Solving practical waste collection with time windows in an urban area

Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests

Multi-trip time-dependent vehicle routing problem with time windows

Simultaneous product and service delivery vehicle routing problem with time windows and order release dates

This paper introduces the Fleet Size and Mix Vehicle Routing Problem with Synchronized Visits (FSM-VRPS), an extension of the Vehicle Routing Problem with Synchronization (VRPS), where a mixed fleet composed of electric and conventional bikes, and passenger cars having different acquisition costs are considered. The problem consists of planning a set of different vehicle routes to serve a set of clients who may require more than one visit by different healthcare specialists, and some of these visits should be synchronized. Moreover, each client must be visited within a specified time window. In addition, the problem uses bikes to reduce Carbon Dioxide (CO2) emission for environmentally cleaner routing operations. This problem has many real-life applications, such as the scheduling of visits for homecare givers in the healthcare sector. We present a mixed integer linear-programming formulation and develop a Multi-Start Adaptive Large Neighborhood Search with Threshold Accepting algorithm. The results showed that our algorithm is highly effective on the FSM-VRPS, as well as on the heterogeneous VRPS. We also demonstrate the advantage of adopting different types of vehicles in terms of reducing the number of vehicles and costs. The analysis of the results also indicated that the new components added to the standard Adaptive Large Neighborhood Search algorithm enhanced intensification and diversification mechanisms during the search process.

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles.

The mobile production vehicle routing problem: Using 3D printing in last mile distribution

In recent years, with the increasing adoption of electric vehicles (EVs) in waste collection systems, research on related vehicle routing optimization has garnered significant attention. However, existing studies predominantly focus on traditional fuel-powered vehicles, while constraints unique to EVs—such as limited driving range and charging requirements—further complicate the problem. This paper addresses the route optimization problem for electric vehicles in bulky waste collection. A route optimization model incorporating three-dimensional packing constraints is established. Feasible solutions are initially constructed using a Genetic Algorithm (GA), followed by refinement through an improved Adaptive Large Neighborhood Search (ALNS) algorithm. Enhanced destruction and repair operators are designed to simultaneously optimize charging station selection and the efficient spatial arrangement of bulky waste. Simulation experiments across multiple datasets validate the algorithm's feasibility. Results demonstrate that the proposed method effectively resolves charging demands during bulky waste collection and significantly reduces operational costs.

This paper presents the methodology for solving the municipal waste collection problem in urban areas.This problem is treated as a vehicle routing problem where the demand in nodes and travel times between all pairs of nodes in a transport network are taken as stochastic quantities, known in the literature as the vehicle routing problem with stochastic demands (VRPSD).This problem is formulated as a chance-constrained programming model with normal distribution.Heuristic and metaheuristic methods, as well as their combinations, are applied to efficiently solve this model.Finally, the paper presents a comparative analysis of the obtained results with real data.

Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the trailer. This gives rise to the planning of truck-and-trailer routes containing truck-only subroutes, in addition to truck-only routes and truck-and-trailer routes without subroutes. We propose a branch-and-price algorithm for the TTRPTW, using problem specific enhancements in the pricing scheme and alternative lower bound computations. We also tailor an adaptive large neighborhood search algorithm to the TTRPTW in order to obtain good initial columns. When compared to existing metaheuristic algorithms we obtain highly competitive results. Some instances with up to 100 customers are solved to optimality with the proposed branch-and-price algorithm.

This paper presents a decision support for addressing urban waste collection services that are characterized by the presence of a garage from which all the vehicles start their routes, an intermediate facility where the vehicles unload the waste and a large number of pick up positions. Moreover, in a shift the vehicle can perform several routes subject to time and capacity constraints. Our aim is supporting the decision maker in planning the optimal vehicle routes with the objective of minimizing the length of the travels while collecting all the daily waste. The Waste Collection Vehicle Routing Problem (WCVRP) is modelled as a Mixed Integer Linear Programming problem with shift and time constraints. In order to address the large dimension of the considered city street networks, we propose a heuristic algorithm based on a clustering strategy and a farthest insertion heuristic for the solution of the Traveling Salesman Problem. A set of tests and a case study show the effectiveness of the proposed heuristic algorithm to plan the waste collection in a work day of an Italian city.

This paper presents an overview of recent advances in the field of the vehicle routing problem (VRP), based on papers published in high-quality journals during the period from January 2015 to July 2017. A distinctive feature of the presented survey is its focus on new versions of the VRP, which have recently emerged or gained momentum, and the corresponding new solution methods, with particular emphasis on computational intelligence (CI) approaches. The list of newly proposed or currently popular VRP formulations include last mile and same day delivery, crowdshipping, bike sharing systems, post-disaster response plans, local routing in large production or cargo plants, customer-centric VRP, autonomous delivery, unnamed aerial vehicle delivery, green VRP, waste collection VRP, rich VRP, or VRP with backhauls. Simultaneously, an adequate increase of interest in the application of traditional CI methods (e.g., genetic, memetic, ant colony or particle swarm optimization, simulated annealing, or their various hybrid versions) can be observed in the VRP domain. At the same time, approaches proven efficient in other optimization areas (e.g., hyperheuristics, methods based on Monte Carlo simulations, algorithms rooted in game theory and bi-level optimization-Stackelberg games, or cognitively motivated methods) have lately entered the VRP field and become a viable alternative to more traditional techniques. Since VRP is one of the fastest growing fields in the operations research area, we believe that an analysis of the recently published VRP papers from the perspective of their novelty in problem formulation and/or applied solution method can provide a true value for the CI community, especially young researchers entering the field and seeking challenges in this interesting and fast developing research area.

The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

Although urban freight transport is a significant contributor to the development of a country, it has adverse effects on the environment and the quality of life in urban areas. To reduce these adverse effects, we can deploy sustainable city logistic strategies. Scheduling and routing of vehicles is a crucial decision in city logistic strategies. Hence, in this paper, we solve the Multi-Depot Two Echelon Capacitated Vehicle Routing Problem (MD2E-CVRP), which is a variant of Vehicle Routing Problem (VRP) with heterogeneous fleets at both levels. Since VRP is NP-hard, we have proposed a Simulated Annealing (SA) based heuristic solution algorithm and have tested it on the standard 2E- CVRP and MD2E-CVRP instances. The results obtained from SA have a good solution quality at just one-fiftieth of computational time using CPLEX and was found to be faster than Adaptive Large Neighborhood Search (ALNS) with only a marginal drop in solution quality.

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>

<title>Abstract</title> <bold>MODEMS</bold> ( <bold>M</bold> unich <bold>O</bold> n- <bold>D</bold> emand <bold>E</bold> lectric <bold>M</bold> obility <bold>S</bold> ervice) showcases a Proof-of-Concept (PoC) for enhancing on-campus mobility through the integration of a fleet of Electric Vehicles (EVs), an operational route planning algorithm, and an interactive online portal. The framework enables users to submit transportation requests between designated service stations across the university campus and subsequently notifies them of the request acceptance or rejection based on system availability. Each request comprises a pickup and a delivery location, a passenger count, and a desired service time window at either pickup or delivery. Fleet agents are primarily characterized by their maximum passenger capacity and battery State-of-Charge (SoC) constraints. The core objective is to fulfill all user requests within their respective time windows, while minimizing the cumulative fleet travel time. The problem represents an advanced variant of the multi-agent Capacitated Vehicle Routing Problem with Pickup, Delivery and Time Windows (CVRPPDTW) constraints, for which we present three Mixed-Integer Linear Programming (MILP) formulations. Due to the unpredictability of MILP solution times, we also implement an Adaptive Large Neighborhood Search (ALNS) algorithm as a supplementary strategy to guarantee the generation of routing plans within reasonable time limits. All strategies are evaluated in curated experiments to validate their operational efficacy in realistic scenarios. Finally, we discuss the achieved results, followed by exploring potential future research directions.