Search Results (61)

Predefined low-altitude corridors create a coupled routing–scheduling problem when multiple drone routes enter the same controlled segment. This study separates an upstream control hub from its scarce directed hub–segment resource and develops an event-expanded continuous-time mixed-integer linear programming model with optional fleet activation, complete-route energy and capacity checks, release precedence, minimum entry headway, holding, and downstream delay propagation. A headway-aware large neighborhood search (HA-LNS) combines route neighborhoods with a finite serial event decoder. Gurobi proves optimality on three small instances, and fixed-route timing MILPs exactly match the decoder, including for a repeated physical-hub visit. Across ten matched networks per scale, HA-LNS changes the mean objective relative to route-only LNS by 0.01%, 0.90%, and 2.33% at nominal scales 30, 50, and 100. Under high conflict-resource density, the reduction reaches 5.77%, while mean holding falls from 2.054 to 0.025 min. Simulated annealing is 1.04% better at scale 50 and statistically indistinguishable at scales 30 and 100, showing that the contribution is conflict-aware integration rather than universal heuristic dominance. The framework identifies directed-resource density as the main condition under which temporal coordination materially improves route decisions. Full article

Maritime emissions in Emission Control Areas pose significant environmental and health concerns, and effective monitoring is essential for regulatory compliance. This paper addresses the scheduling of drones as a remote sensing platform for inspecting vessel emissions in these areas. A mixed-integer linear programming framework is proposed based on a time-expanded network representation. The model incorporates three practical features of the inspection environment. First, vessels are assumed to follow uniform linear motion within a narrow approach channel, enabling a closed-form expression for drone flying times. Second, inspection sub-windows are introduced to account for exhaust plume interference that may affect detection accuracy. Third, a time-dependent weighting scheme is adopted to encourage early inspections. The resulting optimisation model is solved directly using Gurobi. Computational experiments on instances with 30 vessels and a fleet of 15 drones demonstrate that the proposed formulation achieves an average optimality gap of 0.01%, with an average solution time of 5.91 s. Sensitivity analyses reveal that fleet size beyond 15 drones yields negligible additional benefit, while raising drone speed from 20 to 35 knots enables multi-vessel inspection tours and yields a relative increase of 85% in the number of vessels inspected. The results indicate that the proposed framework provides effective support for drone-based vessel inspection scheduling in Emission Control Areas. Full article

Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment. Full article

In post-disaster relief operations, emergency fleets typically consist of vehicles with varying load capacities, travel speeds, and operating costs. These heterogeneous vehicles are prone to unexpected breakdowns during delivery, which can severely disrupt supply chains and delay urgent aid. Existing scheduling approaches, however, rarely account for fleet heterogeneity, real-time breakdowns, and the trade-off between delivery speed and cost within a unified framework. This paper addresses this gap by formulating the dynamic scheduling of heterogeneous emergency fleets as a two-stage mixed-integer programming model, where total transportation time and cost are simultaneously minimized. The key algorithmic contribution is a fuzzy robust adaptive multi-objective hybrid algorithm (FR-AMOHA) with three interconnected design components. First, a fuzzy evaluation-based pre-matching strategy uses entropy-weighted multi-criteria assessment to generate high-quality initial solutions. Second, a failure-resilient rescheduling module freezes system state upon breakdown detection and selects recovery plans via multi-dimensional resilience scoring to prevent cascading failures. Third, a Pareto-guided adaptive neighborhood search dynamically adjusts operator selection to balance time and cost optimization. Tests on 40 real-world instances with 50 to 1000 demand nodes show that FR-AMOHA achieves optimal inverted generational distance values on 17 out of 40 instances, improves hypervolume by 15% to 35% on average compared with other metaheuristics, and keeps computation times between 40 and 250 s, which is within acceptable limits for emergency decision-making. FR-AMOHA outperforms Gurobi and six leading metaheuristics in solution quality with comparable computational cost. 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 Annual Delivery Program (ADP) for Liquefied Natural Gas (LNG) represents a complex maritime inventory-routing problem that requires the precise synchronization of production and distribution. This study introduces a novel Mixed Integer Linear Programming (MILP) model designed to optimize vessel routing and scheduling over a one-year horizon under a direct-shipment assumption. The model minimizes total logistics costs, encompassing both fixed annual fleet costs and daily operating costs. The novelty of the model can be summarized in two aspects. First, it simultaneously optimizes several decisions: the assignment of frequency of deliveries to customers, the assignment of vessels to customers, cargo load sizes, and vessel routing and scheduling. The key distinction is that, unlike existing formulations that take the frequency of deliveries to customers as a fixed parameter, this frequency is itself a decision variable selected from a customer-specific discrete set; the selected frequency partitions the planning horizon into uniform windows and sets each delivery’s cargo load size to the exact demand accumulated over its window from daily demand data. Second, it incorporates several relaxations of selected variables and valid inequalities that enable us to solve the complex model for moderate size problems within a reasonable computational time using the exact optimization approach. Using this novel model, we carried out extensive numerical analysis based on cost and operational parameter scenarios and developed important insights for the characteristics of a solution to the problem. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions. Full article

This paper studies an integrated planning problem in tramp shipping operations, with a particular focus on the bunker–cargo trade-off in maritime logistics. In practice, bunker load and cargo capacity are mutually restrictive: carrying more bunker reduces available payload, while cargo load affects fuel consumption and subsequent bunker demand, jointly shaping operating profitability. To capture this interdependency and overcome the limitations of fragmented decision-making, we jointly optimize cargo selection, vessel scheduling, routing, and bunkering decisions. The problem is formulated as a profit-maximizing mixed-integer linear programming (MILP) model. To solve large-scale instances efficiently, we adopt Dantzig–Wolfe decomposition and develop a column generation algorithm. For the pricing subproblem, we design a customized dynamic programming-based labeling algorithm for the resource-constrained shortest path problem, enhanced by dominance rules and acceleration strategies. Computational experiments show that the proposed approach outperforms alternative algorithms in both solution quality and computational efficiency, especially for large-scale problems. Ablation experiments further quantify the value of key components, including fuel discretization and deterministic acceleration strategies. Scenario analyses under different fuel prices, freight rates, bunkering policies, and fleet structures illustrate how integrated optimization can balance bunker–cargo trade-offs and provide practical decision support for tramp shipping operators. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

The offshore wind energy sector requires efficient logistics to retrieve generated electricity using maritime mobile energy storage systems. This study addresses the maritime mobile energy storage scheduling problem to maximise the total net energy delivered to the onshore grid. The proposed approach utilises a mixed-integer linear programming framework. The mathematical formulation integrates a replicated port node mechanism to plan multi-trip operations over a continuous planning horizon. Additionally, the model accounts for energy transfer loss coefficients and incorporates a speed discretisation strategy to balance propulsion consumption against retrieved electricity. Numerical experiments based on simulated operational scenarios demonstrate the effectiveness of this method. The results indicate that expanding vessel storage capacity from 500 to 600 megawatt-hours eliminates the necessity for multi-stop trips, thereby reducing propulsion energy consumption from 270.79 to 73.65 megawatt-hours. Furthermore, increasing the fleet size from five to six vessels enables the full retrieval of available offshore electricity while decreasing fleet propulsion consumption to 91.08 megawatt-hours. The solver consistently achieves optimal solutions within an average of 0.88 s. Consequently, this framework provides operators with precise decision support for determining fleet capacity and configuring offshore energy retrieval networks. Full article

Intralogistic activities play a critical role in sustaining uninterrupted manufacturing in production systems. With the increased usage of autonomous mobile robots (AMRs) to feed the production systems; a complex problem structure has emerged that includes the simultaneous evaluation of the sizing of the robotic fleet, task assignment and scheduling, as well as feasibility analysis of the investment. In this study, a complete decision-support frame is proposed to decide the minimum number of robots, plan the time index robot-line assignments and calculate the Cost Ratio for multiline manufacturing systems without starvation. In the proposed method, the total robot travel time, plant layout, operation times and safety factors are given as inputs to the time-indexed mixed-integer linear programming (MILP). In the literature, the fleet sizing and the scheduling problems are mostly handled separately. These highly related problems are integrated into one frame in this study. The method is validated by utilizing two worst case scenarios for an uninterrupted operation with changeable batteries and mandatory charging break. The results demonstrate that charging strategies have a huge impact on the number of minimum robots, operational applicability and economic performance. Full article

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader–follower positioning under the battery constraints of ETs, charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the simplified Illinois interstate highway network are conducted to examine the effectiveness and efficiency of the proposed model. Numerical results show that incorporating platooning reduces the total operational cost by 7.6% relative to the non-platooning scenario. The findings also shed some light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers. Full article

With the rapid development of all-electric ships (AESs) and the growing emphasis on sustainable shipping, there is an increasing need for effective scheduling solutions that address the unique challenges associated with AESs, such as battery limitations and charging infrastructure constraints. However, existing studies primarily focus on simplified scenarios, overlooking the complexities inherent in multi-port and multi-vessel shipping networks. To bridge this gap, this paper develops a Mixed-Integer Linear Programming (MILP) model aimed at minimizing total operational costs, specifically targeting the scheduling optimization problem in heterogeneous fleet feeder shipping networks, while explicitly considering charging requirements and time window constraints. To tackle the computational challenges posed by large-scale and strongly constrained scenarios, this study designs an optimization algorithm based on Adaptive Large Neighborhood Search (ALNS), incorporating a two-stage strategy and a destroy–repair mechanism to progressively refine solutions. Based on data from the Yangtze River feeder network, numerical experiments demonstrate the feasibility and effectiveness of the proposed model and algorithm. Additionally, a sensitivity analysis on battery capacity explores the effects of variations in key technical parameters on all-electric ship utilization and overall operational costs. Full article

This paper investigates the joint scheduling problem of battery electric bus fleets and plug-in charging infrastructure in an urban transit system. The operation of an electric bus network is inherently a multi-component system, where vehicle assignment, battery energy management, and charger capacity decisions interact and jointly determine system performance and cost efficiency. To capture these interdependencies, we propose a system-level integrated scheduling framework that simultaneously determines bus trip assignments, charging event timing and duration, and charger utilization plans. The problem is formulated as a continuous-time mixed-integer linear programming model that minimizes the total system cost, subject to operational feasibility, battery state-of-charge dynamics, and charger capacity constraints. To enhance computational tractability, a Lagrangian relaxation-based decomposition approach is developed, coupled with a linear programming-based diving heuristic. Computational experiments on benchmark instances demonstrate that the proposed framework produces high-quality system-level schedules with substantially reduced solution time compared with directly using a commercial solver. A real-world case study based on a large charging station in Beijing shows that the optimized joint schedules reduce the required fleet size from 22 to 13 buses and the number of chargers from five to two, leading to a 38.3% reduction in total system cost. These results highlight the effectiveness and practical value of the proposed approach for the planning and operation of urban electric bus transit systems. Full article