Search Results (96)

Unmanned aerial vehicles (UAVs) are increasingly employed for parcel logistics while simultaneously serving as aerial communication platforms. However, jointly optimizing pickup-and-delivery operations and wireless communication raises a large-scale mixed-integer nonlinear programming problem due to the coupling of binary logistics decisions, trajectory planning, time allocation, user scheduling, and transmit-power control. This paper proposes a two-phase optimization framework that enables a dual-purpose UAV mission by jointly considering parcel pickup-and-delivery and downlink communication within a single framework. The key strength of the proposed approach is that it separates the logistics-dominated delivery stage from the communication-oriented service stage, thereby reducing the difficulty of directly handling the highly coupled MINLP while exploiting the residual mission time for communication enhancement. In Phase 1, a pickup-and-delivery optimization problem is formulated to minimize the delivery completion time by determining the UAV trajectory, time-slot lengths, and item handling sequence, where the binary pickup/drop-off decisions are relaxed and progressively enforced through a penalty convex–concave procedure. In Phase 2, communication performance is enhanced by optimizing user scheduling and transmit power over the entire mission horizon, together with residual flight trajectory refinement after delivery completion using successive convex approximation and block coordinate descent. Simulation results show that the proposed algorithm substantially improves the minimum average spectral efficiency among ground nodes while achieving early completion of logistics tasks. Compared with baseline strategies, the proposed method delivers consistent performance gains under various system parameters. In particular, it improves the minimum average spectral efficiency by up to $15\%$ compared with the baseline that removes the proposed post-delivery trajectory refinement, demonstrating the benefit of exploiting the residual flight trajectory for communication enhancement after delivery completion. Full article

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

This paper addresses the Pickup and Delivery Problem with Time Windows (PDPTW), an NP-hard combinatorial optimization problem with major practical relevance in logistics and transportation. The study focuses on a quadratic unconstrained binary optimization (QUBO) formulation for quantum annealing and benchmarks it against two classical optimization paradigms. A modular Python framework is developed that encodes PDPTW in three ways: a mixed-integer linear programming (MILP) model that serves as an exact reference, a genetic algorithm (GA) metaheuristic, and a QUBO model that is compatible with quantum annealers. The framework supports test scenarios with increasing structural complexity, with both feasible and intentionally infeasible instances. An additional contribution is the conceptual design and preliminary analysis of an automatic-penalty weight-tuning scheme for the QUBO model. Experimental results show that the proposed QUBO formulation can produce high-quality solutions for simpler PDPTW instances, but its performance strongly depends on the careful calibration of penalty weights. MILP provides optimal baselines on small instances but becomes intractable as problem size grows. The GA scales to the largest scenario and finds feasible solutions of reasonable quality, but they are not necessarily optimal. The evaluation also includes a large number of problem instances and runs on IBM Quantum hardware using the Quantum Approximate Optimization Algorithm (QAOA). Full article

In the context of sustainable logistics planning, carbon emission costs have become a critical factor influencing distribution decisions. Meanwhile, the time-dependent characteristics of urban road networks and simultaneous pickup–delivery operations present significant challenges to vehicle routing problems (VRPs). This study addresses a time-dependent vehicle routing problem with simultaneous pickup–delivery and time windows (TDVRPSPDTW). Fuel consumption and carbon emission costs are quantified using a comprehensive emission model, while time-dependent network conditions, simultaneous pickup–delivery demands, and time window constraints are integrated into a unified modeling framework. To solve this NP-hard problem, an improved ant colony optimization (IACO) algorithm is developed by incorporating adaptive large neighborhood search to enhance solution diversity and convergence efficiency. Computational experiments are conducted using internationally recognized VRPSPDTW benchmark datasets and newly constructed TDVRPSPDTW instances, together with sensitivity analyses under varying traffic conditions, time window flexibility, and delivery strategies. The results indicate that the proposed IACO effectively addresses the TDVRPSPDTW. Comparing ant colony optimization with local search (ACO-LS), the IACO achieves a maximum reduction of 11.78% in total distribution cost. Furthermore, relative to the conventional separate pickup–delivery strategy, the simultaneous pickup–delivery mode reduces total distribution cost and carbon emission cost by 49.96% and 53.48%, respectively. Full article

Simultaneous optimization of vehicle routing and cargo loading is essential for reducing operational costs and improving the environmental performance of logistics systems. To overcome the limitations of traditional sequential approaches to the one-to-many pickup and delivery vehicle routing problem with three-dimensional loading constraints (3L-PDVRP), this paper proposes a deeply coupled hybrid genetic algorithm (HGA). The algorithm adopts a grouping-based genetic encoding strategy to accommodate variable fleet sizes and incorporates a tree-search-based loading module. A dynamic three-dimensional loading feasibility verification mechanism is embedded directly into the evolutionary search so that routing decisions are continuously guided by fragility, stacking stability, support constraints, and other loading constraints. In addition, an adaptive hybrid insertion strategy is employed to balance global exploration and local exploitation during route construction and repair. Extensive computational experiments on extended benchmark instances derived from standard datasets show that the proposed method consistently outperforms a large neighborhood search (LNS)-based baseline from the literature, reducing the average total travel distance by 10.60% and increasing the average vehicle loading rate by 2.76%. These results indicate that the proposed HGA provides an effective approach to the synergistic optimization of routing and loading in one-to-many distribution settings, offering practical value for lowering transportation costs and supporting more sustainable logistics operations. This methodology provides decision support for logistics enterprises, reducing travel distances while ensuring three-dimensional loading feasibility, thereby enabling greener and safer transportation operations. Full article

This study examines the logistical challenges arising in omni-channel retailing, where the interaction between traditional stores and online channels requires flexible and efficient transportation planning. In particular, the growth of Buy-Online-and-Pick-up-in-Store (BOPS) services has intensified the need to manage both forward deliveries and customer returns, the latter being a costly component of reverse logistics. To address these challenges, this study introduces the Shared Capacity Vehicle Routing Problem with Simultaneous Pickup and Delivery (SCVRP-SPD), which minimizes total operational cost by considering both transportation costs and the additional transfer costs incurred when reallocating store visits to more efficient delivery paths. In the SCVRP-SPD, stores are designed to serve a dual role as both pickup and return points, and a shared-capacity mechanism is incorporated to utilize leftover capacity in pre-planned trips, improving efficiency while reducing overall logistics cost. A mixed-integer programming model is developed for the problem, and solutions are obtained using GUROBI (version 11.0) and a newly designed Modified Differential Evolution (MDE) algorithm. Numerical experiments are conducted to evaluate the performance of the proposed MDE algorithm and to generate managerial insights, showing that the SCVRP-SPD is a promising strategy for omni-channel retailers seeking to reduce transportation costs, streamline reverse logistics, and better utilize resources. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

This paper investigates a communication-constrained unmanned aerial vehicle (UAV) pickup and delivery system for continuous multi-period operations. To ensure real-time control updates between UAVs and the ground server, a minimum communication rate requirement is imposed throughout each mission. The objective is to minimize the average mission completion time of multiple rotary-wing UAVs while satisfying mobility, payload, safety, and communication constraints. The resulting mixed-integer nonlinear programming problem, involving binary pickup/drop-off decisions, trajectories, and variable time-slot durations, is mathematically intractable. To address this, a successive convex approximation framework combined with a penalty convex–concave procedure is developed, enabling iterative convex reformulation and convergence to a near-optimal binary-feasible solution. Simulation results demonstrate that the proposed algorithm efficiently generates collision-free trajectories and adaptive flight paths that maintain reliable communication links, outperforming baseline strategies in terms of completion time and coordination efficiency under communication constraints. Full article

Urban logistics in smart cities are increasingly challenged by congestion, sustainability pressures, and the growing demand for resilient delivery systems. To address these challenges, this study introduces the Multi-Visit Time-Dependent Truck–Drone Routing Problem with simultaneous Pickup and Delivery (MTTRP-PD), a novel framework that integrates three realistic features: (i) drones serving multiple customers per sortie, (ii) time-dependent truck speeds reflecting dynamic traffic conditions, and (iii) synchronized pickup and delivery between trucks and drones. By incorporating these elements, the proposed model provides a more realistic and comprehensive representation of urban air-ground collaborative logistics in the last mile. An optimization framework and an efficient solution approach are developed and validated through computational experiments. The results demonstrate that enabling multi-visit sortie and simultaneous pickup–delivery operations can significantly reduce logistics costs compared with conventional single-visit or delivery-only strategies. Sensitivity analyses further reveal the critical influence of dynamic traffic conditions on fleet configuration and operational decision making. The findings offer actionable insights for logistics operators and policymakers, illustrating how coordinated UAV–truck collaboration can enhance efficiency, resilience, and environmental sustainability in next-generation urban logistics systems. Full article

This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. The MILP model generates optimal routing and task allocation plans, which are subsequently stress-tested under realistic uncertainties, such as variability in travel and service times, using ABS implemented in AnyLogic. The framework is iterative: violations of temporal or capacity constraints identified during the simulation are fed back into the optimization model, enabling successive adjustments until robust and feasible solutions are achieved for real-world scenarios. Additionally, the study incorporates transshipment scenarios, evaluating the impact of using warehouses as temporary hubs for order redistribution. Results include a comparative analysis between deterministic and stochastic models regarding operational efficiency, time window adherence, reduction in travel distances, and potential decreases in CO₂ emissions. This work provides a contribution to the literature by proposing a practical and robust decision-support framework aligned with contemporary demands for sustainability and efficiency in urban logistics, overcoming the limitations of purely deterministic approaches by explicitly reflecting real-world uncertainties. Full article

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks. Full article

Rural areas often lack convenient delivery logistics services, which has become a major obstacle to their economic development. Network design initiatives that synergize passenger and freight transport have been identified as effective solutions to address this challenge. Building upon this initiative, this study investigates a novel three-echelon location-routing problem that synergizes trucks and fixed-route buses (3E-LRP-TF). The model is designed with an innovative operational mode that enables fixed-route buses and trucks to travel in a parallel manner, representing a valuable extension to traditional integrated passenger–freight distribution network design. A mixed-integer nonlinear programming model with the objective of minimizing the total network cost is constructed to formulate the problem. Furthermore, a bottom-up three-phase adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem. A final empirical study was conducted, with Qingchuan County in China serving as a case study, with the aim of validating the effectiveness of the proposed model and algorithm. The results show that, compared with using trucks alone, the synergistic network system has the potential to reduce costs by more than 5% for parcel pickup and delivery services. The proposed algorithm can address larger-scale problems and exhibits better performance with regard to solution quality and efficiency. Sensitivity analysis indicates that the parcel transport capacity of bus routes exerts a nonlinear effect on total costs, and changes in service radius result in trade-offs between cost and accessibility. These findings provide actionable insights for policymakers and logistics operators. Full article

Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties. Full article

This study proposes a bi-objective optimization model for the inventory routing problem with pickup and delivery (IRP–PD) in a closed-loop supply chain, addressing the growing demand for sustainable logistics solutions. The model simultaneously minimizes transportation costs and inventory costs and enhances driver well-being by incorporating regular rest breaks. The network operates within a circular economy framework, where pallets are both delivered and returned for reuse, contributing to waste reduction. A normalized weighted-sum method is initially used to balance the conflicting objectives. However, since the model cannot efficiently solve large-scale instances, we adopt the NSGA-II metaheuristic to generate a Pareto front, enabling decision-makers to explore trade-offs between objectives. The model is tested on a single instance, and the results demonstrate a promising compromise between economic and social goals. Full article

Adaptive Large Neighborhood Search (ALNS) represents a versatile and highly efficient optimization methodology that has demonstrated significant effectiveness in practical applications. This study introduces an enhanced ALNS approach integrated with Simulated Annealing (SA), termed IALNS-SA. The proposed algorithm incorporates supplementary destruction and repair operators within the ALNS framework to augment its robustness and generalization capacity. Additionally, it adopts the SA acceptance criterion to mitigate local optima entrapment. The research investigates the applicability of IALNS-SA to the Vehicle Routing Problem with Simultaneous Delivery and Pickup and Time Windows (VRPSDPTWs), a pivotal challenge in logistics optimization. Through comprehensive evaluation across 56 large-scale benchmark instances, the algorithm’s performance is systematically compared against four established methods: p-SA, DCS, VNS-BSTS, and DGWO. Empirical results indicate that IALNS-SA achieves superior performance relative to DGWO in 69.64% of cases, surpasses VNS-BSTS in 94.64% of instances, and consistently outperforms both p-SA and DCS. The obtained optimal solutions exhibit reduced total vehicle routing distances, thereby substantiating the operational feasibility and algorithmic efficacy of the proposed methodology. Full article