The permutation flow shop scheduling problem (PFSP) is a fundamental scheduling problem for which heuristic methods are widely used because of the rapidly growing solution space. This study investigates whether solution populations generated entirely...
An innovative trajectory strategy was proposed and accessed for wire arc additive manufacturing (WAAM), applicable to different and more complex geometries, rather than being a single solution. This strategy, named Pixel, can be defined as a complex...
End-to-end obstacle avoidance path planning for intelligent vehicles has been a widely studied topic. To resolve the typical issues of the solving algorithms, which are weak global optimization ability, ease in falling into local optimization and slo...
Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost...
Human–machine collaborative scheduling has been widely applied in the modern manufacturing industry. Traditional scheduling algorithms often rely on frequent rescheduling when new jobs arrive, resulting in low responsiveness and difficulty in m...
This paper proposes a new model initialization approach for solar power prediction interval based on the lower and upper bound estimation (LUBE) structure. The linear regression interval estimation (LRIE) was first used to initialize the prediction i...
Nonlinear substitutions or S-boxes are important cryptographic primitives of modern symmetric ciphers. They are designed to complicate the plaintext-ciphertext dependency. According to modern ideas, the S-box should be bijective, have high nonlineari...
To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect conv...
In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is p...
Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for cl...
Automated operation of urban stormwater pumping stations is a sequential decision-making problem that must prevent detention-basin overflow during intense rainfall while coordinating pump switching, pump use, and dry-running risk. This study proposes...
The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the...
This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to a...
Cold rolling is the precision finishing stage in the steel production process, and its scheduling optimization is essential for enhancing production efficiency. To address the complex process constraints and objectives, this paper proposes a multi-gr...
Numerous applications rely on data obtained from a wireless sensor network where application performance is of utmost importance. However, energy usage is also important, and oftentimes, a subset of sensors can be selected to maximize application per...
Electric vehicles (EVs) are crucial for mitigating greenhouse gas emissions in urban transportation. However, their integration requires efficient charging infrastructure and allocation strategies. In this paper, five heuristic algorithms were develo...
With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon...
The harmony search (HS) algorithm is a novel metaheuristic which has been widely used to solve both continuous and discrete optimization problems. In order to improve the performance and simplify the implementation of the HS algorithm for solving the...
Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear mod...
Strategic drone-delivery planning couples field locations with drone assignments and customer allocations under field capacity, cumulative payload, and round-trip energy constraints. We formulate this problem as a mixed-integer linear program and der...
The escalating global demand for containerized cargo has intensified pressure on container terminals, which serve as vital nodes in maritime logistics. This study aims to enhance operational efficiency in non-automated container terminals by examinin...
Given the environmental impacts produced by the growing increase in waste electrical and electronic equipment (WEEE) and their current inadequate management, this article proposes a mathematical model to define the best location for installing WEEE c...
In this article, we explore the use of meta-heuristic algorithms for costly black-box permutation optimization problems. These combinatorial problems are defined by solution spaces that consist of permutations of elements, with an objective function...
This work proposes a matheuristic approach that combines heuristic techniques with mathematical optimization to address a bi-objective, multi-source team orienteering problem. During the exploration phase, nodes are assigned to depots using a biased-...
Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools...
The Permutation Flow Shop Planning Problem (PFSSP) is fundamental and frequently encountered in manufacturing systems and service operations. This problem is known to be NP-hard for three or more machines. Therefore, various heuristic and metaheurist...
Traditional path-planning algorithms for robotic manipulators typically focus on end-effector planning, often neglecting complete collision avoidance for the entire manipulator. Additionally, many existing approaches suffer from high time complexity...
Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the perfo...
High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itse...
In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT)...
In response to issues such as insufficient bias in random sampling, low convergence efficiency, inadequate path search efficiency, and lack of path smoothness encountered by the traditional RRT* algorithm during path planning, an improved algorithm i...
Background: Dual-level stochastic programming is a technique that allows modelling uncertainty at two different levels, even when the time granularity differs vastly between the levels. In this paper we study the problem of determining the optimal fl...
This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfy...
The Weibull distribution is widely used in reliability estimation across industries, but accurately identifying its parameters remains a challenging task. This research proposes an efficient method for estimating Weibull distribution parameters by co...
To resolve the problems of low prediction accuracy and slow convergence speed of traditional extreme learning machines in network security situation prediction methods, we combine a meta-heuristic search algorithm with neural networks and propose a p...
A method is proposed to fit parameters of Power System Stabilizer controllers in electromechanical multimachine power systems. The use of the Non-dominated Sorting Genetic Algorithm II heuristic method and Tabu search is considered to be initial sear...
The maximum common subgraph of two graphs is the largest possible common subgraph, i.e., the common subgraph with as many vertices as possible. Even if this problem is very challenging, as it has been long proven NP-hard, its countless practical appl...
The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jo...
To enhance the efficiency of locating dynamic missing persons in complex mountain terrain, this study introduces an innovative Slope Probability Search (SPS) algorithm based on a modified A* framework. The algorithm’s core is a dynamic global p...
Group Relative Policy Optimization (GRPO) streamlines the alignment of Large Language Models (LLMs) and Vision–Language Models (VLMs) by eliminating the Critic model. However, its efficiency heavily depends on the group size, G. While a larger...
Efficient production scheduling is a key challenge in industrial operations and continues to attract significant interest within the field of operations research. This paper investigates a range of methodological approaches designed to solve the perm...
Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, construct...
Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion...
Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and...
In this paper, we propose the Manifold Random Drift Particle Swarm Optimization (MRDPSO) algorithm for matrix optimization on smooth manifolds. Conventional swarm intelligence methods generally converge prematurely in constrained domains. To mitigate...
At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation...
The subset-sum problem, a foundational NP-hard problem in theoretical computer science, serves as a critical building block for cryptographic constructions. This work introduces novel classical and quantum heuristic algorithms for the random subset-s...
This research investigates the home health care routing and scheduling problem with soft time windows and perishable medicines (HHCRSP-STW-PM), where care crews must serve patients over multiple periods while accounting for skill-based assignments, m...
Due to increasing energy consumption, green scheduling in the manufacturing industry has attracted great attention. In distributed manufacturing involving heterogeneous plants, accounting for complex work sequences and energy consumption poses a majo...
In recent years, social networks have become increasingly popular as platforms for personal expression, commercial transactions, and government management. The way information propagates on these networks influences the quality and expenses of social...
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