Operations Research Problems Research Articles

Empirical risk minimization for big data driven prescriptive analytics: An exploration of two-stage stochastic programs with recourse

In the operations research literature, data driven analyses using big data are receiving more and more interest and attention. However, big data driven operational analyses are still limited in scope, with only unconstrained big data driven problems, e.g. the newsvendor problem, being comprehensively explored. If big data driven analyses are to become a core part of operations research and practice, one must be able to formulate and solve constrained big data driven models. We therefore propose a direct empirical risk minimization (DERM) method for formulating and solving a class of constrained big data driven operation research problems. In big data driven problems, relevant operational prescriptions are dependent on a set of observed features. In this paper, we assume linear feature relationships, which enable us to fundamentally base the DERM method on linear stochastic programming. The assumption of linear feature relationships is also seen in the literature on big data driven newsvendor models. We prove that if a big data driven solution exists, the DERM method will always, for the class of constrained problems we study, arrive at a feasible solution of the initial operational problem. Moreover, we exemplify the DERM method by formulating and solving two specific big data driven operational problems. In the numerical study of the two operational problems, we show, that under linear demand, the DERM method outperforms, with regard to cost, two benchmark methods and a big data driven method from literature.

A partial recurrence Fibonacci link

The purpose of this note is to develop a conjecture for a Fibonacci number generating function in terms of the elements of a second-order two parameter partial recurrence relation which arose in an operations research problem on Poisson distributed lead time in inventory control.

Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

ABSTRACT The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

Regression Machine Learning Models for the Short-Time Prediction of Genetic Algorithm Results in a Vehicle Routing Problem

Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regression machine learning models to predict the objectives of vehicle routing problems that are solved using a genetic algorithm. Previous studies have generally discussed how (1) operations research methods are used independently to generate optimized solutions and (2) machine learning techniques are used independently to predict values from a given dataset. Some studies have discussed the collaborations between operations research and machine learning fields as follows: (1) using machine learning techniques to generate input data for operations research problems, (2) using operations research techniques to optimize the hyper-parameters of machine learning models, and (3) using machine learning to improve the quality of operations research algorithms. This study differs from the types of collaborative studies listed above. This study focuses on the prediction of the objective of the vehicle routing problem directly given the input and output data, without optimizing the problem using operations research algorithms. This study introduces a straightforward framework that captures the input data characteristics for the vehicle routing problem. The proposed framework is applied by generating the input and output data using the genetic algorithm and then using regression machine learning models to predict the obtained objective values. The numerical experiments show that the best models are random forest regression, a generalized linear model with a Poisson distribution, and ridge regression with cross-validation.

Compressed sensing: a discrete optimization approach

We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine Learning which arises in applications such as signal processing, data compression, image reconstruction, and multi-label learning. We introduce an ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} regularized formulation of CS which we reformulate as a mixed integer second order cone program. We derive a second order cone relaxation of this problem and show that under mild conditions on the regularization parameter, the resulting relaxation is equivalent to the well studied basis pursuit denoising problem. We present a semidefinite relaxation that strengthens the second order cone relaxation and develop a custom branch-and-bound algorithm that leverages our second order cone relaxation to solve small-scale instances of CS to certifiable optimality. When compared against solutions produced by three state of the art benchmark methods on synthetic data, our numerical results show that our approach produces solutions that are on average 6.22%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.22\\%$$\\end{document} more sparse. When compared only against the experiment-wise best performing benchmark method on synthetic data, our approach produces solutions that are on average 3.10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.10\\%$$\\end{document} more sparse. On real world ECG data, for a given ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} reconstruction error our approach produces solutions that are on average 9.95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9.95\\%$$\\end{document} more sparse than benchmark methods (3.88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.88\\%$$\\end{document} more sparse if only compared against the best performing benchmark), while for a given sparsity level our approach produces solutions that have on average 10.77%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10.77\\%$$\\end{document} lower reconstruction error than benchmark methods (1.42%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.42\\%$$\\end{document} lower error if only compared against the best performing benchmark). When used as a component of a multi-label classification algorithm, our approach achieves greater classification accuracy than benchmark compressed sensing methods. This improved accuracy comes at the cost of an increase in computation time by several orders of magnitude. Thus, for applications where runtime is not of critical importance, leveraging integer optimization can yield sparser and lower error solutions to CS than existing benchmarks.

New Mean and Median Techniques to Solve Multiobjective Linear Fractional Programming Problem and Comparison with Other Techniques

In the field of operation research, both linear and fractional programming problems have been more encountered in recent years because they are more realistic in expressing real-life problems. Fractional programming problem is used when several rates need to be optimized simultaneously such as resource allocation planning, financial and corporate planning, healthcare, and hospital planning. There are several techniques to solve the multiobjective linear fractional programming problem. However, because of the use of scalarization, these techniques have some limitations. This paper proposed two new mean and median techniques to solve the multiobjective linear fractional programming problem by overcoming the limitations. After utilizing mean and median techniques, the problem is converted into an equivalent linear fractional programming problem; then, the linear fractional programming problem is transformed into linear programming problem and solved by the conventional simplex method or mathematical software. Some numerical examples have been illustrated to show the efficiency of the proposed techniques and algorithm. The performance of these solutions was evaluated by comparing their results with other existing methods. The numerical results have shown that the proposed techniques are better than other techniques. Furthermore, the proposed techniques solve a pure multiobjective maximization problem, which is even impossible with some existing techniques. The present investigation can be improved further, which is left for future research.

Mathematical Optimization in Innovation Productivity: A Framework and A Case Study on UAV Border Patrolling in Türkiye

Purpose: In this paper, the potential of mathematical optimization (MO) in enhancing innovation productivity is explored. Innovation is a process that converts new ideas and methods into products and services, MO can contribute to innovation management by improving productivity across all stages, from pre-innovation to post-innovation. This paper establishes a connection between MO and innovation productivity while demonstrating an application for a post-innovation phase problem of unmanned aerial vehicles (UAVs). Methodology: A framework for incorporating MO into the design problems of innovation processes is developed. Additionally, a MO model is developed for a case study concerning UAV border patrolling in Türkiye. Findings: Computational experiments demonstrate MO's effectiveness in optimizing UAV routes and strategies, enhancing operational efficiency, and innovation productivity. Optimal recommendations and trade-offs among different mission considerations are obtained in 18 minutes on average (with a median of 5 seconds) over 210 runs. Originality: A link is established between MO and innovation productivity. An operations research problem is introduced for UAV operations in border patrolling in Türkiye. The codebase and data are openly provided for readers to apply the model in their research.

A hybrid multi-objective evolutionary algorithm with high solving efficiency for UAV defense programming

Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations face two main challenges: 1) Constructing a multi-objective optimization model reflecting the complexities of new UAV application patterns, and 2) solving the time-consuming WTA problem with high-dimension decision variables as it is NP-complete. This paper introduces a modified differential evolution based on a new mapping method called Pareto-Optimal-Matching that solves multi-objective binary optimization problems and constructs a 3 objective Sensor-Weapon-Target Assignment model. The proposed algorithm overcomes the inherent limitation of traditional differential evolution, which only performs well in continuous problems. Promising results were obtained based on different simulation metrics under various problems (proposed model, MOKP, and ZDT5), validating the superior performance of the proposed algorithm.

Ethical decision-making in human-automation collaboration: a case study of the nurse rostering problem

AbstractAs artificial intelligence (AI) is increasingly present in different aspects of society and its harmful impacts are more visible, concrete methods to help design ethical AI systems and limit currently encountered risks must be developed. Taking the example of a well-known Operations Research problem, the Nurse Rostering Problem (NRP), this paper presents a way to help close the gap between abstract principles and on-the-ground applications with two different steps. We first propose a normative step that uses dedicated scientific knowledge to provide new rules for an NRP model, with the aim of improving nurses’ well-being. However, this step alone may be insufficient to comprehensively deal with all key ethical issues, particularly autonomy and explicability. Therefore, as a complementary second step, we introduce an interactive process that integrates a human decision-maker in the loop and allows practical ethics to be applied. Using input from stakeholders to enrich a mathematical model may help compensate for flaws in automated tools.

Optimize or satisfice in engineering design?

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design.

Sailing league problems

AbstractWe describe a class of combinatorial design problems which typically occur in professional sailing league competitions. We discuss connections to resolvable block designs and equitable coverings and to scheduling problems in operations research. We in particular give suitable boolean quadratic and integer linear optimization problem formulations, as well as further heuristics and restrictions, that can be used to solve sailing league problems in practice. We apply those techniques to three case studies obtained from real sailing leagues and compare the results with previously used tournament plans.

Generalized vehicle routing problem: Contemporary trends and research directions

Generalized Vehicle Routing Problem (GVRP) is a challenging operational research problem which has been widely studied for nearly two decades. In this problem, it is assumed that graph nodes are grouped into a number of clusters, and serving any node of a cluster eliminates the need to visit the other nodes of that cluster. The general objective of this problem is to find the set of nodes to visit and determine the service sequence to minimize the total traveling cost. In addition to these general conditions, GVRP can be formulated with different assumptions and constraints to practically create different sub-types and variants. This paper aims to provide a comprehensive survey of the GVRP literature and explore its various dimensions. It first encompasses the definition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful suggestions are discussed to extend the problem. For this review study, Google Scholar, Scopus, Science Direct, Emerald, Springer, and Elsevier databases were searched for keywords, and 160 potential articles were extracted, and eventually, 45 articles were judged to be relevant.

Location-pricing problem in a two-echelon supply chain: A behavioral game-theoretic approach

The location-pricing problem is a relatively new class of problems in operations research that aims to solve both the pricing and facility location problems simultaneously. Unlike the classical facility location problem, where the demand of customers is known, in the location-pricing problem, the demand is a function of price. Other factors can also influence the final price of products, including production, transportation and inventory costs, competition and agents' behavior, which make the location-pricing problem complex. To this end, this paper proposes a bi-level mixed integer nonlinear programming model for the location-pricing problem in a two-echelon supply chain with players who make decisions based on their social preferences in a competitive market. The social preferences of players are a topic of great importance in the field of behavioral game theory. If the supply chain players care about the profits of other competitors in their utility functions, the simplifying assumption of pure selfishness must be corrected. It is predicted that considering social preferences for supply chain members can lead to better outcomes and payoffs. This paper proposes a single-to-multiple Stackelberg-Nash game in which a focal company serves as the leader, responsible for the location, capacity allocation, pricing, and production/distribution planning of manufacturers. Additionally, there are two competing retailers, as followers who base their pricing strategies on social preferences in order to gain a larger market share. The competition between the retailers determines the demands of customers. The effects of various parameters are then examined using a sensitivity analysis, and some economic and managerial insights are provided. The findings indicate that when both retailers exhibit status-seeking behavior in the market, average market demand increases as a result of lower retail and wholesale prices, resulting in an increase in the profit margins of the supply chain and agents compared to a model with self-interested players.

Packing problems on generalised regular grid: Levels of abstraction using integer linear programming

Packing a designated set of shapes on a regular grid is an important class of operations research problems that has been intensively studied for more than six decades. Representing a d-dimensional discrete grid as Zd, we formalise the generalised regular grid (GRG) as a surjective function from Zd to a geometric tessellation in a physical space, for example, the cube coordinates of a hexagonal grid or a quasilattice. This study employs 0-1 integer linear programming (ILP) to formulate the polyomino tiling problem with adjacency constraints. Rotation & reflection invariance in adjacency are considered. We separate the formal ILP from the topology & geometry of various grids, such as Ammann-Beenker tiling, Penrose tiling and periodic hypercube. Based on cutting-edge solvers, we reveal an intuitive correspondence between the integer program (a pattern of algebraic rules) and the computer codes. Models of packing problems in the GRG have wide applications in production system, facility layout planning, and architectural design. Two applications in planning high-rise residential apartments are illustrated.

Stochastic Localization Methods for Convex Discrete Optimization via Simulation

Solving Convex Discrete Optimization via Simulation via Stochastic Localization Algorithms Many decision-making problems in operations research and management science require the optimization of large-scale complex stochastic systems. For a number of applications, the objective function exhibits convexity in the discrete decision variables or the problem can be transformed into a convex one. In “Stochastic Localization Methods for Convex Discrete Optimization via Simulation,” Zhang, Zheng, and Lavaei propose provably efficient simulation-optimization algorithms for general large-scale convex discrete optimization via simulation problems. By utilizing the convex structure and the idea of localization and cutting-plane methods, the developed stochastic localization algorithms demonstrate a polynomial dependence on the dimension and scale of the decision space. In addition, the simulation cost is upper bounded by a value that is independent of the objective function. The stochastic localization methods also exhibit a superior numerical performance compared with existing algorithms.

Improving neighborhood exploration into MOEA/D framework to solve a bi‐objective routing problem

AbstractLocal search (LS) algorithms are efficient metaheuristics to solve combinatorial problems. The performance of LS highly depends on the neighborhood exploration of solutions. Many methods have been developed over the years to improve the efficiency of LS on different problems of operations research. In particular, the exploration strategy of the neighborhood and the exclusion of irrelevant neighboring solutions are design mechanisms that have to be carefully considered when tackling NP‐hard optimization problems. An MOEA/D framework including an LS‐based mutation and knowledge discovery mechanisms is the core algorithm used to solve a bi‐objective vehicle routing problem with time windows (bVRPTW) where the total traveling cost and the total waiting time of drivers have to be minimized. We enhance the classical LS exploration strategy of the neighborhood from the literature of scheduling and propose new metrics based on customer distances and waiting times to reduce the neighborhood size. We conduct a deep analysis of the parameters to give a fine tuning of the MOEA/D framework adapted to the LS variants and to the bVRPTW. Experiments show that the proposed neighborhood strategies lead to better performance on both Solomon's and Gehring and Homberger's benchmarks.

Solving a multi-objective solid transportation problem: a comparative study of alternative methods for decision-making

The transportation problem in operations research aims to minimize costs by optimizing the allocation of goods from multiple sources to destinations, considering supply, demand, and transportation constraints. This paper applies the multi-dimensional solid transportation problem approach to a private sector company in Egypt, aiming to determine the ideal allocation of their truck fleet.In order to provide decision-makers with a comprehensive set of options to reduce fuel consumption costs during transportation or minimize total transportation time, a multi-objective approach is employed. The study explores the best compromise solution by leveraging three multi-objective approaches: the Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. Optimal solutions are derived for time and fuel consumption objectives, offering decision-makers a broad range to make informed decisions for the company and the flexibility to adapt them as needed.Lingo codes are developed to facilitate the identification of the best compromise solution using different methods. Furthermore, non-dominated extreme points are established based on the weights assigned to the different objectives. This approach expands the potential ranges for enhancing the transfer problem, yielding more comprehensive solutions.This research contributes to the field by addressing the transportation problem practically and applying a multi-objective approach to support decision-making. The findings provide valuable insights for optimizing the distribution of the truck fleet, reducing fuel consumption costs, and improving overall transportation efficiency.

Among Us as an Operations Research Problem

Funding: The authors thank Universidad de Antioquia and Universidad ESUMER who have financed this research. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ited.2022.0029 . The Teaching Note is available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Meaningful Learning and Mathematical Modelling - Contributions to Dealing with Business Problem Situations in Higher Education

Background: Applying mathematical modelling in problem situations shows the possibility of observation of the existing meaningful learning as a learning environment for the construction of knowledge with problems situations of the professional daily life of students enrolled in the subject Operational Research of Fisul College Administration Course, at Serra Gaúcha, Rio Grande do Sul. Objectives: To elaborate, construct, and study a mathematical model that describes and/or explains these living situations in a professional routine integrated into the meaningful learning theory. Design: Qualitative research, based on the meaningful learning theory, in the operational research with the assistance of Solver and mathematical modelling. Setting and participants: The research was carried out in two classes of 24 students who attended the subject Operational Research in Higher Education. Data collection and analysis: The research was developed in five stages: Diagnostic Probing, Advanced Organizers, Evaluative Instrument, Modelling of Operational Research Problems, and Closure Activities. Results: Most students have evidence of the presence of subsumers in the activities developed, inserting concepts related to management, where they are related to the Business Administration course. Conclusions: The results revealed evidence of the Administrator’s professional conditions, such as the ability to recognise and define problems, introduce changes in the productive process, where the problems originated, and traces of construction of knowledge itself were perceived.

Special Issue of INFORMS Journal on Computing—Quantum Computing and Operations Research

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS Journal on ComputingAhead of Print Special Issue of INFORMS Journal on Computing—Quantum Computing and Operations ResearchCarleton Coffrin, Elisabeth Lobe, Giacomo Nannicini, Ojas ParekhCarleton Coffrin, Elisabeth Lobe, Giacomo Nannicini, Ojas ParekhPublished Online:15 May 2023https://doi.org/10.1287/ijoc.2023.cfp.v35.n3IntroductionQuantum computers offer an alternative way to approach computational problems, and it is widely believed that they can solve certain tasks significantly faster or with greater accuracy than any classical (i.e., nonquantum) computer. The impact of quantum computers on operations research, and vice versa, has thus far been limited, but this is expected to change. Indeed, the construction of more refined and scalable quantum hardware and software calls for the solution of a diverse range of problems that operations researchers are in prime position to address; examples include hardware design, hardware control, compiling, scheduling, and classical simulation of quantum devices. Furthermore, because of tremendous progress in hardware development and in the construction of basic algorithmic primitives, the last few years have shown significant growth in the volume of research dedicated to the application of quantum computers for the solution of operations research problems. This includes, but is not limited to, optimization, simulation, and learning.Special Issue ScopeThe INFORMS Journal of Computing wishes to promote high-quality research at the intersection of quantum computing and operations research. To foster the growth of the community and give visibility to top-tier work in this area, the Journal is devoting a special issue to this topic, and it invites authors to submit manuscripts that substantially advance the state of the art in methodology, theory, algorithms, applications, or software implementation as they pertain to all areas at the intersection of quantum computing and operations research. This includes, but is not limited to, novel results regarding the development and analysis of quantum or quantum-inspired classical algorithms for solving problems in operations research or based on the application of operations research techniques, the development of solution methodologies for problems that arise in the design and operation of quantum computers, and insightful empirical testing of the aforementioned types of algorithms.The main goal of this special issue is to give a platform to novel research in this emerging area. Survey and tutorial papers are included in this call; however, any survey or tutorial paper will have to maintain a very high standard of novelty and contribution with respect to existing surveys and tutorials in the open literature. We strongly encourage interested authors to contact one of the guest editors for this issue to discuss whether their work fits the scope of this special issue (see Timeline).The editorial statement of the journal is available at https://pubsonline.informs.org/page/ijoc/editorial-statement.Submissions should follow the standard requirements of the INFORMS Journal on Computing articles available at https://pubsonline.informs.org/page/ijoc/submission-guidelines.Please note that the INFORMS Journal on Computing has a data policy https://pubsonline.informs.org/page/ijoc/datapolicy and a software policy https://pubsonline.informs.org/page/ijoc/softwarepolicy.TimelineThe editors ask potential authors to prepare a short proposal, that is, a letter of intent, before making a full submission of a paper. Please note the following dates:September 30, 2023: Letter of interest sent to the editors (via email), with an abstract for the proposed contribution. (This step is not mandatory but strongly preferred to ensure proper review. Early letters are encouraged; submit the letter copying all guest editors).January 15, 2024: Deadline for first round of submissions.April 15, 2024: First round of reviews completed; decisions (and requests for revision, if appropriate) delivered to authors.June 15, 2024: Revisions due after first round decisions.August 15, 2024: Second round of reviews delivered to authors.August 30, 2024: Final versions of accepted papers must be submitted.Submission InstructionsPapers must be submitted online at https://mc.manuscriptcentral.com/ijoc.When submitting a paper, please note the following:Under “Step 1: Type, Title, & Abstract,”- select “Special Issue” for “Type,”- select “Special Issue” under “Select topic area of submission,”- and answer “Quantum Computing” for “If this paper is for a special issue, which one is it for?”. Back to Top Next FiguresReferencesRelatedInformation Articles In Advance Article Information Metrics Information Published Online:May 15, 2023 Copyright © 2023, INFORMSCite asCarleton Coffrin, Elisabeth Lobe, Giacomo Nannicini, Ojas Parekh (2023) Special Issue of INFORMS Journal on Computing—Quantum Computing and Operations Research. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.cfp.v35.n3 PDF download