Search Results (521)

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article

This paper studies a bi-objective distributed flexible job shop scheduling problem considering machine on–off decisions. A mathematical model is formulated to minimize the makespan and total energy consumption while distinguishing processing energy, idle energy, and on–off energy. To address the coupled effects among job-to-factory assignment, machine selection, operation sequencing, and machine on–off states, a domain-knowledge-driven memetic algorithm (DKMA) is proposed. The algorithm represents each schedule with a three-layer encoding scheme and integrates hybrid initialization, knowledge-driven neighborhood search, and energy-saving reconstruction to improve solution-set quality and the use of on–off-eligible idle intervals. The proposed model and algorithm are evaluated through Taguchi parameter tuning, small-scale mixed-integer linear programming (MILP) validation, component ablation experiments, and multi-algorithm comparisons. The results show that DKMA improves solution-set coverage, Pareto-front approximation, and energy control on the tested instances, which supports its applicability to distributed green scheduling with machine on–off decisions. Full article

Grid-based multi-agent path finding (MAPF) solvers scale to large teams, but their discrete schedules may not provide high-quality continuous finite-radius motions near the square-grid corner-passing threshold. We study endpoint-time-preserving geometric smoothing for disk agents at radius $0.35$. We establish an embedded-graph corner-passing threshold for synchronized finite-radius local passes and derive the square-grid radius $r_c=\sqrt{2}/4$. Finite-radius realizations are formulated as Lipschitz trajectories, and we prove that standard four-neighbor schedules without vertex conflicts or head-on edge swaps are pairwise continuously feasible up to this threshold. The smoother replaces windows by shortcuts only when speed, obstacle-clearance, pairwise continuous-collision detection, and length checks pass. Accepted shortcuts preserve endpoint times, schedule-level makespan, discrete arrival records, and discrete sum-of-costs while enforcing geometric length non-increase; the strict-decrease subset yields the reported geometric sum-of-costs reductions. Across six MovingAI map settings, LaCAM solves 575 benchmark instances; 570 smoothed trajectories pass finite-radius validation, with median geometric sum-of-costs reductions of 9.9% on the main slice and 11.2% on the five-map extension. A targeted 100-agent radius sweep further supports the threshold interpretation by showing a clean feasibility transition around the predicted corner-passing radius. The results support time-preserving smoothing as a validated geometric-quality layer for scalable grid planners. Full article

Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance. Full article

Underground mine production scheduling under uncertainty is a complex and multi-field coupling system project. In this study, underground mine production scheduling seeks to determine the optimal start time of extraction-related projects, with the objectives of maximizing net present value, minimizing makespan, and maximizing resource utilization rate. The Copula function is adopted to formulate the correlation between uncertain project duration and cost and generate a set of stochastic scenarios. Then, the K-means algorithm classifies the scenarios into multiple scenario families, and the SBR algorithm is adopted to perform scenario reduction. Moreover, a rank choice function-based hyper-heuristic algorithm is extended to solve the multi-objective optimization model, which makes an excellent balance among the three objective functions. For determining the optimal scheduling plan, the cross-efficiency DEA algorithm is used to evaluate the archive set, sort the optimal solution, and guide the next iteration. The computational case verifies the effectiveness and efficiency of the multi-objective underground mine scheduling model, stochastic scenario and technical and hyper-heuristic algorithm. Full article

Traditional job shop scheduling research primarily focuses on regular performance measures such as makespan. However, in a Just-in-Time (JIT) production environment, the objective shifts toward minimizing non-regular measures, specifically the weighted sum of earliness and tardiness (E/T) penalties, as excessive earliness leads to increased work-in-process inventory costs. Addressing the JIT scheduling problem in Assembly Job-shop Scheduling Problem (AJSP) is challenging, as traditional genetic algorithms (GAs) often suffer from premature convergence due to the randomness of initial populations. This paper proposes an Improved Genetic Algorithm (IGA) based on a multi-rule collaborative initialization mechanism. The algorithm explicitly incorporates assembly tree structure constraints during the encoding phase. For population initialization, a hybrid strategy is designed by integrating forward scheduling, backward scheduling, and forward-scheduling-based delay adjustment rules to ensure both the quality and diversity of the initial solutions. Simulation experiments and ablation studies demonstrate that the proposed IGA consistently achieves lower total weighted costs across various problem scales compared to standard algorithms. The results validate that the collaborative initialization strategy effectively balances global exploration and local exploitation, providing a robust solution for AJSP under JIT constraints. Full article

Efficient cooperative task allocation is essential for multiple unmanned aerial vehicles (UAVs) performing complex missions. However, diverse dynamic events in real-world scenarios require rapid response through dynamic task allocation (DTA). Although evolutionary algorithms have been widely adopted for DTA, existing methods often fail to maintain consistency between allocation decisions and actual operational states, consider only limited classes of dynamic events, and still leave room for performance improvement. This paper formulates multi-UAV DTA as a dynamic multi-objective optimization problem (DMOP) that jointly minimizes the residual target value and mission makespan, incorporating a state inheritance mechanism and a comprehensive set of dynamic events covering multiple facets of disruptions in observation task scenarios. To solve this DMOP, a multi-swarm dynamic crow search algorithm for task allocation (MDCSATA) is proposed, which integrates five strategies: violation-tolerant multi-swarm co-evolution for feasibility and diversity; objective-oriented heuristic initialization to accelerate convergence; an adaptive position update for better exploration and exploitation; stagnation and elite guided perturbation for intensified local exploitation; and an event-aware change response for rapid adaptation to dynamic events. Experiments on three constructed scenarios against seven state-of-the-art algorithms show that MDCSATA achieves superior performance on the evaluation metrics with acceptable runtime. It obtains the best MHV and MIGD in all scenarios, improving MHV by at least 0.93% and reducing MIGD by at least 12.92% across scenarios. These results confirm its effectiveness for DTA. Full article

Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper proposes the multiple flying sidekick traveling salesman problem with variable drone speed (mFSTSP-VDS). Formulating drone cruising speed as a continuous variable under strict non-linear energy constraints, we design a hybrid algorithm (ALNS-SA-VND) to jointly optimize routing, task allocation, and speed. Empirical analysis of Wuhan’s road network demonstrates the VDS strategy’s robustness. Specifically, VDS reduces the system makespan by up to 17.5% compared to rigid maximum-speed strategies, with consistent stability across varying load scenarios. By adaptively trading permissible battery capacity for temporal synchronization, VDS effectively mitigates unnecessary truck waiting times at rendezvous nodes. This study quantitatively validates the impact of sortie-specific speed adaptation on time efficiency, providing an exploratory theoretical baseline for tactical-level planning in smart logistics networks. Full article

The job shop scheduling problem (JSSP) is a paradigmatic and strongly $\mathcal{NP}$-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600 s wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from $6\times 6$ to $1000\times 1000$ operations. OptalCP emerges as the most robust engine overall, certifying optimality on 191 of the 332 instances ($57.5\%$) with the smallest average optimality gap ($3.55\%$), followed by CP Optimizer (166 optima), OR-Tools (144), and Hexaly (116), while Hexaly dominates on industrial-scale problems and produces the bulk of the 22 new best-known upper bounds and one new best-known lower bound reported here. A Friedman test followed by Nemenyi post hoc comparisons confirms that OptalCP attains significantly smaller optimality gaps than the three other engines ($p<0.001$). Solver competitiveness depends sharply on instance size and the $n/m$ ratio, with square instances confirmed as the hardest case. In practical terms, these findings support an instance-aware approach to CP solver selection: OptalCP is the default choice for small to large instances of moderate aspect ratio, whereas Hexaly is preferable for industrial-scale problems with tens of thousands of operations or extreme $n/m$ ratios, where it is the only engine that reliably returns high-quality feasible schedules within the time budget. Full article

In flexible job shop scheduling, industrial robots undertake both workpiece transportation and loading/unloading operations. Equipping each machine with dedicated buffers tends to increase transportation workload and further intensify transport bottlenecks. Shared buffers are therefore introduced to temporarily store workpieces and relieve congestion in the production process. This paper establishes a transport-integrated flexible job shop scheduling model with shared buffer constraints, which minimizes makespan, total energy consumption, and machine load range simultaneously. Correspondingly, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed to achieve better solution performance. A time-window-based path-planning decoding scheme is constructed to address buffer constraints and transportation conflicts in the coordinated production and transportation process. In parallel, four initialization rules are designed to improve the quality and diversity of the initial population, and a variable neighborhood search algorithm (VNS) is embedded to enhance the local exploitation ability of the proposed algorithm. The performance of the presented method is evaluated through two groups of numerical experiments. The first group is carried out on extended benchmark instances. Comparisons with the conventional Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization algorithms (MOPSO) validate the efficacy of the proposed strategies and demonstrate the superiority of ENSGA-II in both solution quality and computational efficiency. Experimental results on real-world cases further illustrate that the proposed method can effectively solve the integrated scheduling problem in flexible manufacturing systems where industrial robots are employed as the main transport resources. Full article

In multi-variety, small-batch electric oven manufacturing, sequence-dependent setup times (SDST) and worker skill heterogeneity jointly affect makespan, labor cost, and energy consumption. This study addresses a multi-objective hybrid flow shop scheduling problem with SDST and multi-skilled worker assignment (HFSP-SDST), in which symmetry and asymmetry coexist: the three objectives require balanced trade-offs, whereas sequence-dependent setups and skill–speed compatibility impose asymmetric constraints. A mixed-integer linear programming model is formulated to minimize the three objectives, embedding a skill–speed downward compatibility mechanism that couples worker assignment with processing time, power demand, and labor cost. To solve it, a hybrid algorithm integrating NSGA-II, variable neighborhood search (VNS), and multi-objective simulated annealing (MOSA) is designed on a four-matrix encoding with problem-specific crossover, neighborhood, and feasibility-repair operators. On 24 test instances of varied scale and structure, NSGA-II-VNS-MOSA attains the highest mean hypervolume (2.05) and the best average rank (2.07) against classical and recent Q-learning-guided algorithms, with its advantage growing as setup asymmetry intensifies; an ablation study shows that VNS and MOSA jointly increase hypervolume by 89.5% and reduce the inverted generational distance (IGD) by 45.2% relative to baseline NSGA-II. A real electric oven case confirms that the resulting Pareto set offers decision-makers actionable trade-offs among the three objectives. Full article

As scientific workflows grow more complex and green computing becomes a priority, efficient multi-objective scheduling is essential to optimize makespan, cost, and energy consumption for large task graphs. However, existing methods often suffer from scalability bottlenecks and insufficient modeling of task dependencies, leading to degraded performance on large-scale workflows. This paper proposes HiGAT-AC, a framework that combines a hierarchical graph attention network with actor-critic reinforcement learning for scalable workflow scheduling in heterogeneous systems. HiGAT-AC splits large workflows into subgraphs via spectral clustering and uses a three-level hierarchy to capture local task dependencies, coordinate inter-subgraph information, and conduct global resource allocation. The actor-critic model employs Chebyshev scalarization to balance the three conflicting objectives. Experimental results show that HiGAT-AC achieves competitive composite scores across workflow scales from 500 to 1000 tasks, with scores reaching 0.954 on 500-task workflows and 1.000 on 1000-task workflows, while remaining stable above 0.70 across all scales. Compared with traditional and representative learning-based methods, HiGAT-AC exhibits favorable overall performance and relatively stable scalability on large task graphs, providing a promising solution for scientific workflow scheduling that balances performance and sustainability. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

The Flexible Job-shop Scheduling Problem (FJSP) plays an important role in production and processing in Smart Manufacturing Systems. Unlike the traditional Job-shop Scheduling Problem (JSP), the additional flexibility in machine selection enlarges the search space and increases scheduling difficulty, particularly for large-scale instances. Existing algorithms improve either convergence speed or solution quality, but maintaining both remains difficult as problem size grows. This paper presents a Hybrid Local Search Algorithm (HLS-FJSP), integrating Greedy Search, Genetic Algorithm, and Tabu Search into a two-phase optimization framework. Control parameters and a process monitoring mechanism are used to adjust the search behavior during different optimization stages. Computational experiments on benchmark instances show that the proposed method obtains competitive makespan results compared with several existing algorithms. The results also show stable improvement capability when used for further optimization of existing schedules. Full article

Efficient task scheduling in heterogeneous IoT–Fog environments is challenging due to limited fog resources, diverse task demands, and conflicting QoS objectives. This paper proposes ASQS, a Quality-Source-driven Multi-Anchor Synchronized Search algorithm for IoT–Fog task scheduling. ASQS is biomimetically motivated by collective search behaviors in natural systems, where distributed exploration, collective memory, and probabilistic cooperation support an exploration–exploitation balance. Specifically, ASQS constructs quality layers from candidate schedules, extracts representative quality-source anchors, and reuses them through an ACO-inspired probabilistic synchronization mechanism, thereby improving the utilization of high-quality historical search information. FNO-based search and Lévy-flight perturbation are further incorporated to enhance directional guidance and long-range exploration. Experiments on 33 benchmark functions, ablation studies, sensitivity analysis, standard fog scheduling scenarios, and large-scale task-intensive scenarios were conducted to evaluate ASQS. The results show that ASQS achieves competitive optimization accuracy, stable convergence, and superior comprehensive scheduling performance in terms of fitness, makespan, latency, load balance, and constraint handling. In particular, the large-scale experiment with 100 fog nodes and up to 8000 IoT tasks verifies the scalability of ASQS under heavy workload pressure. Statistical tests further confirm the reliability of the observed improvements. These results demonstrate that ASQS is an effective, scalable, and biomimetically motivated optimizer for IoT–Fog task scheduling. Full article