Total Travel Cost Research Articles

Bridging the Gap: Remote Evaluation and Programming of Cardiac ImplantableDevices in Medically Underserved Areas.

Delivering care in medically underserved areas (MUA) is a public health challenge. The purpose of this study was to evaluate the advantages of a noncommercial multivendor solution for remote interrogation and programming of cardiac implantable electronic devices (CIEDs) among inhabitants of MUAs. This prospective comparative study evaluated benefits and safety of remote CIEDs evaluation in patients living >100km from the Bordeaux University Hospital. A conventional in-person evaluation at the Bordeaux hospital was compared with a remote evaluation performed 6months later at a nurse office close to the patient home. The technical solution consisted of remote controlling CIED programmers from the 5 main manufacturers allowing evaluation of all kinds of CIEDs. A majority of the 34 included patients (94%) were satisfied with the remote evaluation strategy. To attend the in-person evaluation at the Bordeaux Hospital, the patients covered a median distance of 253km (ICR: 221-258km) compared with 9km (ICR: 6-19km) (P< 0.001) to get to the nurse's office. The use of a medical vehicle was reduced by a factor 7 (44% vs 6%; P = 0.001) with the remote evaluation, which translated into a reduction in the total traveling cost per patient by a factor 32 (96 Euros [87-268 Euros] vs 3 Euros [2-8 Euros]; P< 0.001). The travel carbon footprint was 14 times lower (28 Kg CO2-eq [24-28 Kg CO2-eq] vs 2 Kg CO2-eq [1-5 Kg CO2-eq]; P< 0.001) with the remote evaluation strategy. Remote evaluation of CIEDS in patients living MUA is feasible and safe. This approach is associated with a high level of satisfaction, lower economic and ecological costs compared with the in-person assessment in this population. (Remote Programming of Cardiac Implantable Electronic Devices 2 [REACT 2]; NCT06272344).

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed-route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEVs) are an innovative technological solution that could help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, MO, using responses from a community survey ( n = 244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed-route transit, and transportation network companies (TNCs) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly [20%], lower income [27%], and households with limited access to private vehicles [34%]). ODT reduced total travel time by 32% compared with fixed-route transit, produced 2.4 to 4.3 times less greenhouse gas emissions per passenger mile (compared with transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10 to $12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

Non-dominated sorting simplified swarm optimization for multi-objective omni-channel of pollution-routing problem

Abstract The activities of the traffic department mainly contribute to the generation of greenhouse gas (GHG) emissions. The swift expansion of the traffic department results in a significant increase in global pollution levels, adversely affecting human health. To address GHG emissions and propose impactful solutions for reducing fuel consumption in vehicles, environmental considerations are integrated with the core principles of the vehicle routing problem. This integration gives rise to the pollution-routing problem (PRP), which aims to optimize routing decisions with a focus on minimizing environmental impact. At the same time, the retail distribution system explores the use of an omni-channel approach as a transportation mode adopted in this study. The objectives of this research include minimizing total travel costs and fuel consumption while aiming to reduce GHG emissions, promoting environmental sustainability, and enhancing the convenience of shopping and pickup for customers through the integration of online and offline modes. This problem is NP-hard; therefore, the Non-dominated Sorting Simplified Swarm Optimization (NSSO) algorithm is employed. NSSO combines the non-dominated technique of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the update mechanism of Simplified Swarm Optimization to obtain a set of Pareto-optimal solutions. Moreover, the NSSO, a multi-objective evolutionary algorithm, is adopted to address multi-objective problems. The PRP benchmark dataset is utilized, and the results are compared with two other multi-objective evolutionary algorithms: NSGA-II and Non-dominated Sorting Particle Swarm Optimization (NSPSO). The findings of the study confirm that NSSO exhibits feasibility, provides good solutions, and achieves faster convergence compared with the other two algorithms, NSGA-II and NSPSO.

Exploring the role of Mobility-as-a-Service in morning commuting trips

Promising the seamless integration of multiple transportation modes, Mobility-as-a-Service (MaaS) has gained popularity over the years, yet its effectiveness in enticing private car users and improving travel efficiency remains uncertain. This study explores the competitiveness of MaaS-enabled multi-modal travel options versus private car usage through equilibrium analysis. In addition to pricing that affects the fixed cost for travel, we examine the often overlooked inconvenience cost associated with multi-modal trips. We establish our analysis for the commuting problem of a one-origin-one-destination network, where a highway and a mass transit line connect the residential area and the central business district (CBD) area. Travelers choose their departure time and travel mode among auto, Park-and-Ride (PnR), ride-hailing and transit (RnT) to minimize their total travel cost. Inconvenience costs associated with searching for parking and waiting for ride-hailing are explicitly modeled. We analytically provide the mode share and departure time windows under all possible equilibria. Our findings reveal the complex nature of mode choice, distinctly affected by fixed and inconvenience costs, with demand playing an even more significant role. Notably, fixed costs set an entry fee to adopt different modes, and the feature of inconvenience costs affects the utilization of available transportation resources. More importantly, to benefit the overall system, we encourage maintaining a balanced mode share by implementing pricing and capacity strategies rather than aiming for a completer transition of private car users to MaaS.

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

PurposeA fleet of mobile robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of task allocation and motion planning problem for a fleet of mobile robots which are requested to operate in an intelligent industry. More specifically, the robots are requested to serve a set of inspection points within given service time windows. In comparison with the conventional time windows, our problem considers fuzzy time windows to express the decision maker’s satisfaction for visiting an inspection point.Design/methodology/approachThe paper develops a unified approach to the combined problem of task allocation and motion planning for a fleet of mobile robots with three objectives: (a) minimizing the total travel cost considering all robots and tasks, (b) balancing fairly the workloads among robots and (c) maximizing the satisfaction grade of the decision maker for receiving the services. The optimization problem is solved by using a novel combination of a Genetic Algorithm with pareto solutions and fuzzy set theory.FindingsThe computational results illustrate the efficiency and effectiveness of the proposed approach. The experimental analysis leverages the potential for using fuzzy time windows to reflect real situations and respond to demanding situations.Originality/valueThis paper provides trade-off solutions to a realistic combinatorial multi-objective optimization problem considering concurrently the motion and path planning problem for a fleet of mobile robots with fuzzy time windows.

Generalized Riskiness Index in Vehicle Routing Under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework

We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101187, 72371204, 72021002, and 71901180], the Qatar National Research Fund [Grant ARG01-0430-230029], Natural Science Foundation of Sichuan Province [24NSFSC6232], and Guanghua Talent Project of the Southwestern University of Finance and Economics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0345 .

Time-reliability optimization for the stochastic traveling salesman problem

This paper presents a novel approach to addressing the Stochastic Traveling Salesman Problem (STSP), a classical problem in combinatorial optimization, by integrating travel time and reliability factors into the decision-making process. Traditional TSP models primarily focus on minimizing the total travel distance or cost without considering the reliability of each route. In real-world situations, especially in logistics and network design, it's just as important to have reliable routes. A reliable route means there's a good chance it will be completed successfully and on time. Our research extends the conventional STSP framework by incorporating a reliability metric for each route, alongside the standard travel time metric. A tri-objective optimization model is proposed to minimize the mean and standard deviation of travel time and maximize route reliability simultaneously. A new algorithm called Permutation Binary-Addition-Tree (BAT) is proposed to solve the problem more efficiently when there is uncertainty. Our approach marks a significant step towards more realistic and practical solutions for route optimization problems in dynamic and uncertain environments. We also present a complexity analysis of our model against traditional cost-only TSP solutions, demonstrating the efficacy of considering reliability in route planning.

Enhancing On-demand Transportation: Prioritizing Travel Costs with Hybrid Evolutionary Algorithms

Objective: Enhancing sustainability in on-demand transportation is a challenging task by offering flexible and optimized mobility solutions. This study focuses on on-demand mobility systems. We introduce a novel evolutionary computing method designed to address a bi-objective customized on-demand transportation problem, aiming to minimize total travel cost and total waiting time. Methods: The method incorporates specific optimization techniques, along with an efficient dominance sorting approach, using an intelligent candidate list to reduce computational time. Results: Comparative results demonstrate the effectiveness of this hybrid computing method, particularly when prioritizing total travel cost from the service provider’s perspective. Conclusion: These findings present a promising framework for decision-makers, empowering them to navigate favorable compromises between conflicting objectives and make informed choices aligned with their preferences and customer-oriented constraints.

Manage morning commute for household travels with parking space constraints

Parking has always been a thorny problem in urban transport systems. Parking availability and parking reservation management may have a significant influence on the travel behavior of household travelers. This paper explores the morning commute for household travels by considering parking space constraint and the parking reservation mechanism in a two-mode transport system. Household travelers depart from home to their children’s school by car or by rail and then go to work alone. For household travelers who drive, some household travelers have reserved parking spaces while other household travelers must compete for public ones on a first-come-first-served (FCFS) basis. When household travelers do not have a parking spot, they use rail transit for their commute. We derive ten equilibrium departure patterns based on the school-work time difference, the number of parking spots and the ratio of reserved parking spots. With these equilibrium departure patterns, we thoroughly discuss the impact of school-work time difference on the total travel cost of auto travelers and parking reservation ratio. It is found that, given the school-work time difference, appropriate parking reservation allocation can minimize the total system travel cost. Then, the system performance, including total system travel cost and total congestion cost, is examined by regulating the parking supply, the parking reservation allocation and the school-work time difference. We found that there is a unique optimal parking supply to minimize the total system travel cost, and appropriate staggering policy can alleviate traffic congestion and thus improves social welfare. Finally, numerical analysis is conducted to verify the findings, and the Pareto frontier for system performance is analyzed. Traffic authorities can select the optimal solution based on the preferences of traffic indicators to develop effective strategies to manage morning commuting of household travels with parking space constraint. The study demonstrates that implementing reasonable staggering policy and efficient parking management can effectively promote efficiency of urban morning commuting systems and reduce social costs for household travelers.

Urban rail transit disruption management based on passenger guidance and extended bus bridging service considering uncertain bus running time

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions.

A hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures, but current approaches take a long time to cover the entire area. Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments. This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time. The proposed framework consists of two stages, the exploration stage and the exploitation stage, resolving the task in a sequential manner. At the exploration stage, the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs, and the Voronoi-based planner is used to enable the UAVs to reach their best region. After obtaining the global map, viewpoints are generated and divided while taking into account the battery capacity of each UAV. Finally, a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction. Several experiments are conducted in both a simulated and real environment to show the time efficiency, robustness, and effectiveness of the proposed method. Furthermore, the whole system is implemented in real applications.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

Quantifying hospital-associated costs, and accompanying travel costs and productivity losses, before and after withdrawing TNF-α inhibitors in juvenile idiopathic arthritis.

To quantify differences in hospital-associated costs, and accompanying travel costs and productivity losses, before and after withdrawing TNF-α inhibitors (TNFi) in JIA patients. This was a retrospective analysis of prospectively collected data from electronic medical records of paediatric JIA patients treated with TNFi, which were immediately discontinued, spaced (increased treatment interval) or tapered (reduced subsequent doses). Costs of hospital-associated resource use (consultations, medication, radiology procedures, laboratory testing, procedures under general anaesthesia, hospitalization) and associated travel costs and productivity losses were quantified during clinically inactive disease until TNFi withdrawal (pre-withdrawal period) and compared with costs during the first and second year after withdrawal initiation (first and second year post-withdrawal). Fifty-six patients were included of whom 26 immediately discontinued TNFi, 30 spaced and zero tapered. Mean annual costs were €9165/patient on active treatment (pre-withdrawal) and decreased significantly to €5063/patient (-44.8%) and €6569/patient (-28.3%) in the first and second year post-withdrawal, respectively (P < 0.05). Of these total annual costs, travel costs plus productivity losses were €834/patient, €1180/patient, and €1320/patient in the three periods respectively. Medication comprised 80.7%, 61.5% and 72.4% of total annual costs in the pre-withdrawal, first and second year post-withdrawal period, respectively. In the first two years after initiating withdrawal, the total annual costs were decreased compared with the pre-withdrawal period. However, cost reductions were lower in the second year compared with the first year post-withdrawal, primarily due to restarting or intensifying biologics. To support biologic withdrawal decisions, future research should assess the full long-term societal cost impacts, and include all biologics.

Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost

Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances.

Generalized vehicle routing problem: Contemporary trends and research directions

Generalized Vehicle Routing Problem (GVRP) is a challenging operational research problem which has been widely studied for nearly two decades. In this problem, it is assumed that graph nodes are grouped into a number of clusters, and serving any node of a cluster eliminates the need to visit the other nodes of that cluster. The general objective of this problem is to find the set of nodes to visit and determine the service sequence to minimize the total traveling cost. In addition to these general conditions, GVRP can be formulated with different assumptions and constraints to practically create different sub-types and variants. This paper aims to provide a comprehensive survey of the GVRP literature and explore its various dimensions. It first encompasses the definition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful suggestions are discussed to extend the problem. For this review study, Google Scholar, Scopus, Science Direct, Emerald, Springer, and Elsevier databases were searched for keywords, and 160 potential articles were extracted, and eventually, 45 articles were judged to be relevant.

Modification of the Ant Colony Optimization Algorithm for Solving Multi-Agent Task Allocation Problem in Agricultural Application

This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for solving task allocation is improved by modifying the efficiency factor. In the proposed algorithm, the efficiency factor is defined as a function that relates not only to the capability of the agents and the distance between the agents, but also to the distance between the agents and the target. To solve the task allocation problem, the capability list of the agents was also adjusted using common UAV capabilities in agricultural application. Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. The optimum number of ants and agents in the proposed algorithm was also analysed for robust performance. Simulation results revealed that the addition of the numbers of agents and ants increases the average efficiency of the algorithm. In this study, we have also added a function to calculate the system capability utilization. By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. In addition, a simple experiment using five ground robots with a centralized control was also carried out as a proof of concept for the proposed algorithm.

An adaptive large neighborhood search for the robust rig routing

In the oil and gas industry, there is a need for constructing auxiliary maintenance and storage buildings nearby exploration sites. As the initial stage for such constructing purposes, it is required to drill wells for construction piles. To this end, there is a fleet of mobile drilling rigs traveling from one exploration site to another, performing the work on drilling wells. For each site, we have a time window for completing all the work on drilling. To complete all the work among all the exploration sites in time, it may be necessary to allow several drilling rigs to perform work at the same site. Yet, in the real world, unforeseen circumstances can affect drilling time, and that, if disregarded, can lead to a disruption of the work plan. Thus, in this problem, we maximize possible deviations of drilling times from expected values when there is still a feasible solution to perform all well-drilling requests in time. At the same time, total traveling costs must be no more than a given threshold. It is a so-called threshold robustness approach. The described Drilling Rig Routing Problem (DRRP) differs from other well-studied variants of Vehicle Routing Problem (VRP) in that the work splitting affects the service time. There is a limited number of papers dealing with such problems and no papers about the robust formulation for them. This research proposes a mathematical model for the novel formulation of the DRRP with uncertainties and an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it. The destruction operators work either at the route, customer, or visit level. The reconstruction operators take into account a possibility to change the work partition. Computational results for the algorithm and Gurobi solver show the dominance of the ALNS scheme for medium- and large-sized instances.

Improving neighborhood exploration into MOEA/D framework to solve a bi‐objective routing problem

AbstractLocal search (LS) algorithms are efficient metaheuristics to solve combinatorial problems. The performance of LS highly depends on the neighborhood exploration of solutions. Many methods have been developed over the years to improve the efficiency of LS on different problems of operations research. In particular, the exploration strategy of the neighborhood and the exclusion of irrelevant neighboring solutions are design mechanisms that have to be carefully considered when tackling NP‐hard optimization problems. An MOEA/D framework including an LS‐based mutation and knowledge discovery mechanisms is the core algorithm used to solve a bi‐objective vehicle routing problem with time windows (bVRPTW) where the total traveling cost and the total waiting time of drivers have to be minimized. We enhance the classical LS exploration strategy of the neighborhood from the literature of scheduling and propose new metrics based on customer distances and waiting times to reduce the neighborhood size. We conduct a deep analysis of the parameters to give a fine tuning of the MOEA/D framework adapted to the LS variants and to the bVRPTW. Experiments show that the proposed neighborhood strategies lead to better performance on both Solomon's and Gehring and Homberger's benchmarks.

Freight transport using additional railcars attached to intercity passenger trains with transshipment and railcar circulation: Tabu-search-based Lagrangian heuristic

Developing under-exploited passenger rail networks is emerging as an alternative for freight transport. In light of this, we investigate a long-haul freight mode in which additional railcars for carrying shipments are attached to passenger trains, and the shipments can transfer among the trains at intermediate stops. To coordinate space–time schedules of shipments and railcars, we construct a two-level space–time network: one level captures the departure, transshipment, and arrival of shipments; whereas the other level depicts the holding, reshuffling, and repositioning of railcars. Incorporating variables of space–time arc selection and constraints concerning shipment (un-) loading, locomotive pulling capacity, supply–demand coupling, and flow conservation on networks, a binary integer model is established to minimize the total travel cost of both railcars and shipments. The primal problem is decomposed using Lagrangian relaxation. Lagrangian multipliers and dual bounds are approximated by solving the Lagrangian dual. We devise a primal heuristic based on tabu search for expediting high-quality solutions. Numerical experiments demonstrate computational performance and managerial insights.

Designing sustainable logistics networks for classified municipal solid wastes collection and transferring with multi-compartment vehicles

The success stories of waste classification and processing systems in reducing emissions and conserving natural resources have inspired many countries to adopt similar systems. The current research on waste collection logistics network design is limited in capturing the features of waste classification systems. The environmental objectives, the use of multi-compartment vehicles to separate waste and reduce driving distance, and the joint decision-making of transfer station locations and vehicle routes are not integrated into the same optimization framework. This study aims to investigate the strategic planning of transfer stations and vehicle fleet dispatch in a city-level classified waste collection system. The problem is formulated as an integer programming problem that minimizes total travel costs while considering greenhouse gas (GHG) emissions. To solve the problem effectively, we propose a fast hybrid heuristic algorithm based on column generation (CG) and Adaptive Large Neighborhood Search (ALNS). The proposed algorithm framework is tested on various instances and applied to a real-sized logistics network in Chengdu, China. Computational results indicate that the proposed algorithm achieves high efficiency while maintaining a satisfactory gap. The results confirm that introducing the waste classification system can help reduce total travel costs and greenhouse gas emissions.