Vehicle Routing Research Articles

A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

Common Voice Disorders among Bus Helpers in Dhaka City

Background: There are approximately 9911 buses and 8515 minibuses are running within Dhaka city. Most of them have bus helpers who most of the time uses loud voice and yelling for their job. For which they are under a great risk of having voice disorder. No study was conduct to find the rate of voice disorder in this group. This study will seek out the rate of voice disorder and the risk among this group. Materials and methods: A total of 80 bus helpers participated in the study. Their vehicle routes were within Dhaka city. It was a cross-sectional survey. Sampling was done by convenience technique. Samples were selected from different bus depots in Dhaka city. Data were collected by face to face interview using a questionnaire. Data analysis was done by descriptive statistical analysis. Statistical Package for Social Science- 17 (SPSS) and Microsoft Excel 2007 were used for data analysis. Results: The result shows that 84% of bus helpers had voice problem throughout their carrier. Current symptoms present among them are hoarseness (92%) vocal fatigue (73%), pain in throat (58%), constant throat dryness (67%) and frequent throat clearing (48%). Less frequent symptoms are breathy voice, strained voice, monotone voice, sore throat, acid taste and loss of voice. Only risk factor found is not drinking adequate water. Conclusion: The study reveals that different types of Vocal disorders are very common among the bus helpers. So, preventive measures and education are necessary to get rid of this problem. IAHS Medical Journal Vol 7(1), June 2024; 60-64

A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. 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.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.

Nested benders decomposition for a deterministic biomass feedstock logistics problem

AbstractIn this paper, we address a biomass feedstock logistics problem to supply biomass from production fields to satellite storage locations (SSLs) and from there to bioenergy plants (BePs) and then to a biorefinery. It entails a new problem feature of routing load-out equipment sets among the SSLs to perform loading/unloading of biomass and/or its pre-processing operations. The ownership of the loading equipment is a very capital-intensive link of the ethanol production supply chain, which when loaded onto trucks and routed along the logistics chain significantly brings down the ethanol production costs. This will make ethanol a cost-competitive alternative to fossil fuels, lead to sustainable use of fossil fuels and add to the overall relevance of the bioenergy sector. In this regard, the objective of our problem is to minimize the total cost incurred due to the ownership of equipment sets, fixed setups, and land rental cost, as well as the cost of transporting biomass from the fields to the BePs and biocrude oil from the BePs to the refinery. A mixed-integer mathematical model of the problem is presented, and a nested Benders decomposition-based solution approach is developed which involves decomposing this large problem into three stages. Stage 1 deals with the selection of fields, BePs, and SSLs, and assignment of fields to the SSLs. The remaining model consists of multiple Capacitated Vehicle Routing Problems (CVRPs) that are separable over individual BePs. For each BeP, the CVRP is further decomposed into Stage 2 and Stage 3 sub-problems where the Stage 2 problem is an allocation problem that assigns SSLs to tours associated to each BeP, and the Stage 3 problem is a variant of Traveling Salesman Problem (TSP) that determines the sequence in which equipment is routed over the predesignated set of SSLs for each tour. These sub-problems are integer programs rather than linear programs. First novelty of our proposed approach is to effectively handle the integrality of variables arising due to the consideration of the routing of load-out equipment. Second is solution methodology and in the use of proposed multi-cut version of optimality cuts that capture the solution value at an integer solution for the sub-problems. These cuts aid in faster convergence and are shown to be stronger than those proposed in the literature. The applicability of the proposed methodology is demonstrated by applying it to a real-life problem that utilizes available GIS data for the catchment area of regions around Gretna and Bedford in Virginia. We then solved a set of varying problem size instances using the state-of-the-art CPLEX® Branch-and-Bound and Benders Strategy methods. The CPLEX® algorithms struggled to solve instances even 10 times smaller than the real-life problem size instances; with MIP optimality gaps ranging from 5.85% to 82.79% in the allowed time limit of 10,000 s. On the other hand, our proposed nested Benders decomposition algorithm was able to achieve faster convergence and provided optimal solutions for all the considered problem instances with an average CPU run-time of around 3,700 s. This validates the efficacy and superiority of our solution approach. Lastly, we summarize our work and point out some interesting potential future research opportunities.

Modeling demand responsive service for the elderly healthcare activities considering temporal and spatial variations

ABSTRACT This paper develops a cost-effective demand-responsive transportation (DRT) model aimed at enhancing the accessibility and convenience of clinical visits for the elderly by optimizing two NP-hard problems. First, a set of pick-up stops is optimized based on potential stop locations, spatial and temporal demand, and acceptable walking distance. Next, vehicle routing and scheduling are optimized, considering vehicle capacity, users’ desired arrival times, and travel companions, to minimize total costs. An elite genetic algorithm (EGA) is developed to effectively search for the optimal solution. In the case study, demands are generated from a survey on elderly clinical visits and population census data in Xi’an. The proposed EGA is applied to different clustered demands classified by time interval to identify optimal solutions. Sensitivity analysis is conducted to explore the relationships between model parameters and decision variables for the studied DRT system.

Research on time-varying path optimization for multi-vehicle type fresh food logistics distribution considering energy consumption

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges.

Enhancing offshore parcel delivery efficiency through vessel-unmanned aerial vehicle collaborative routing

Offshore oil and gas production is vital for global energy supply, but it faces logistical challenges due to the high costs and inefficiencies of traditional supply methods. This paper introduces the vessel-unmanned aerial vehicle (UAV) routing problem with multiple visits in a single flight (VURP-M), a novel logistical model that addresses aimed at enhancing the replenishment of offshore platforms with small, essential items. The VURP-M allows the UAV to perform multiple visits during a single flight, optimising the delivery process. To tackle the VURP-M, we propose two mixed-integer second-order cone programs that capture the problem's complexities. Given its NP-hard nature, we employ an adaptive large neighbourhood search (ALNS) method, featuring a segmented initialisation process and problem-specific operators guided by a rule-based mechanism to improve solution efficiency. The ALNS formulates an initial solution by solving a travelling salesman problem to create a giant tour, which is then used to group sequential targets into multiple UAV flights. The subsequent optimal resolution of a SOCP determines the take-off and landing points for each flight. Subsequently, the ALNS refines the initial solution through destroy and repair operators, enhancing the search for superior sequences and allocation schemes. The effectiveness of our approach is demonstrated through a real-world case study and numerical experiments on random instances featuring up to 100 platforms. The results offer implications of the collaborative vessel-UAV model for the offshore logistics industry.

Local Search Heuristic for the Two-Echelon Capacitated Vehicle Routing Problem in Educational Decision Support Systems

This study focuses on developing a heuristic for Decision Support Systems (DSS) in e-commerce logistics education, specifically addressing the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). The 2E-CVRP involves using Urban Transshipment Points (UTPs) to optimize deliveries. To tackle the complexity of the 2E-CVRP, DSS can employ fast and effective techniques for visual problem-solving. Therefore, the objective of this work is to develop a local search heuristic to solve the 2E-CVRP quickly and efficiently for implementation in DSS. The efficiency of the heuristic is assessed through benchmarks from the literature and applied to real-world problems from a Brazilian e-commerce retailer, contributing to advancements in the 2E-CVRP approach and promoting operational efficiency in e-commerce logistics education. The heuristic yielded promising results, solving problems almost instantly, for instances in the literature on average in 1.06 s, with average gaps of 6.3% in relation to the best-known solutions and, for real problems with hundreds of customers, in 1.4 s, with gaps of 8.3%, demonstrating its effectiveness in achieving the study’s objectives.

Fixed-Ratio Approximation Algorithm for the Minimum Cost Cover of a Digraph by Bounded Number of Cycles

We consider polynomial time approximation for the minimum cost cycle cover problem of an edge-weighted digraph, where feasible covers are restricted to have at most k disjoint cycles. In the literature this problem is referred to as Min-k-SCCP. The problem is closely related to classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) and has many important applications in algorithms design and operations research. Unlike its unconstrained variant, the Min-k-SCCP is strongly NP-hard even on undirected graphs and remains intractable in very specific settings. For any metric, the problem can be approximated in polynomial time within ratio 2, while in fixed-dimensional Euclidean spaces it admits Polynomial Time Approximation Schemes (PTAS). In the same time, approximation of the more general asymmetric Min-k-SCCP still remains weakly studied. In this paper, we propose the first fixed-ratio approximation algorithm for this problem, which extends the recent breakthrough Svensson-Tarnawski-Vegh and Traub-Vygen results for the Asymmetric Traveling Salesman Problem.

Multi-depot vehicle routing problem with drones in emergency logistics

Multi-depot vehicle routing problem with drones in emergency logistics

Joint optimization of recyclable inventory routing problem under uncertainties in an incentive-based recycling system

Due to the value of resource recovery and the development of a circular economy, waste recycling has gathered global attention. Recently, many emerging cities designed new systems like an incentive-based recycling system (IBRS). In such systems, recyclables are collected through community recycling nodes by offering incentives, then transported to street recycling stations and sorted before being finally recycled. The increased recycling nodes and the incentives enhance the convenience and residents’ enthusiasm for waste recycling, but also intensify the uncertainty of recycling quantities and the complexity of the recycling operation management. Poor recycling operation management may result in increased recycling costs or greater loss of recyclables, which discourages residents from participating in recycling. Based on an existing IBRS, this study investigates the joint optimization problem of the recyclable inventory management at each community recycling node and the vehicle routing from the recycling nodes to the recycling station. A two-stage dual-objective multi-period stochastic programming model is established to minimize the loss of recyclables and logistics costs, which is further reformulated using the weighting method and transportation cost approximation parameters. To solve the reformulated model, a three-phase iterative algorithm is designed by combining the progressive hedging algorithm and route splitting algorithm based on the Lin-Kernighan heuristic. A case study is conducted using data from Shanghai’s IBRS. The proposed joint decision model is superior to separate decisions and the three-phase iterative algorithm can reduce the average total cost by up to 42.12% compared to the genetic algorithm and the Iteration-Move-Search method in the literature. Additionally, a sensitivity analysis is conducted to provide managerial insights.

A novel mixed-integer linear programming formulation for continuous-time inventory routing

Inventory management, vehicle routing, and delivery scheduling decisions are simultaneously considered in the context of the inventory routing problem. This paper focuses on the continuous-time version of this problem where, unlike its more traditional discrete-time counterpart, the distributor is required to guarantee that inventory levels are maintained within the desired intervals at any moment of the planning horizon. In this work, we develop a compact mixed-integer linear programming formulation to model the continuous-time inventory routing problem. We further discuss means to expedite its solution process, including the adaptation of well-known rounded capacity inequalities to tighten the formulation in the context of a branch-and-cut algorithm. Through extensive computational studies on a suite of 90 benchmark instances from the literature, we show that our branch-and-cut algorithm outperforms the state-of-the-art approach. We also consider a new set of 63 instances adapted from a real-life dataset and show our algorithm’s practical value in solving instances with up to 20 customers to guaranteed optimality.

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. We developed a decision-making tool based on OpenStreetMap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To illustrate how these VRPTW algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem

Nowadays, the world’s fishing fleet uses 20% more fuel to catch the same amount of fish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange for a significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.

A real-time energy and cost efficient vehicle route assignment neural recommender system

A real-time energy and cost efficient vehicle route assignment neural recommender system

Improved Whale Optimization Algorithm with Variable Neighbourhood Search Strategy (WOA-VNS) in Solving Vehicle Routing Problem (VRP) for Recommending Multi-days Tourist Routes in Yogyakarta

Improved Whale Optimization Algorithm with Variable Neighbourhood Search Strategy (WOA-VNS) in Solving Vehicle Routing Problem (VRP) for Recommending Multi-days Tourist Routes in Yogyakarta

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.

Optimization of carbon emission in an integrated machine-piece scheduling and vehicle routing problem and its solution using MOPSO and NSGAII metaheuristic algorithms

The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.