Search Results (703)

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems. Full article

We present Dipper, a lightweight 64-bit block cipher with 96-bit and 128-bit key variants, built on a 28-round hybrid SPN–ARX structure. Each round applies a full-state key addition, sixteen parallel 4-bit GIFT S-boxes, four word-wise rotations, two 16-bit modular additions over half of the state, and the GIFT-64 bit permutation, combining the compact substitution layer of GIFT-style designs with the diffusion efficiency of ARX operations. We evaluate Dipper from both hardware and cryptanalytic perspectives under a single, fully open-source methodology. Round-based Verilog implementations were synthesized alongside PRESENT, GIFT, and SIMON variants using an identical Yosys + ABC + Nangate45 flow. Under this flow, Dipper-64/96 and Dipper-64/128 require 2498 and 2824 gate equivalents (GE), respectively, both falling between GIFT-64-128 (2191 GE) and PRESENT-128 (2963 GE); notably, Dipper-64/128 is more compact than PRESENT-128 at the same key size, despite incorporating an additional ARX diffusion layer. A broader comparison re-implements eleven established lightweight ciphers under the same flow, and post-place-and-route FPGA results on Lattice ECP5, measured software timings, and Cortex-M memory footprints support deployment across RFID, sensor-node, and edge-gateway scenarios. For differential resistance, we develop a mixed-integer linear programming (MILP) model that couples the exact GIFT differential distribution table with a Lipmaa–Moriai encoding of modular addition. Predicted and empirical differential probabilities agree tightly for reduced-round variants, while five-round trails reveal differential clustering. The security evaluation further includes proven-optimal linear trail bounds up to ten rounds, an exhaustive impossible-differential search bounding the longest distinguisher at five rounds, and experimental integral distinguishers of at most five rounds, leaving the 28-round cipher a margin close to 3× against the longest identified distinguisher. All RTL, synthesis scripts, reference implementations, and MILP models are released for full reproducibility. Full article

Bike-sharing systems (BSS) are increasingly adopted as sustainable urban mobility solutions; however, the station location problem has been tackled through isolated approaches—mathematical optimization, GIS-based topological inspection, or multi-criteria analysis—without a unified framework for data-scarce cities. This study presents a three-stage integrated methodology for optimal BSS network design, applied to the Municipality of Athens, a city with no pre-existing BSS. Stage 1 generates candidate bike stations through GIS analysis, with demand estimated via a transit-population composite proxy combining fixed-track transit ridership and population density, and road safety quantified via kernel density estimation of traffic accidents. Stage 2 formulates a Maximal Coverage Location Problem (MCLP) as a mixed-integer linear program (MILP) solved via the ε-constraint method, generating Pareto fronts across twelve scenarios defined by four temporal periods and three walking thresholds. Stage 3 applies the AHP-TOPSIS methodology to rank candidate Pareto-optimal stations across six criteria. The selected optimal configuration of 124 stations at a 300 m walking threshold achieves 94% weighted demand coverage with a stable year-round network. Road safety is the dominant AHP criterion (weight = 43.4%), reflecting Athens’ critical infrastructure gap. These results validate the framework’s applicability in data-scarce contexts and offer a transferable methodology for BSS planning. Full article

Decarbonising municipal solid waste (MSW) is a defining algorithmic challenge for smart cities: waste-to-energy (WtE), composting and material-recovery investments must be committed years ahead under deep uncertainty about household source-separation uptake and the governing policy instruments (landfill taxes, carbon prices, compost subsidies). We present a two-stage stochastic mixed-integer linear programming (MILP) framework, applied to the Attica region of Greece (Athens; 5122 t/day MSW) and calibrated to confirmed 2024 weighbridge data. The contribution is an integration strategy rather than a new technique: endogenous WtE capacity sizing (Special Ordered Sets of Type 2 (SOS2) piecewise-linear cost, economies-of-scale exponent 0.85), the bilinear capacity–build coupling linearised exactly by McCormick envelopes (one factor being binary), and Pigouvian shadow-price recovery of the optimal policy instruments are combined in a single-shot, gap-bounded MILP and embedded in a 10,000-run Latin-hypercube Monte Carlo layer over 12 parameters with Spearman sensitivity indices. The individual components are established; their joint formulation is, to our knowledge, new. The pipeline solves 10,415 MILP instances. Three results are policy-relevant: investment is robust to rollout uncertainty (VSS ≈ €0; EVPI ≈ €5.4 M, 0.15%); the carbon price alone explains ~80% of cost variance (ρ = +0.891); and, under the model’s calibration, the implied Pigouvian-optimal landfill tax (€1100–3300/t) indicates a binding landfill cap is needed to secure diversion. The framework transfers to any metropolitan MSW system facing decarbonisation and circular-economy mandates. Full article

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, medicine perishability, and flexible service times. The objective minimizes total travel and penalty costs for early or late services. We formulate a mixed integer linear program (MILP) model to optimally solve small instances and develop an effective greedy randomized adaptive search procedure (GRASP) to address large instances. GRASP includes a tailored construction heuristic with problem-specific local search operators in the local search phase. Numerical experiments conducted on newly generated instances demonstrate that while the MILP model provides optimal solutions for small instances, only GRASP is able to handle large-scale instances within a reasonable computational time. Sensitivity analyses allow us to examine the impact of the approach’s parameters, perishability of medicine, soft time windows, and care crew resources. Full article

Background: Perishable agri-food supply chains require logistics networks that remain economically viable despite fluctuations in demand, transportation costs, and market prices. This study develops and evaluates a two-echelon logistics network for the banana supply chain in Magdalena, Colombia. Methods: A mixed-integer linear programming model was formulated to maximise daily profit by jointly determining collection-centre activation and product flows among 14 producers, five candidate collection centres, and two commercial buyers. The deterministic solution was complemented by sensitivity analysis and 1000 Monte Carlo optimisation scenarios incorporating variability in demand, transportation costs, and selling prices. Results: Under nominal conditions, all five collection centres were activated, the full demand of 42,000 kg/day was served, and the optimal profit was USD 3093/day. Centres C1–C4 operated at full capacity, whereas C5 reached 42.9% utilization. Under uncertainty, the mean profit decreased to USD 1955.70/day, the mean unmet demand was 954.03 kg/day, and shortages occurred in 72.3% of scenarios. C1–C4 remained the network core, while C5 acted as a flexible contingency facility. Conclusions: The proposed framework reveals an efficiency–resilience trade-off overlooked by deterministic optimisation. Demand growth and capacity reductions are the principal operational risks, supporting investment in collection capacity and proactive demand management. Full article

The emergence of unmanned aerial vehicles (UAVs) introduces new opportunities for the design of intelligent and flexible transportation systems beyond traditional road-based logistics. This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities. A two-stage optimization framework is developed, combining demand pre-processing with a routing algorithm that determines fleet utilization and delivery schedules under operational constraints. The proposed framework utilizes a data pre-processing stage, which converts enterprise resource planning order records into delivery-ready item data, with a mixed-integer linear programming (MILP) routing model that assigns eligible spare parts to UAV trips and determines the use of a fixed fleet under payload, dimensional, service-time, and battery-related constraints. The approach is evaluated using real annual order data from a metal-industry plant, combined with simulated intra-day arrival profiles due to the absence of exact order-placement timestamps in the ERP records. The results indicate that UAV-based transportation can serve a substantial share of internal demand while achieving shorter delivery-response times for the modeled UAV layer under the simulated dispatch instances and significantly lower direct energy-related transportation costs compared with the existing pickup-based process. The results highlight the role of UAVs as a complementary transportation layer in controlled industrial networks, supporting the transition toward more responsive and intelligent future transportation systems. Full article

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources. Full article

As fixed charging stations (FCSs) approach saturation, mobile charging stations (MCSs) have emerged as a flexible complement. This study proposes a bi-level optimization framework that integrates dynamic siting of MCSs with coordinated pricing for both MCSs and FCSs. The upper level maximizes the operator’s total net profit by jointly deciding MCS deployment and spatio-temporal prices, while the lower-level models EV users’ charging choices via cost minimization. The bi-level problem is reformulated as a single-level mathematical program with equilibrium constraints (MPECs) by replacing the lower-level with Karush–Kuhn–Tucker (KKT) optimality and complementarity conditions. The nonconvexities are addressed using the Big-M method, auxiliary variables, and piecewise linearization. This reformulation converts the problem into a mixed-integer linear program (MILP). The case studies show that the proposed coordinated strategy substantially improves the operator’s total net profit compared with the fixed sitting benchmark. This improvement is mainly achieved by allowing the MCSs to respond to spatiotemporal demand variations. Compared with the MCS-only optimization benchmark, the increase in total net profit is marginal under the tested scenario. This result suggests that coordinated MCS–FCS pricing mainly improves the investor’s portfolio-level outcome by reducing internal competition between MCSs and FCSs, rather than by increasing the standalone profit of the MCSs. The proposed framework provides an optimization approach for coordinated MCSs and FCSs operation in saturated charging networks. Full article

The deployment of 5G technology represents a significant advancement in telecommunications, offering unprecedented speed, connectivity, and innovation opportunities. However, this progress comes at a significant cost for Public Land Mobile Network (PLMN) operators, who face challenges in meeting high Quality of Service (QoS) standards for optimal user experience while ensuring appropriate levels of security. This paper addresses the joint optimization of latency and resource consumption under security constraints within 5G networks, focusing on the Packet Data Unit (PDU) session path to ensure compliance with security and latency requirements. We propose an innovative approach in which access control rules are distributed across User Plane Functions (UPFs) in the network. The optimization problem has been formulated as a mixed integer linear programming (MILP) problem that aims to minimize round-trip latency and operational costs for PLMN operators. We evaluate the performance of our model using a discrete event network simulator (NS3). The simulation results demonstrate the effectiveness of our approach, particularly in scenarios with stringent latency requirements. Latency is reduced, and a lower session drop rate is maintained, especially in conditions of network congestion. These findings emphasize the importance of considering both QoS and security in the design of next-generation 5G networks. 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

With the increasing penetration of distributed renewable energy sources, such as photovoltaic and wind power, their strong randomness and volatility pose significant challenges to distribution network operation and control. Simultaneously, missing and noisy source-load data in practical distribution network operation further constrain the accuracy of optimization decisions. To address these issues, this paper proposes a data-driven optimization method that integrates low-rank limited-information reconstruction, WGAN-GP-based scenario generation, and source–storage–load coordinated dispatch. Firstly, a low-rank matrix completion model solved by singular value thresholding (SVT) is used to reconstruct incomplete photovoltaic and load profiles. Secondly, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is trained on the reconstructed dataset to generate renewable-output scenarios, and five representative scenarios are retained through conditional scenario matching and averaging. Finally, a mixed-integer linear programming (MILP) dispatch model is established by considering energy-storage operating constraints, demand response constraints, and time-of-use electricity prices. The numerical case uses 60 daily profiles with 24 hourly points per day and a 20% random missing-data setting. Case study results show that the proposed reconstruction method reduces the overall RMSE from 177.15 kW to 52.40 kW compared with zero-fill processing. The coordinated dispatch decreases the daily operating cost from 10,060.36 CNY to 9414.67 CNY, corresponding to a 6.42% cost reduction. The limitations of the single-test-day benchmark and simplified active-power dispatch validation are also discussed. 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

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

This paper presents a two-stage stochastic unit commitment model to mitigate the costs and reliability effects of high wind energy penetration into the power system. Wind energy variability/uncertainty is explored through this system. Mixed-integer linear programming (MILP) is used to solve the model and find optimal unit commitment and dispatch variables under uncertainty in wind conditions. The model champions reduced reliance on deterministic approaches, lowered costs, increased wind utilization, and provides reliable systems that can sustain 24 h. Wind energy is expected to constitute a significant share of future electricity generation portfolios; however, its inherent intermittency and variability often lead to mismatches between energy supply and demand. This uncertainty complicates generation scheduling decisions, particularly in determining which power plants to commit, their operating durations, and the optimal dispatch timing. Consequently, advanced optimization strategies are required to ensure efficient and reliable system operation. The proposed approach provides a structured, robust framework for optimal generation scheduling and resource allocation, even under limited wind availability. By enhancing the integration of wind energy into the power system, the method minimizes operational costs, improves resource utilization, and maintains system reliability and stability. Full article