Operations Research Community Research Articles

The resonance of Mike Jackson's work with the use of systems ideas in community operational research

AbstractThe body of work of Mike Jackson covers several major themes in OR/Systems Thinking and articulates key aspects of Critical Systems Thinking; with an interest throughout in applications to complex social challenges. In this paper, as a direct response to this Festschrift, and acknowledging his contribution to Community OR, five active UK‐based researchers have engaged in their own process of community‐based learning in order to articulate the ways Jackson's work resonates with their contemporary research and practice. The researchers used a variation of the Delphi method to reflect first on the ways that the body of work of Jackson resonated with their practice and research agendas. This produced a framework of ideas. Examples from the UK and overseas are then provided to illustrate these points. Ultimately, the researchers used these experiences and reflections to produce a series of statements for developing Community OR practice (and theory)—reflecting and extending Jackson's work.

A survey on optimization studies of group centrality metrics

AbstractCentrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization.

Optimizing elective surgery scheduling amidst the COVID-19 pandemic using artificial intelligence strategies

The COVID-19 pandemic profoundly affects elective surgery and healthcare resources. Efficient management of resources, like ward capacity and operating theaters, is crucial. The operations research community explores solutions, notably leveraging artificial intelligence, to address scheduling challenges amid COVID-19 restrictions. In this situation, applying AI becomes essential to getting the best results. In this paper, we address the problem of daily scheduling elective surgeries while accounting for hospital ward capacity. It is possible to reduce this issue to a scheduling puzzle that, given a variety of restrictions, resembles a four-stage hybrid flow shop. These limitations include the availability of resources, patient flow control, wait time avoidance, patient prioritizing, and resource coordination. With the crucial aid of artificial intelligence, our main goal is to assign patients to different surgical resources to minimize the length of time they spend on average in the hospital ward. We suggest putting into practice effective optimization strategies that make use of AI-based algorithms, particularly the variable neighborhood search (VNS) and variable neighborhood descent (VND) algorithms, which are inextricably linked with artificial intelligence concepts. Our studies demonstrate the effectiveness and efficiency of the general VNS in addressing the daily elective surgical scheduling issue (SSP) with the priceless assistance of artificial intelligence. The experiments are based on novel data instances that were inspired by current literature guidelines. The test results conclusively demonstrate the ability of our algorithms to find virtually perfect solutions. Moreover, our results highlight that the use of these methods, strengthened by AI, can significantly increase the size of the solved issue by a remarkable factor of 19.54. In light of the current COVID-19 pandemic, AI thus becomes a key factor in optimizing the scheduling of elective surgeries and the allocation of resources.

The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem

The permutation flowshop scheduling problem (PFSP) attracted much interest from the operations research (OR) community, resulting in various heuristic and metaheuristic methods over the past half-century. However, given the hard nature of the PFSP, efficient algorithms rely on the configuration of initial solutions and sophisticated heuristics. Combining OR and artificial intelligence (AI), this paper investigates the marriage of OR and reinforcement learning for the PFSP. A novel method integrating NEH into the Q-learning algorithm is proposed for solving the PFSP with makespan criterion. With the refinement of action selection by strengthening good actions and weakening bad actions, the proposed Q-NEH algorithm shows a better learning and convergence rate, and outperforms the Q-learning algorithm for all 120 instances in Taillard’s benchmark dataset, with a significantly decreased average relative learning error (RLE) from 13.05 % to 1.73 %. Furthermore, by comparing the performances of the Q-NEH algorithm with related state-of-art benchmark algorithms in a wide range set of instances, it further confirms the superiority and stability of the Q-NEH algorithm in statistics. With a total of 2570 independent tests in two phases of experimental evaluations, the superiority of the Q-NEH algorithm for the PFSP suggests that the integration of OR and AI is a promising research direction for solving complex scheduling problems.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

Learning to Optimize Contextually Constrained Problems for Real-Time Decision Generation

The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported in part by the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03565 .

Mathematical optimization modelling for group counterfactual explanations

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of Explainable Artificial Intelligence. In Supervised Classification, this means associating with each record a so-called counterfactual explanation: an instance that is close to the record and whose probability of being classified in the opposite class by a given classifier is high. While the literature focuses on the problem of finding one counterfactual for one record, in this paper we take a stakeholder perspective, and we address the more general setting in which a group of counterfactual explanations is sought for a group of instances. We introduce some mathematical optimization models as illustration of each possible allocation rule between counterfactuals and instances, and we identify a number of research challenges for the Operations Research community.

Learning for multiple purposes: A Q-learning enhanced hybrid metaheuristic for parallel drone scheduling traveling salesman problem

In recent years, combining the artificial intelligence techniques with traditional operations research approaches for solving combinatorial optimization problems is an interesting issue in Operations Research (OR) community. There have been numerous studies in the literature that combine machine learning technique with metaheuristic algorithms for a single purpose to improve the latter’s performance. In this paper, we attempted to integrate the reinforcement learning technique Q-learning into a ruin-and-recreate based metaheuristic for multiple purposes, and thus proposed a hybrid heuristic Q-learning aided slack induction by string removals (QSISRs). Incorporating drones into last-mile delivery alongside trucks and couriers is an important trend in the development of urban logistics distribution. The parallel drone scheduling traveling salesman problem (PDSTSP) arises when a certain proportion (from 20%–100%) customers are located within a drone’s flight range from the depot, and drones can directly serve customers from the depot. In this problem, drones and trucks operate independently, and no synchronization is required. The proposed QSISRs was applied to the PDSTSP to evaluate its performance. Numerical experiments validate the effectiveness of the Q-learning technique for algorithm performance enhancement, and also demonstrate the effectiveness of the proposed QSISRS for solving the PDSTSP. Furthermore, comparison results indicate that QSISRs is one of the state-of-the-art heuristic approaches for solving this problem.

Improved dynamic programming algorithms for unconstrained two-dimensional guillotine cutting

In the unconstrained two-dimensional cutting problem (U2DCP), we are given a large rectangular sheet to be cut in order to extract small rectangular pieces, with no limits on the number (demand) of desired pieces. We face the variant with guillotine constraint, requiring to cut any rectangle in two parts through vertical/horizontal cuts with end points on the rectangle boundaries.For a given U2DCP instance, the dynamic programming approach can be used either to optimally solve it, or to obtain a full matrix of upper bounds suitable for the constrained variant of the problem where limits exist on the piece demands. The elements of the full matrix are also usable as partial solutions to build lower bounds for the non-guillotine variant.In this paper, we propose two major improvements to a dynamic programming procedure previously shown to be capable of solving very large size instances. First, we introduce a new option for one of the three conditions used for the anti-redundancy strategies on cut coordinates. Second, following the effort of the Operations Research community to exploit the feature of modern CPUs containing multi-core processors, we provide a parallelization scheme.An extended computational campaign is presented. We compare the upgraded procedure with its previous version on a single thread and with the currently state-of-the-art algorithm for multi-thread platforms, outperforming both in terms of execution time on average by a factor of 1.7 and 12, respectively, or for some problem instances up to 4.5 and 50, respectively. Moreover, the new procedure can solve two very large instances previously unsolved, as well as the new huge instances proposed in this paper.

A cascading model of stakeholder engagement for large-scale regional development using structured dialogical design

We present a large-scale systemic intervention conducted in the wine village communities of Cyprus. The scientific aim of this paper is to contribute to expanding community operational research's purpose, scope, and tools by reflecting on a cascading engagement model of stakeholders toward regional development in Cyprus. An ad-hoc committee comprising three representatives of the Department of Town Planning and Housing, three members of the Cyprus Academy of Public Administration, and a few external consultants came up initially with about a dozen stakeholders’ categories they considered “relevant.” Using a stepwise (cascading) expansion, they eventually engaged over 200 actors representing 26 stakeholder categories. The first 29 stakeholders identified 71 obstacles to developing the wine villages using Structured Democratic Dialogue (SDD). They clustered the obstacles into themes and formed working groups to address them. Each group identified and invited experts and additional stakeholders they considered relevant to form extended theme-specific groups. Each group conducted a SWOT analysis to deepen understanding and produced a thematic vision map using SDD. A holistic vision map was constructed through a final SDD process in which participants were the authors of the most influential factors from each thematic vision map. The approach adheres to the bottom-up, community-led method and falls within community operational research and citizen science. The cascading model offers a formula for multi-methodological intervention. The stepwise bottom-up identification and engagement of stakeholders empowers and engages authentically, especially those marginalised. The expansion in numbers and the effective engagement of stakeholders creates the required momentum for societal change. We discuss the model's broader applicability, frame it within third-phase science, and suggest future directions.

Markov chain Monte Carlo approach to the analysis of response patterns in data collection process

Survey research, such as telephone, mail, or online questionnaires, is one of the most widely used tools for collecting sample data. We are often interested in the total number of replies that would be received during a given time period. Many researchers have developed a wide variety of curve-fitting methods to predict the response rate of recipients over time. However, previous models are based on some assumptions that are hardly justified in practice. In this paper, a new response model is proposed that is based on meaningful parameters such as the ultimate response rate of questionnaire recipients, delay rate of respondents, and average delivery time of responses. To estimate those model parameters, we use the Markov chain Monte Carlo (MCMC) method, which is increasingly popular in the operational research community. With mail survey data in marketing research, we test our Bayesian response model and compare its performance with those of traditional curve-fitting models.

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry.

Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area.

The reorganization of an emergency medical system means that we look for new locations of ambulance stations with the aim of improving the accessibility of the service. We applied two tools that are well known in the operations research community, namely mathematical programming, and computer simulation. Using the hierarchical pq-median model, we proposed optimal locations of the stations throughout the country and within large towns. Several solutions have been calculated that differ in the number of stations that are supposed to be relocated to new positions. The locations proposed by the mathematical programming model were evaluated via computer simulation. The approach was demonstrated under the conditions of the Slovak Republic using real historical data on ambulance dispatches. We have concluded that (i) the distribution of the stations proposed by the hierarchical pq-median model overcomes the current distribution; the performance of the system has significantly improved even if only 10% of the stations are relocated to new municipalities; (ii) the variant that relocates 40% of the stations is a reasonable compromise between the benefits and induced costs; (iii) optimizing station locations in big towns can significantly improve the local as well as the nationwide performance indicators; the response times in two regional capitals has reduced by more than 4 min.

Frameworks and Results in Distributionally Robust Optimization

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.

A control algorithm for a non-stationary batch service production system using Kalman filter

This paper presents a dynamically optimized control algorithm for a fully automated batch service production process, with non-stationary nature of order arrivals. The problem of interest is a non-stationary SELSP, i.e., stochastic economic lot sizing problem, with erratic order flow, where the objective is to continuously optimize the production lot size according to the real-time detection of demand fluctuations. Such a problem has a dual nature of being a planning problem, which used to be dealt with within the operational research (OR) community and a control problem, often dealt with within the control theory (CT) circle.To tackle the problem a Kalman Filter (KF) based approach has been employed, as one of the most reliable methods in CT, with an edge over its previously studied alternatives, i.e., Exponential Filter (EF) and its fuzzified version. It is illustrated that the adopted KF performs much better both in terms of efficiency metrics of OR, and responsiveness metrics of CT. The results advocate that the new generation of automated production planning and control systems may benefit from the concepts and methods from both communities of OR and CT, in an integrated fashion.

On the role of metaheuristic optimization in bioinformatics

AbstractMetaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

How can operational research make a real difference in healthcare? Challenges of implementation

This paper is based on the keynote address given by the paper's first author at EURO 2021. We draw on our experience over more than three decades to define the critical challenges of healthcare implementation. We do not address issues pertaining to technical quality of a solution. Rather, we focus on five general characteristics of the problem that should be carefully considered for any healthcare project that requires implementation. The problem needs an internal Champion; there should be a current Critical Issue; one must understand and adapt to the Cultural dynamics of the organization; appropriate Data exists; and we need to manage Expectations. We illustrate each with examples of our successes, failures, and mixed results. Finally, we summarize what short and long-term steps we believe the operational research community can take that will lead to improvement in each of these areas.

Homeland security research opportunities

Homeland security research has gone through a significant transformation since the events of September 11, 2001, and continues to evolve. This article identifies opportunities that the industrial engineering and operations research communities can seize. By drawing together insights from thought leaders in these communities, a path outlining research problems and discovery is provided that will serve to guide industrial engineering and operations research innovations and help move homeland security research forward over the next decade.

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

The multivisit drone routing problem with edge launches: An iterative approach with discrete and continuous improvements

AbstractIn recent years, the usage of drones in last‐mile logistics has stirred great interest in the operations research community. Many papers have considered schemes of hybrid truck‐and‐drone delivery. In this paper, we focus on the Multivisit Drone Routing Problem with Edge Launches (MVDRP‐EL) which assumes: a heterogenous set of packages, a drone capable of carrying multiple packages at a time and that can be launched and retrieved along an edge, a flexible launch/retrieval site set, and a user‐defined energy depletion function. We believe this paper is the first to exploit edge launch ability through a global continuous approach. In this context, we propose an original formulation based on the Covering Salesman Problem to compute a valid lower bound for the problem and an iterative solution method to determine a MVDRP‐EL solution with quality launch/retrieval sites along the road network edges. Each iteration of the solution method consists of two phases. In the first phase, the road network edges are discretized to obtain launch/retrieval sites, and a first solution is determined. In the second phase, the truck route is set and we reduce the completion time by carefully synchronizing truck and drone routes by solving an original Mixed Integer Second Order Cone Program. The lower bound formulation and the proposed method have been tested on several instances and results indicate the effectiveness of the proposed method and the potential value of launching along an edge, respectively.