Routing Cost Research Articles

Stochastic Vehicle Routing With Delivery Choice

ABSTRACTWe consider the problem of designing delivery routes for vehicles where the vendor has the choice of how much of the demand from a customer to fulfill. The customer demand is known a priori only as a probability distribution. Exact customer demand is known only after visiting the customer. Different customers are able to negotiate different prices for each unit of product with the vendor. Given a route, the objective is to decide at each customer location, how much demand to satisfy so as to maximize expected profit taking into account a linear penalty cost for unfulfilled demand and the vehicle routing costs. In this article, we develop several new structural results for this problem. We illustrate how these structural results can be embedded in different heuristic frameworks commonly used for deterministic vehicle routing problems. This helps develop efficient routes for a single vehicle as well as a multiple vehicle scenario for this stochastic variant. For small‐sized problems that allow for exhaustive enumeration, we demonstrate the effectiveness of the illustrated heuristic. For larger problem instances, based on structural results, we develop methods that allow the heuristic to run more efficiently than otherwise. Results are reported on instances based on benchmark instances drawn from literature for upward of 100 customers and vehicle capacity up to 600 units. Computational times needed to heuristically solve such problems are within 1 100 s.

Research on Route Optimization of RO-Ro Ship Based on Ant Colony Algorithm

This study is based on an ant colony algorithm for route optimisation of roll-on roll-off (RO-RO) vessels, aiming to minimise the total cost of RO-RO shipping routes. The model takes into account factors such as port berthing cost, fuel consumption and sailing time, and ensures the stability and efficiency of the optimisation results by adjusting the algorithm parameters such as pheromone initialisation, pheromone evaporation rate and ant colony size. The results of the study provide a practical solution for ro-ro ship route optimisation, which significantly reduces the operating costs and also provides key technical support for shipping companies to compete in the global market. In addition, the study demonstrates the potential of the optimisation model to reduce transport costs, improve shipping efficiency and reduce carbon emissions, providing an effective technical support for shipping companies in an increasingly competitive global market. Through this optimisation approach, shipping companies are able to better manage their route selection, resource allocation and operational processes, enhancing market competitiveness and promoting sustainable development of the shipping industry.

Dynamic assignment of delivery order bundles to in-store customers

Many larger grocery stores offer home delivery services. However, the delivery cost is usually high and such services are rarely profitable. One way of reducing cost is by outsourcing some orders to in-store customers for a compensation. While initially single orders were dynamically assigned to customers, companies started exploring the assignment of order bundles instead to reduce per-order compensation and exploit consolidation potential. We investigate the value of dynamic assignment of bundles in this work. To this end, we consider a setting where all orders are known and, over time, unknown in-store customers enter the system for a short time and offer transportation of bundles of orders for compensation. The store decides dynamically which bundle to assign to which in-store customer (if any). At the end of the time horizon, the remaining orders are delivered by a dedicated fleet of store employees. The goal of the store is to minimize the compensation prices together with the delivery cost. We propose a threshold-based policy with scenario-based tuning. Popularity and compensation price thresholds are determined a priori by solving a set of perfect information scenarios. In every state, bundles are only assigned if they are popular enough and the compensation is comparably low. The thresholds (i.e., popularity threshold and compensation threshold) are adapted over time to account for the decrease in assignment opportunities. We show the effectiveness of our policy in a comprehensive computational study and highlight the value of bundle assignments compared to assigning individual orders. We further show that our strategy not only reduces the compensation paid to in-store customers but also the final routing cost.

Districting in last‐mile delivery with stochastic customers

AbstractDue to the peculiarities of city streets, last‐mile logistics is typically organized using territory‐based routing approaches, which divide the city into a set of districts and assign drivers to deliver in one or more of them. This allows drivers to develop a deep understanding of the characteristics of each district, and clients benefit from consistent service. However, these advantages must be carefully weighed against the flexibility of daily customer assignments, which enable planners to maximize driver utilization and minimize routing costs. In this paper, we propose a new holistic framework for defining districts, considering the impact on the quality of logistics decisions and fleet capacity usage. Specifically, we address how districting decisions affect the demand distribution within each district. Computational experiments on both simulated and realistic instances demonstrated a significant reduction in costs compared to benchmark techniques.

A Multi‐Trip Vehicle Routing Problem With Release Dates and Interrelated Periods

ABSTRACTThis article proposes a new multi‐trip vehicle routing problem variant, originally motivated by a practical application, namely a car components distribution to repair centers (warehouses). The problem is named the multi‐trip vehicle routing with release dates and interrelated periods (MTVRP‐RDIP). The routes may start at different pre‐defined periods, called departure periods, and may have different durations. Thus, two routes that start at different periods may be active at the same period, leading to the so‐called interrelated periods. Moreover, a route may only start at a period if a vehicle is available, and the availability of the vehicle depends not only on the existing vehicles but also on the active routes at that time period. The clients are classified according to their importance and represent warehouses that require car components that are made available throughout the time horizon, thus leading to the consideration of release dates. Delays in satisfying client orders and not serving orders in the time horizon result in penalty costs that must be minimized. The objective to minimize also includes routing costs and vehicle utilization costs. Two metrics are defined to compute an order's delay, each leading to a different mixed integer linear model. Computational results showed that one model clearly outperforms the other and even this one is only suited to address the smallest instances. A matheuristic based on a rolling‐horizon process that iteratively solves the better‐performing model with fewer periods was designed to tackle the largest instances. The matheuristic can provide feasible solutions for all test instances in an efficient manner that are, on average, better than the ones provided by the model in most of the compared key performance indicators.

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

Branch and Price for the Stochastic Traveling Salesman Problem with Generalized Latency

We consider an extension of the symmetric traveling salesman problem with generalized latency that explicitly models uncertainty. The stochastic traveling salesman problem with generalized latency (STSP-GL) aims to choose a subset of nodes of an undirected graph and determines a Hamiltonian tour among those nodes, minimizing an objective function that is a weighted combination of route design and passenger routing costs. These nodes are selected to ensure that a predefined percentage of uncertain passenger demand is served with a given probability. We formulate the STSP-GL as a stochastic program and propose a branch-and-price algorithm for solving its deterministic equivalent. We also develop a local search approach with which we improve the performance of the branch-and-price approach. We assess the efficiency of the proposed methods on a set of instances from the literature. We demonstrate that the proposed methods outperform a known benchmark, improving upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that solutions of the stochastic model are both more cost-effective and robust than those of the deterministic model. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the Bundesministerium für Bildung und Forschung [Grant 03ZU1105FA]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0417 .

Passive unmanned maritime search and rescue routing method

Addressing the challenges inherent in passive unmanned search and rescue missions at sea, including difficulties in target identification, broad search areas, and slow route planning, a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning, an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the Whale Optimization Algorithm. The validity of the model is confirmed through designated scenario experiments, indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs. This innovation offers valuable decision-making support for the planning of future emergency maritime search and rescue operations in both military and civilian sectors.

Label free electrochemical detection of 2-aminoindane

This study describes a label-free voltammetric detection of 2-aminoindane (2-AI), a novel psychoactive substance, using graphite screen-printed electrode modified with an electrochemically oxidized cobalt bis(dicarbollide) derivative (CoK/G/SPE). Morphological, structural and electrochemical characterization is assessed by using a computational approach, scanning electron microscopy, infrared spectrometry and electrochemical techniques. The binding/recognition of 2-AI (as a primary analyte) and 2,5-dimethoxy-4-iodoamphetamine (DOI, as interferent from category amphetamines) is compared, with CoK as a monomer as well as electrochemically deposited on the G/SPE surface. The mechanism of selectivity is proposed. The experimental findings show that these two stimulants compete for the binding/recognition sites on the modified electrode surface, with the adsorption constant values of (2.62 ± 0.67) x 105 and (6.10 ± 1.32) x 105, a linear range of 19.9 – 119 μmol L–1 and 9.97 – 235 μmol L–1, and the limit of detection 7.2 μmol L–1 and 2.37 μmol L–1 for 2-AI and DOI, respectively. An important point considering the analytical applicability and significance of CoK/G/SPE for forensic analysis is the possibility to distinguish 2-AI from an amphetamine derivative in a two-component mixture. The CoK/G/SPE electrodes are successfully applied to determine 19.9 μmol L–1 2-AI in spiked model samples with recovery values of 85.3–94.8 % and relative standard deviations 10–21 %. Such reproducibility is satisfactory considering the low cost of the electrode fabrication route.

Provably efficient security-aware service function tree composing and embedding in multi-vendor networks

Multicast greatly benefits many emerging applications such as federated learning, metaverse, and data warehouse. Recently, due to frequent cyber-attacks, multicast services have tended to request rigorous security agreements, which likely differ among the destinations. To meet such agreements, one can employ security-aware service functions (SFs) to construct the security-aware SF tree (S-SFT) for multicast services. A security-aware SF can be provided by various vendors with diverse configurations and implementation costs. The multi-configured SFs and the various security agreements will add significant complexity to the deployment process of the security-aware multicast request. In this work, for the first time, we study how to effectively compose and embed an S-SFT over the network with multiple vendors. We formulate the problem of security-aware SFT composing and embedding. We develop a new technique called cost-security-centrality (CSC) based on the pigeonhole’ s principle and propose a heuristic algorithm called CSC-based S-SFT deployment (CSC-SD). Via thorough mathematical proofs, we show that CSC-SD is logarithm approximate. Extensive simulations show that CSC-SD significantly outperforms the benchmarks and reveal that more function sharing facilitates saving implementation cost, but more routing sharing does not indicate saving routing cost.

Dynamic pickup-and-delivery for collaborative platforms with time-dependent travel and crowdshipping

We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time-dependent travel times. While dedicated vehicles are available throughout the day, the availability of crowdshippers is unknown a priori and they appear randomly for only portions of the day. With an objective of minimizing the sum of routing costs, piece-rate crowdshipper payments, and lateness charges, we model the uncertainty in request arrivals and crowdshipper appearances as a Markov decision process. To determine an action at each decision epoch, we employ a heuristic that partially destroys the existing routes and repairs them under the guidance of a parameterized cost function approximation that accounts for the remaining temporal capacity of delivery vehicles. We benchmark our real-time heuristic with an adaptive large neighborhood search and demonstrate the effectiveness of our method with several performance metrics. In addition, we conduct computational experiments to demonstrate the impact of inserting wait time in the route scheduling and the benefit of explicitly modeling time-dependent travel times. Through our computational testing, we also investigate the potential of demand management mechanisms that facilitate many-to-one request bundles or one-to-many request bundles to reduce the cost to service requests.

An exact method for the two-echelon split-delivery vehicle routing problem for liquefied natural gas delivery with the boil-off phenomenon

In this paper we investigate a two-echelon vehicle routing problem for liquefied natural gas (LNG) delivery to determine how to transport LNG from an overseas production terminal to a set of import terminals by vessels, and transport the LNG from the import terminals to a set of filling stations either by tanker trucks or bunker barges. Some important features of this problem are that part of LNG will be evaporated during delivery and split deliveries are allowed at both import terminals and filling stations, which render the problem more intractable than those considered in most of the existing two-echelon vehicle routing studies. The objective is to find the optimal first-echelon and second-echelon delivery schemes to minimize the sum of the routing cost and boil-off cost. To solve the problem, we develop a customized branch-and-price-and-cut (BPC) algorithm incorporating a specialized labeling algorithm tailored to address the issues of LNG evaporation and split deliveries in solving the challenging pricing subproblems. To speed up the solution algorithm, we introduce some heuristic pricing strategies to quickly solve the pricing subproblems, and explore the (strong) k-path inequalities and subset-row inequalities to tighten the lower bound obtained by column generation. We conduct extensive numerical studies on simulation instances and a case study of LNG delivery in region along the Yangtze river, China to verify the effectiveness of the model and proposed algorithm. The numerical results demonstrate that our algorithm significantly outperforms CPLEX and the existing BPC algorithm on related topic, confirm the superiority of our integrated two-echelon solution method over its sequential solution counterpart, and illustrate that the locations of the production terminal and import terminals are highly related to the solution performance.

Efficient load balancer for local internet service providers based on operation research

Load balancing in internet services acts as a reverse proxy to distribute network bandwidth or application traffic across multiple servers. To decrease the internet route cost and to make the network intermediate devices more intelligent distance, number of hops, bandwidth capacity, equipment maintenance, power consumption ...etc must be considered. .. The aim is for the devices to have self-decision: acting upon data found in the network and transport layer protocols (Internet Protocol IP, Transmission Control Protocol TCP, File Transfer Protocol FTP, User Datagram Protocol UDP), and delivering the services to the secondary internet (wireless or optical fibers) ISPs. To achieve this target, the use of an operation research algorithm, such as linear programming, has been proposed to solve the problem of minimizing transport and distribution costs by developing and overcoming the transmission load cost of the path selection. The proposed EWRRLB (Efficient Weighted Round Robin Load Balancer) will assign different costs to each internet connection based not only on its capacity or priority but also on the cost of transmission paths. This allows load balancers to allocate the best economic path and the share of the bandwidth to certain connections.

Biogas utilisation – Life cycle assessment of enabling technology for transport biomethane - UK case study, Bore Hill farm Biodigester

Policies, implemented and being considered in the UK, promote the use of biogas from anaerobic digestion (AD); however, small-medium scale AD plants, the majority of the UK's AD plants, have little access to energy distribution networks. The opportunity for using biomethane (upgraded biogas) for transportation, is rewarded in the Renewable Transport Fuels Obligation (RTFO), offering the AD plants the lowest cost route to achieving the targets set out in the Biomass Strategy for biomethane. In this study, Life Cycle Assessment is used to demonstrate the environmental benefits of using biomethane for transportation (considering global warming potential and fossil fuel scarcity impacts), compared to a biogas-to-electricity scenario, where the gas grid is not accessible. The results show that biomethane scenarios have significant environmental advantages in selected impact categories, evidencing that biomethane for transportation can decarbonise AD industries and contribute to the Net Zero target in the UK. Biomethane scenarios show greater potential for capturing biogenic CO2. However, biogas upgrading technologies, evolving technically and economically, should be studied further through techno-economic assessments for the appropriate scale and application in AD operations. This study contributes to the UK AD industry and policy makers concerned with the decarbonisation of biogas-to-biomethane routes for the future.

Facile synthesis of ultrafine WO3 nanoparticles for highly sensitive acetoin biomarker gas detection

Facile synthesis of metal oxide semiconductors (MOSs) nanoparticles with ultrafine size is quite promising to improve gas sensing performance due to the large effect of the particle size on the electron depletion layer (EDL), however, which is currently difficult to realize. In this work, we synthesized ultrafine WO3 nanoparticles of 20 ∼ 30 nm via room-temperature hydrolysis with following annealing and investigated the gas sensing performance of the WO3 nanoparticles towards acetoin biomarker released by Listeria monocytogenes (LMs). The WO3 gas sensor displays high response (Ra/Rg = 32.0@5 ppm), excellent selectivity and low detection limit at 140 °C. The ultrafine size of WO3 comparable to its Debye length can lead to a larger variation of EDL thus a higher sensor response. Our work has provided a facile, low cost and large-scale synthetic route to constructing high-performance acetoin biomarker gas sensors and might contribute to the development for LMs detection.

Sustainable and efficient separation of ternary multi-azeotropic mixture butanone/ethanol/water based on the intensified reactive extractive distillation: process design, multi-objective optimization, and multi-criteria decision-making

It is of great significance and necessity to develop a sustainable and efficient separation scheme of butanone (MEK), ethanol (EtOH), and water, which are usually formed during the cinepazide maleate synthesis process. To the best of our knowledge, the systematic separation of this ternary multi-azeotropic mixture remains comparatively less studied, compounded by the significant challenges posed by its intricate composition comprising four azeotropes and three distillation boundaries. Therefore, a systematic approach by integrating the process design, multi-objective optimization, and multi-criteria decision-making (MCDM) methods was proposed to achieve the sustainable separation. Initially, three separation schemes, i.e., triple-column extractive distillation (TCED), double-column reactive extractive distillation (DCRED), and side stream intensified double-column reactive extractive distillation (SS-DCRED), were mainly explored via the thermodynamic and kinetic analysis. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was employed to optimize the process based on three objectives namely the total annual cost (TAC), CO2 emissions, and process route index (PRI). Finally, an efficient MCDM method combining CRITIC and TOPSIS was employed to rank the Pareto front solutions of the corresponding process design. Based on the comparison results, the optimal TCED process exhibits the lowest PRI with a decrease of 50.61% and 55.06% compared to that of the SS-DCRED and the DCRED, respectively. It also shows the best performance simultaneously considering economic, environmental, and safety criteria. Of note, the SS-DCRED separation process exhibited the lowest TAC, with a decrease of 60.67% and 11.45% compared to that of the TCED and the DCRED, respectively.

Local detouredness: A new phenomenon for modelling route choice and traffic assignment

This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.

Prize-collecting Electric Vehicle routing model for parcel delivery problem

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs’ route and usage cost and maximize collected profits (prizes) while satisfying EVs’ load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly.

The Electric Vehicle Routing and Overnight Charging Scheduling Problem on a Multigraph

In the electric vehicle (EV) routing and overnight charging scheduling problem, a fleet of EVs must serve the demand of a set of customers with time windows. The problem consists in finding a set of minimum cost routes and determining an overnight EV charging schedule that ensures the routes’ feasibility. Because (i) travel time and energy consumption are conflicting resources, (ii) the overnight charging operations take considerable time, and (iii) the charging infrastructure at the depot is limited, we model the problem on a multigraph where each arc between two vertices represents a path with a different resource consumption trade-off. To solve the problem, we design a branch-price-and-cut algorithm that implements state-of-the-art techniques, including the ng-path relaxation, subset-row inequalities, and a specialized labeling algorithm. We report computational results showing that the method solves to optimality instances with up to 50 customers. We also present experiments evaluating the benefits of modeling the problem on a multigraph rather than on the more classical 1-graph representation. History: Accepted by Andra Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada through the Discovery grants [Grant RGPIN-2023-03791]. It was also partially funded by HEC Montréal through the research professorship on Clean Transportation Analytics. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0404 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0404 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Towards greener city logistics: an application of agile routing algorithms to optimize the distribution of micro-hubs in Barcelona

The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.