Explore the vast range of titles available.

This study examines the fragmented and rapidly evolving body of knowledge on the application of quantum computing to NP‐hard decision problems in Operations Management (OM) and Operations Research (OR). It aims to systematically map how quantum optimization approaches are formulated and applied across core OM/OR problem classes, highlighting current advances and unresolved challenges for research and practice. A systematic mapping review was conducted using peer‐reviewed studies indexed in Scopus and Web of Science from 2014 to 2026. The literature was classified by problem type, mathematical formulation, quantum technique, and application domain, with attention to the alignment between quantum models and established OM/OR decision frameworks. The review reveals a strong predominance of QUBO‐based formulations and annealing‐oriented approaches, mainly applied to logistics, manufacturing, and financial optimization problems. Applications remain largely exploratory, with limited empirical validation, weak theoretical integration with OM/OR decision‐making models, and persistent challenges related to scalability and hybrid quantum‐classical performance. Only a small subset of studies demonstrates how quantum formulations can support real‐world scheduling and resource coordination problems. This study proposes an analytical framework linking quantum optimization paradigms to canonical OM/OR NP‐hard problem classes, identifying key research gaps and methodological tensions to support more theory‐driven and empirically grounded future applications, especially in complex decision environments. Quantum computing techniques such as Quantum Annealing and Quadratic Unconstrained Binary Optimization are effectively solving NP‐hard problems in operations management and research, particularly in logistics, manufacturing, and finance. This study maps these applications to present a framework for future adoption across industries.

Modern computers require an exponential increase in resources when solving computationally hard problems, motivating the need for an alternative computing platform to solve such problems in an energy‐efficient manner. Vertex coloring, a nondeterministic polynomial time (NP‐hard) combinatorial optimization problem, is one such problem. Herein, an experimental demonstration of using cardiac cell‐based bio‐oscillator network coupling dynamics to solve a vertex coloring problem in various scales of graphs using a simple cell patterning method to construct scalable and controlled cardiac cell networks is presented. Although there are limitations to using these cardiac cells as oscillators, such as their low frequency compared to complementary metal–oxide–semiconductor (CMOS) oscillators, that result in longer processing times, the accuracy in large graph instances, the significantly less amount of energy consumption, and the ease of fabrication and potential to extend this system to massively parallel 3D structures make the bio‐oscillators a promising new platform for collective computing applications. Experimental demonstrations of several multinode cardiac cell‐based bio‐oscillator networks, from small to large scales, for solving vertex coloring problems, are presented. Although there is limitation in terms of low frequency, advantages in energy consumption and ease of fabrication and potential to extend to massively parallel 3D structures make bio‐oscillators a promising new platform for collective computing applications.

Mobile nodes in underwater wireless sensor networks are becoming very important as they not only enable flexible sensing areas but also entails the ability to provide means for data and energy sharing among existing static sensor nodes. In this paper, three efficient meta-heuristic evolutionary algorithms ant colony optimization, artificial bees colony and firefly algorithm, inspired by swarm intelligence are being compared with an objective to achieve the shortest path for the mobile node in traversing the complete sensing network. We transform this problem into the traveling salesman problem. It is the most famous and commonly used nondeterministic-polynomial combinatorial optimization problem in which an artificial agent is set to travel between different cities and calculate distance or time consumed to travel between these nodes or cities for best route selection. Heuristic and meta-heuristic algorithms are being used for decades to solve such type of problems. In this comparative study, an analysis of meta-heuristic algorithms for obtaining results in less processing time while searching for the optimal solution has been done. Moreover, this paper provides a classification of mentioned algorithms and highlights their characteristics. The experiment has been carried out on these algorithms by manipulating different parameters such as population and number of iteration.

Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address these challenges, the swarm intelligence algorithm is introduced as a metaheuristic method and effectively implemented. However, existing technology exhibits drawbacks such as slow convergence speed, low precision, and poor robustness. In this paper, we propose a novel metaheuristic approach called the Red-billed Blue Magpie Optimizer (RBMO), inspired by the cooperative and efficient predation behaviors of red-billed blue magpies. The mathematical model of RBMO was established by simulating the searching, chasing, attacking prey, and food storage behaviors of the red-billed blue magpie. To demonstrate RBMO’s performance, we first conduct qualitative analyses through convergence behavior experiments. Next, RBMO’s numerical optimization capabilities are substantiated using CEC2014 (Dim = 10, 30, 50, and 100) and CEC2017 (Dim = 10, 30, 50, and 100) suites, consistently achieving the best Friedman mean rank. In UAV path planning applications (two-dimensional and three − dimensional), RBMO obtains preferable solutions, demonstrating its effectiveness in solving NP-hard problems. Additionally, in five engineering design problems, RBMO consistently yields the minimum cost, showcasing its advantage in practical problem-solving. We compare our experimental results with three categories of widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, and (3) high-performance optimizers, including CEC winners.

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods.

In the cluster editing problem, one has to partition the set of vertices of a graph into pairwise disjoint subsets (called clusters) minimizing the number of edges between clusters and the number of missing edges within clusters. We consider a version of the problem in which cluster sizes are bounded from above by a positive integer . This problem is NP-hard for any fixed . We propose polynomial-time approximation algorithms for this version of the problem. Their performance guarantees equal for the case and for . We also show that the cluster editing problem is APX-hard for the case of .

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.

Typically, the problem of scheduling exams for universities aims to determine a schedule that satisfies logistics constraints, including the number of available exam rooms and the exam delivery mode (online or paper-based). The objective of this problem varies according to the university’s requirements. For example, some universities may seek to minimize operational costs, while others may work to minimize the schedule's length. Consequently, the objective imposed by the university affects the complexity of the problem. In this study, we present a grouping-based approach designed to address the problem of scheduling the exam timetable. The approach begins by profiling the courses’ exams based on their requirements, grouping exams with similar requirements to be scheduled at the same time. Then, an insertion strategy is used to obtain the exam schedule while satisfying the imposed constraints of the targeted university. We applied this approach to the problem of exam scheduling at Al-Hussein Bin Talal University in Jordan and achieved a balanced exam schedule that met all the imposed constraints.

In this study, for the first time, the classical single row facility layout problem is extended to its dynamic type by considering several planning periods. This new problem consists of two types of costs e.g. material handling cost and rearrangement cost of the departments at the beginning of each period. The problem is formulated by a mixed integer linear programming model. Because of the high complexity of the problem, two well-known meta-heuristic algorithms e.g. the GA and the SA are proposed to solve the problem. In addition, both of the algorithms are hybridized considering the restart and acceptance probability strategies. In order to study the performance of the proposed algorithms, 20 benchmark problems are generated randomly. Considering one of the generated benchmarks, the parameters of the algorithms are tuned by a typical method and final experiments are performed accordingly. The obtained results strongly prove the superiority of the SA hybridized by the restart strategy as it shows much better performance comparing to other proposed algorithms in more than 60% of the benchmarks.

We consider a two-bar charts packing problem in which it is necessary to pack bar charts consisting of two bars in a unit-height strip of minimum length. Each bar has a height of at most 1 and unit length. The problem under consideration is NP-hard and generalizes the bin packing problem and two-dimensional vector packing problem. This paper proves updated accuracy estimates and time complexity for several previously developed polynomial approximation algorithms for the two-bar charts packing problem and particular cases of the problem. We show the attainability of the estimates. Furthermore, we consider a problem of packing an unlimited number of bar charts belonging to different types and propose a polynomial algorithm to solve the problem in case .