dominant themes emerged that highlighted: behavioural challenges concerning flexibility, pivoting and the abandonment of projects; the influence of the evolving ambitions, maturity and behaviours of a practitioner’s OR group; the hidden and changing motivations of host healthcare organisations; the reliance of practitioners on intuition and how their praxis is influenced by their agency within their group and its relationships with healthcare organisations; and how attributes of altruism, broader life experience and creative risk-taking influence an individuals’ praxis. In summary, we identified numerous behavioural factors considered important to success that operate within and across individual projects.

Learning to Approximate Industrial Problems by Operations Research Classic Problems

Operations research (OR) practitioners are accustomed to dealing with variants of classic OR problems. Indeed, an industrial problem often looks like a traveling salesman problem, a vehicle routing problem, a shortest path problem, etc., but has an additional constraint or a different objective that prevent the use of the powerful algorithms produced by decades of research on the classic OR problems. This situation can be frustrating, notably when we realize that the classic problem catches most of the structure of the variant. In “Learning to approximate industrial problems by operations research classic problems,” Axel Parmentier introduces a machine learning approach to use the algorithms for the classic OR problems on the variant. The idea is to leverage structured learning to obtain a mapping that approximates an instance of the variant by an instance of the classic problem.

Beyond Hard and Soft OR: operational research from a software engineering perspective

This paper argues that current practice in the Operational Research (OR) discipline needs to tackle management problems from a broader point of view by including a new perspective. While Hard OR is posed as the means to a solution, and Soft OR covers the stakeholder/business perspective, this paper proposes OR Engineering to tackle the model/program perspective. This proposal is substantiated by an empirical study that explored OR history and the rise of “wicked problems” in contrast to Software Engineering (SE) history and the idea of “no silver bullet”. The main findings are five areas for future works, intertwined between Soft OR and OR Engineering: agility, documentation of versioning, technical debt, systems testing and architecture. Overall, although the goal may be ambitious, this paper aims to stimulate reflective thinking and promote a novel and different line of action and research among OR and SE practitioners present and future.

A comprehensive empirical demonstration of the impact of choice constraints on solving generalizations of the 0–1 knapsack problem using the integer programming option of CPLEX®

This paper provides comprehensive empirical and analytical evidence of the impact of choice constraints on two important categories of knapsack problems by using the integer programming option of CPLEX®. Specifically, if choice constraints are added to the 270 multidimensional knapsack problems (MKPs) in Beasley's OR-Library (used widely for comparing MKP heuristics), then the time needed to solve these problems with CPLEX® version 12 is reduced by more than 99.97%. Similar results are demonstrated for the 810 multi-demand multidimensional knapsack problems (MDMKPs) in Beasley's OR-Library. Additionally, using these 270 MKP and 810 MDMKP problem instances, it is shown that even if only some of the variables have choice constraints imposed on them, the CPLEX solution times are drastically reduced. These results provide motivation for operations research practitioners to check whether choice constraints are applicable (even if only on some of the variables) when solving real-world problems involving generalizations of the 0–1 knapsack problem.

Solving the unsupervised graph partitioning problem with genetic algorithms: Classical and new encoding representations

The Graph Partitioning Problem (GPP) is one of the most ubiquitous models that operations research practitioners encounter. Therefore, several methods have been proposed to solve it. Among these methods, Genetic Algorithm (GA) appears to carry very promising performances. However, despite the huge number of papers being published with this approach, only few of them deal with the encoding representation and its role in the reported performances.In this paper, we present classical and new encoding representations for the unsupervised graph partitioning problem. That is, we suppose that the number of partition subsets (clusters) is not known apriori. Next, we conduct an empiric comparison to identify the most promising encodings.

Developing a smart operational research with hybrid practice theories

The growth of technology-rich data-driven decision environments is seen by some as a challenge to the future relevance of Operational Research. Extant research remains unspecific about the distinct contribution that Operational Research can make in environments that are influenced by big data, data science and analytics. This paper explores the possibility that these environments hold the potential for a new integrative Operational Research offering, which we conceptualise as Smart Operational Research. In developing this proposal, we combine automated co-occurrence analysis of a corpus of literature with human-driven data interpretation to identify instantiations of hybrid decision-making. We then bring theory and practice together to outline the Smart Operational Research framework with the overall aim to enhance actionable insight and positive results for Operational Research practitioners.

In defence of Soft OR: Reflections on teaching Soft OR

Soft Operational Research (OR) methods, despite providing considerable benefit to organisations in tackling a range of problems, are not widely utilised by OR practitioners. Reasons include a lack of awareness of both methods and their benefits, perceptions that the methods lack the robustness of their “Hard” OR cousins and challenges in learning the methods. The research presented in this article seeks to better understand these issues and their implications for teaching Soft OR through (a) the development and delivery of a pedagogically informed bespoke training program in the Soft OR method Strategic Options Development and Analysis to analysts working in a Defence environment and (b) reflecting upon the training through analysis of learning diaries and trainee feedback. The findings contrast the challenges faced by post-experience and undergraduate students in learning Soft OR methods, and encompass (i) tailoring programs to match students’ experience, (ii) appreciating the significance of context in the adoption of Soft OR approaches, and (iii) identifying mechanisms for gaining competence and confidence in these approaches. The research aims to improve uptake of Soft OR methods, addresses a literature gap on the challenges of Soft OR from a learner perspective, and extends research in this area into the Defence domain.

Developing Operations Research Practitioners: United States Air Force Academy Operations Research Program

This paper describes the four practical components of the USAFA OR program at the United States Air Force Academy and explains how the program produces good practitioners of OR for the Air Force.

Designing and Sustaining an Award-Winning Analytics Educational Program: The Heinz College of Information Systems and Public Policy at Carnegie Mellon University

In 2016, the Heinz College of Information Systems and Public Policy at Carnegie Mellon University was awarded the INFORMS UPS George D. Smith Prize for excellence in preparing students to be effective practitioners of operations research (OR) and analytics. In this article, we describe the philosophy underlying the college’s approach to analytics education and the specific ways in which students in both the information systems and public policy programs combine coursework and experiential learning to apply a multidisciplinary problem-solving toolkit on their way to becoming men and women for intelligent action.

A survey of computational methods in protein–protein interaction networks

Protein–protein interaction networks are mathematical constructs where every protein is represented as a node, with an edge signaling that two proteins interact. These constructs have enabled a series of graph theoretic computational methods in the analysis of how cell life works. Such methods have found diverse applications from helping create more reliable interaction data, to identifying new protein complexes and predict their functionalities, and investigating the minimum requirements for cell life through protein essentiality. Our goal with this survey is to provide an overview of the research in the area from a network analysis perspective. In this work, we provide a brief introduction to protein–protein interaction networks, followed by the methods that we currently have to obtain such interactions and the databases they can be found at. Then, we proceed to discuss the network properties of protein–protein interaction networks and how they can be exploited to identify protein complexes and functional modules, as well as help classify proteins as essential. We finish this survey with a full bibliography on work in protein–protein interactions that could be of interest to operations research and computational science academicians and practitioners.