Search Results (294)

In recent years, with the advancement of production technology in the manufacturing industry, the scheduling problems that rely on modeling in real-world scenarios have gradually evolved into complex process flows. Aiming at the limited problem modeling capabilities of existing scheduling problems, this study proposes Multi-Stage Flexible Job Shop Scheduling Problem (MS-FJSP). MS-FJSP alters the fixed operation processing sequence of jobs in conventional scheduling problems and introduces staged processing to incorporate flexible constraints on operation selection. Furthermore, MS-FJSP modifies the constraint of unique machine compatibility, enabling arbitrary adjustments to machine combinations according to processing requirements. To address the complex flexibility and large-scale solution space of MS-FJSP, we propose a particle swarm optimization algorithm based on double neighborhood tabu search (TS-PSO). Specifically, the PSO algorithm determines a superior neighborhood structure for this problem, while the TS algorithm improves and optimizes the solution quality within the neighborhood of this solution structure. We verify the algorithm’s performance using a dataset consisting of 12,000 MS-FJSP instances and an MS-FJSP instance modeled from a real-world scheduling scenario. Experimental results demonstrate that TS-PSO can achieve excellent solution quality within a reasonable time, and MS-FJSP possesses efficient modeling capability for real-world scheduling scenarios. Full article

This paper proposes an efficient algorithm that integrates a variable neighborhood search (VNS) framework with an adaptive voxel discretization for the three-dimensional irregular packing problem. The problem arises in additive manufacturing, logistics loading, and other fields, especially in strip packing scenarios where the filling length in a virtual container with a fixed cross-section and infinite length is to be minimized. The algorithm first discretizes continuous three-dimensional geometric models into Boolean voxel matrices, thereby transforming complex geometric interference detection into efficient logical operations. An initial solution is generated using a greedy “largest-volume-first” strategy. An innovative adaptive voxel precision adjustment mechanism is introduced to dynamically modify the discretization granularity according to the current filling rate, realizing a hierarchical solution strategy of “coarse-grained fast search + fine-grained precise optimization”. On this basis, a variable-neighborhood iterative framework based on tabu search (TS-VNS) is constructed. Three complementary neighborhood operators are designed: single-item reinsertion, block exchange, and rotation perturbation, together with an adaptive operator selection mechanism driven by historical contributions. Experiments on multiple standard instances of varying scales and complexities (e.g., miniature chess pieces and engine components) show that the proposed algorithm outperforms comparative methods in both packing height and average height, achieving a favorable balance between solution efficiency and stability. Thus, it provides a reliable and efficient approach for the practical engineering application of three-dimensional irregular packing. Full article

The logical structure of approximation algorithms has been identified by the scientific community in four principal parts: tuning parameters, generating initial solutions, generating neighbor solutions, and stopping algorithm execution. A review of the literature specifically for the algorithms Threshold Accepting (TA) and Tabu Search (TS) indicates that, in most cases, choices are performed on one or several of these logical parts, often implicitly guided by expert knowledge for improving algorithm performance. However, these design choices, particularly in the selection of initialization and neighborhood strategies, are rarely analyzed in a systematic and reproducible manner. A formal experimental framework is presented to systematically analyze logical structure design choices, which are typically based on empirical expertise, by isolating and evaluating the combined effects of methodologies in the logical parts of initialization and neighborhood under controlled conditions of TA and TS algorithms in solving the one-dimensional Bin Packing Problem (BPP). A total of 324 benchmark instances were used to assess multiple algorithmic variants. Performance was evaluated in terms of solution quality and computational effort, supported by graphical analysis and statistical methods, including Wilcoxon signed-rank tests, effect size measures, bootstrap-based confidence intervals, and linear regression. The experimental results consistently show that the simpler internal logical structure of TA and TS algorithms, specifically with a probability-guided initialization combined with a single neighborhood operator, can achieve a better balance between solution quality and computational effort compared to more complex alternatives in general instances of BPP. Full article

This work proposes solving the Traveling Salesman Problem by applying combined heuristic global and local search methods. The proposed method is divided into three phases: first, it evaluates an initial route and chooses the minimum value of rows in a distance matrix. The next phase seeks to improve the route’s cost globally and with a 2-opt local search method, remove the crossings, and further minimize the cost of departure. Finally, the last phase evaluates and conserves each cost using tabu search, proposing a parameter $\beta$ that describes the algorithm convergence factor. This paper assessed 29 TSPLIB instances and compared them with other algorithms: the ant colony optimization algorithm (ACO), artificial neural network (ANN), particle swarm optimization (PSO), and genetic algorithm (GA). With the proposed algorithm, results close to the optimal ones are obtained, and the proposed algorithm is assessed on 29 TSPLIB instances. Based on 30 independent runs per instance, the method achieves a mean absolute percentage error (MAPE) of 1.4484% relative to the known optima, demonstrating its accuracy. Furthermore, statistical comparisons using the coefficient of variation (CV) for runtime and the Wilcoxon signed-rank test confirm that the proposed hybrid algorithm is significantly faster than traditional ant colony optimization (T-ACO) and a new ant colony optimization algorithm (N-ACO) while maintaining competitive solution quality. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing operations are fixed, while only the rescheduling window composed of waiting operations and urgent-order operations is re-optimized. On this basis, two rescheduling strategies, namely complete rescheduling and deferred rescheduling, are designed and compared. Second, to improve the solution capability in complex dynamic environments, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) with a three-layer encoding scheme is proposed. The algorithm incorporates hybrid initialization, tabu-guided crossover, simulated annealing mutation, and critical-path-based variable neighborhood search. Experimental results show that the proposed method performs well in energy consumption optimization and tool wear control, while effectively improving the diversity and distribution quality of the Pareto solution set. Further analysis indicates that deferred rescheduling generally outperforms complete rescheduling, while the original-orders-first and urgents-first strategies exhibit different strengths in convergence, solution quality, and objective optimization. The proposed study provides an effective modeling and optimization framework for multi-objective green rescheduling problems and offers theoretical support for production scheduling decisions that need to balance production efficiency, energy saving, and tool-related cost control in practical manufacturing systems. Full article

With the rapid growth of the civil aviation industry, airport gate resources—especially those equipped with jet bridges (more convenient than shuttles)—have become increasingly scarce, posing new challenges to the sustainable management of airport operations. In a real-world application of the airport transport optimization study field, the airport gate assignment problem (AGAP) has emerged as a critical scheduling task in airport operations with the rapid growth of passenger demand. In this study, a mixed-integer linear programming model is developed for AGAP, aiming to minimize baggage transfer vehicle usage, maximize airline satisfaction, reduce passenger boarding time, and enhance the overall sustainability of airport operations. To efficiently address the computational complexity of this integrated modeling framework, a customized multi-objective particle swarm optimization (MOPSO) algorithm is proposed, augmented by a tabu search (TS) strategy. The TS algorithm provides high-quality initial solutions for MOPSO and performs local intensification on elite particles, thereby enhancing both convergence speed and solution quality. Extensive numerical experiments demonstrate that the proposed hybrid approach significantly outperforms the standalone MOPSO algorithm, achieving a 26.37% improvement over the original gate assignment scheme and a further 1.25% improvement compared to the standalone MOPSO, confirming the effectiveness and practicality of the proposed method. Full article

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems. Full article

This paper presents an integrated mathematical model to improve the safety and operational efficiency of train traffic in centralised railway dispatching systems. The proposed approach combines the alternative graph model with a Mealy automaton to synchronously address route planning, delay minimisation, and strict compliance with safety requirements. Formal control theory based on finite-state automata is employed to describe routing logic and signal control through state transitions, while the alternative graph model represents scheduling constraints and resource conflicts. To enhance real-time adaptability, a tabu search algorithm is implemented for train schedule optimisation, enabling dynamic rescheduling under changing operational conditions. The mathematical formulation incorporates blocking time parameters, a system of discrete constraints, and automaton-based safety conditions governing train movements and route authorisation. The integrated model explicitly formalises the processes of block section occupation and release, ensuring consistency between control logic and scheduling decisions. Practical testing and computational experiments demonstrate that the proposed approach effectively reduces train delays, improves the reliability of dispatch control, and increases system resilience to dynamic disturbances. The results confirm that the developed model can be implemented within existing centralised dispatching infrastructures without requiring a complete system overhaul. Overall, the proposed framework expands the functional capabilities of centralised dispatch systems by enabling efficient schedule generation, minimising the propagation of delays, and ensuring reliable command exchange between central control posts and field-level railway infrastructure. Full article

Controlled islanding is the last line of defense to prevent blackouts in power systems. This paper proposes a novel optimal splitting sections searching method for power systems with grid-forming (GFM) wind turbines, based on branch transient potential energy. First, an improved generator internal node potential energy is defined to uniformly characterize the transient energy accumulation of both synchronous generators and GFM wind turbines; coherent generator groups are then identified using K-means clustering. Second, a splitting sections searching model is formulated with the objective of minimizing the sum of branch stability indices (BSIs) on the splitting sections. An island inertia constraint is introduced as a penalty term to address the reduced system inertia caused by grid-following (GFL) wind turbines. An improved biogeography-based optimization (BBO) algorithm integrated with tabu search (TS) is employed for the solution. Finally, simulations are conducted on a modified New England 39-bus system. The results demonstrate that, compared to traditional models focusing on power imbalance or power flow disruption, the proposed method achieves better frequency and voltage stability in the formed islands, although this improvement comes at the cost of increased load shedding in certain scenarios. In power systems with GFM wind turbines, both frequency and voltage deviations are reduced, thereby validating the effectiveness of the proposed method in enhancing island stability. Full article

The city of Kinshasa faces severe traffic congestion, requiring strategic enhancements to its transport infrastructure capacity. Although a comprehensive transport master plan has been proposed by the Japanese International Cooperation Agency (JICA) report its implementation requires substantial financial investment, which presents a significant challenge for the resource-constrained environment of the Democratic Republic of Congo. This research proposes an indicative optimisation-based network augmentation strategy that accounts for traveller equilibrium behaviour, the primary origin–destination demand patterns, and the underlying network structure. The study formulates the problem as a bilevel Transport Network Design Problem (TNDP) under a construction length budget constraint. Greedy-Simulated Annealing and Greedy-Tabu Search are proposed as the recommended computational search approaches, as they achieved the highest travel time reductions in the experimental study while also demonstrating stable and repeatable solution performance compared with other classical metaheuristic methods commonly used in TNDP research. Greedy Simulated Annealing and Greedy-Tabu Search are proposed as the recommended computational search approaches, as they achieved the highest travel time reductions in the experimental study while also demonstrating stable and repeatable solution performance compared with other classical metaheuristic methods commonly used in TNDP research. The computational experiments indicate a 30% reduction in total travel time, accompanied by a substantial decrease in highly congested links from 52.94% in the baseline network to 3.45% in the optimised design and nearly threefold improvement in edge betweenness centrality for a 100 Km constrained budget. The study further provides recommended new link constructions, together with alternative network redesign solutions that achieve comparable performance improvements. Full article

This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines the most suitable architecture for regression problems. The methodology implements an algorithm based on Tabu Search, called the Best R-Value Determination algorithm (BR-vD), which optimizes the topology obtaining a lower Mean Square Error (MSE) and a higher correlation coefficient. The process is developed in three phases: first, the variables that impact the travel time are analyzed; then, the proposed algorithm is used to find the best topology; and finally, the travel times are estimated. The proposal is evaluated in two case studies: in the first, the algorithm automatically designs the architecture, and a 0.99366 correlation coefficient is achieved between the results and the objectives. In the second case, the performance of the algorithm is compared with a fuzzy travel time model, achieving a 0.99898 correlation coefficient. In both cases, the proposed algorithm is capable of designing topologies with coefficients greater than 0.99 and Mean Absolute Errors (MAEs) of 3.2765 and 0.6957 s, respectively. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

This paper addresses single-machine due-window assignment scheduling with truncated learning effects and past-sequence-dependent setup times. In practical production systems, truncated learning effects capture the ceiling of skill improvement, past-sequence-dependent setup times reflect sequence-dependent switching efforts, and position-dependent weights allow varying importance across job positions. The due-window assignment includes the common, slack, and different assignments. The objective cost is the minimum of the weighted sum of earliness and tardiness, the number of early and tardy jobs, due-window cost, and the completion time. In which the weights are position-dependent. For the common and slack due-window assignments, several optimal structural properties are established. Based on these, the optimal schedule can be derived by solving a series of assignment problems, i.e., the problems can be solved in polynomial time $O\left(n^5\right)$, where n is the number of jobs. Under the common, slack, and different assignments without the number of early and tardy jobs cost, the optimal schedule of the problems can be obtained from an assignment problem, i.e., the problems can be solved in $O\left(n^3\right)$ time. In addition, an extension of the job-dependent processing times is given. This study extends existing research models in this domain and proposes polynomial-time algorithms that guarantee optimal solutions for minimizing the objective cost function. The proposed approach not only advances scheduling theory by handling multiple realistic constraints simultaneously but also offers a practical decision-making tool for just-in-time production systems. The algorithms are tested numerically and compared with simulated annealing algorithm and tabu search algorithm. Full article

The calibration of suspension geometry involves highly nonlinear kinematic relationships and leads to challenging optimization landscapes that are difficult to explore efficiently with classical local methods. Quadratic Unconstrained Binary Optimization (QUBO) provides a unified discrete formulation that enables the use of a wide range of metaheuristic solvers, but its practical behavior in realistic engineering-inspired problems remains insufficiently benchmarked. This paper presents a computational benchmarking study of classical, parallel, and hybrid metaheuristic solvers applied to a QUBO-formulated double wishbone suspension geometry problem. A symbolic multi-body kinematic model is constructed and discretized into a large-scale QUBO instance capturing camber and caster tracking objectives across multiple roll conditions. Using a fixed low-resolution binary encoding, we systematically evaluate solver performance in terms of objective value, runtime, and time-to-solution trade-offs. The benchmark includes standard simulated annealing and tabu search, parallel simulated annealing, population-based annealing, bandit-controlled hybrid heuristics, and continuous-relaxation-based ADMM methods with and without annealing refinement. Extensive experiments conducted on a Euro-HPC pre-exascale system demonstrate that parallel and hybrid solvers can achieve substantial reductions in wall-clock time—often exceeding two orders of magnitude—while attaining objective values comparable to classical simulated annealing. The results reveal clear trade-offs between solution quality and computational efficiency, and highlight how solver structure influences performance on large QUBO instances derived from symbolic engineering models. Rather than focusing on final design optimality, this study provides a reproducible reference benchmark and practical insights into solver behavior for QUBO-based engineering optimization problems. Full article