Routing Challenges Research Articles

Vehicle routing with heterogeneous service types: Optimizing post-harvest preprocessing operations for fruits and vegetables in short food supply chains

This study focuses on the post-harvest preprocessing of fruits and vegetables, aiming to provide an effective way to conduct preprocessing operations in short food supply chains. We consider both a heterogeneous fleet of mobile preprocessing units and the possibility to pick up products for centralized preprocessing. The resulting problem is a variant of the classic heterogeneous fleet vehicle routing problems with time windows (HFVRPTW), with the additional consideration of multi-depot and heterogeneous service types, which we refer to as HFVRPTW-MDHS. These additional considerations are important to include in the development of more efficient food supply chains, but lead to a challenging routing problem. In this paper, we formulate the HFVRPTW-MDHS using a mixed-integer linear programming model. Due to the complexity of the model, we propose a customized adaptive large neighborhood search (ALNS) metaheuristic. We design a multi-level struct-based solution representation to improve the efficiency of the ALNS and develop customized methods for solution evaluation, feasibility checks, and neighborhood search. Comparing our results with the results of an exact algorithm and solutions in the existing literature, we find that our ALNS algorithm can obtain high-quality solutions quickly when solving HFVRPTW-MDHS and related variants of the VRP. Finally, we study the application of our approach in the case of precooling, which is a commonly used preprocessing operation, to illustrate the effectiveness of our approach in a relevant practical context.

A New Exact Algorithm for Single-Commodity Vehicle Routing with Split Pickups and Deliveries

We present a new exact algorithm to solve a challenging vehicle routing problem with split pickups and deliveries, named as the single-commodity split-pickup and split-delivery vehicle routing problem (SPDVRP). In the SPDVRP, any amount of a product collected from a pickup customer can be supplied to any delivery customer, and the demand of each customer can be collected or delivered multiple times by the same or different vehicles. The vehicle fleet is homogeneous with limited capacity and maximum route duration. This problem arises regularly in inventory and routing rebalancing applications, such as in bike-sharing systems, where bikes must be rebalanced over time such that the appropriate number of bikes and open docks are available to users. The solution of the SPDVRP requires determining the number of visits to each customer, the relevant portions of the demands to be collected from or delivered to the customers, and the routing of the vehicles. These three decisions are intertwined, contributing to the hardness of the problem. Our new exact algorithm for the SPDVRP is a branch-price-and-cut algorithm based on a pattern-based mathematical formulation. The SPDVRP relies on a novel label-setting algorithm used to solve the pricing problem associated with the pattern-based formulation, where the label components embed reduced cost functions, unlike those classical components that embed delivered or collected quantities, thus significantly reducing the dimension of the corresponding state space. Extensive computational results on different classes of benchmark instances illustrate that the newly proposed exact algorithm solves several open SPDVRP instances and significantly improves the running times of state-of-the-art algorithms. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72222011, 71971090, 71821001, 72171112], by the Young Elite Scientists Sponsorship Program by CAST [Grant 2019QNRC001], and by the Research Grants Council of Hong Kong SAR, China [Grant 15221619]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2022.1249 .

A modified Ant Colony System for the asset protection problem

• Time-efficient solutions of the Asset Protection Problem (APP). • The Modified Ant Colony System (MACS) is proposed. • Especially designed solution-improving heuristic techniques. • New best results in large-scale benchmark instances. • MACS outperforms previous APP solution approaches. During an escaped wildfire in a populated area’s vicinity, protective tasks should be carried out to secure crucial community assets, e.g., bridges, hospitals, power stations, and communication towers. In a real-life scenario, an important asset may require the combined effort of different fire suppression resources, which should be dispatched and scheduled to act synchronously in protecting the respective asset. The present research addresses the solution of a challenging routing problem in emergency response, the Asset Protection Problem (APP), which incorporates selective characteristics in routing a heterogeneous vehicle fleet with complex temporal and spatial constraints, i.e., time windows and synchronization requirements. Notably, the Modified Ant Colony System (MACS) algorithm is proposed to obtain effective APP solutions within a time suitable for operational purposes. Based on the conducted experiments, MACS outperforms the previously published solution approaches in the solution of large-scale APP benchmark instances. Notably, MACS obtained superior solutions in 159 out of 240 large-scale instances, while 87 of them represent new best results, considering the solutions achieved by the commercial solver CPLEX with a ten-hour time limit.

Planning the city operations of a parcel express company

We introduce an interesting and challenging routing and scheduling problem arising in the city operations of SF Express, a large package express carrier in China. Vehicles execute multiple trips during a planning horizon spanning multiple shifts, where a trip can involve deliveries only, pickups only, or deliveries followed by pickups. Complicating factors include split deliveries and pickups, cross-trip consistency requirements, and limited unloading capacity at the main hubs. We develop an optimization-based multi-phase heuristic solution approach seeking to minimize the number of vehicles used. An extensive computational study using real-world instances demonstrates the effectiveness of the approach.

Analyzing the benefits of an integrated mobility system using a matheuristic routing algorithm

In many Western countries, governments are currently implementing an innovative demand-driven mobility policy. Providers of collective door-to-door transport, called dial-a-ride services, are increasingly invoked to replace unprofitable public transport in rural areas. This requires an integrated mobility system in which a user’s trip may consist of a combination of dial-a-ride services and regular public transport. In order to optimally integrate both systems from an operational point of view, dial-a-ride providers need to solve a challenging routing problem. Their flexible vehicle routes should be synchronized to the timetables of the remaining public transport services, while the optimal selection of the users’ transfer terminals depends on the actual structure of the dial-a-ride routes. This paper introduces a routing algorithm and integrated scheduling procedure to enforce this synchronization for problems of a realistic scale, enabling the design and operational implementation of an integrated mobility system. Experiments, performed on a new artificial benchmark data set with realistic characteristics, clearly indicate that from the perspective of the dial-a-ride providers, considerable operational benefits can be obtained by integrating public transport into their services. The resulting distance savings for the dial-a-ride vehicles are shown to depend on the operational characteristics of the system, the geographical distribution of the demand, and the ability to flexibly assign transfer terminals to user requests. Furthermore, the proposed algorithm is also very efficient in solving related problems in passenger and freight transport.

Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles

Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput.

Improved Memetic Algorithm for Multi-depot Multi-objective Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real-world applications. In this paper, an extended version of CARP, the multi-depot multi-objective capacitated arc routing problem (MDMOCARP) is proposed to tackle practical requirements. Firstly, the critical edge decision mechanism and the critical edge random allocation mechanism are proposed to optimize edges between depots. Secondly, a novel adaptive probability of local search with fitness is proposed to improve the Decomposition-Based Memetic Algorithm for Multi-Objective CARP (D-MAENS). Compared with the D-MAENS algorithm, experimental results on MD-CARP instances show that the improved memetic algorithm (IMA) has performed significantly better than D-MAENS on convergence and diversity in the metric IGD and the metric HV.

Conflict-free routing of multi-stop warehouse trucks

The recent interest in greater vehicular autonomy for factory and warehouse automation has stimulated research in conflict-free routing: a challenging network routing problem in which vehicles may not pass each other. Motivated by a real-world case study, we consider one such application: truck movements in a tightly constrained warehouse. We propose an extension of an existing conflict-free routing algorithm to consider multiple stopping points per route. A high level metaheuristic is applied to determine the route construction and assignment of vehicles to routes.

Sinkhole Attacks in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of a large number of nodes, communicating sensor readings to the base stations through other nodes. Due to their energy limitations and positioning in hostile environments, WSNs are vulnerable to various routing attacks. From a security point of view in WSN, data authenticity, confidentiality, Integrity, and availability are the important security goals. It is in common practice that a security protocol used to be created by focusing a particular attack in WSN. Most renowned attacks in WSN are Sybil attack, Denial of Service attack, wormhole attack, selective attack, HELLO Flooding attack, Sinkhole attack etc. This survey focuses on one of the most challenging routing attacks, called Sinkhole attack. A Sinkhole attack is one of the sternest routing attacks because it attracts surrounding nodes with misleading routing path information and performs data forging or selective forwarding of data passing through it. It can cause an energy drain on surrounding nodes resulting in energy holes in WSNs and it can cause inappropriate and potentially dangerous responses based on false measurements. Researchers had presented several ways to detect and identify sinkhole attacks. This survey reviews related work on Sinkhole attack detection, prevention strategies, and attack techniques and also highlights open challenges in dealing with such attacks. Among many discussed techniques, fuzzy logic-based systems are considered to be good in performance in intruder detection system (IDS).

Large neighborhood-based metaheuristic and branch-and-price for the pickup and delivery problem with split loads

We consider the multi-vehicle one-to-one pickup and delivery problem with split loads, a NP-hard problem linked with a variety of applications for bulk product transportation, bike-sharing systems and inventory re-balancing. This problem is notoriously difficult due to the interaction of two challenging vehicle routing attributes, “pickups and deliveries” and “split deliveries”. This possibly leads to optimal solutions of a size that grows exponentially with the instance size, containing multiple visits per customer pair, even in the same route. To solve this problem, we propose an iterated local search metaheuristic as well as a branch-and-price algorithm. The core of the metaheuristic consists of a new large neighborhood search, which reduces the problem of finding the best insertion combination of a pickup and delivery pair into a route (with possible splits) to a resource-constrained shortest path and knapsack problem. Similarly, the branch-and-price algorithm uses sophisticated labeling techniques, route relaxations, pre-processing and branching rules for an efficient resolution. Our computational experiments on classical single-vehicle instances demonstrate the excellent performance of the metaheuristic, which produces new best known solutions for 92 out of 93 test instances, and outperforms all previous algorithms. Experimental results on new multi-vehicle instances with distance constraints are also reported. The branch-and-price algorithm produces optimal solutions for instances with up to 20 pickup-and-delivery pairs, and very accurate solutions are found by the metaheuristic.

A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows

This paper considers the vehicle routing problem with soft time windows, a challenging routing problem where customers' time windows may be violated at a certain cost. The vehicle routing problem with soft time windows has a lexicographic objective function, aimed at minimizing first the number of routes, then the number of violated time windows, and finally the total routing distance. We present a multistage very large-scale neighborhood search for this problem. Each stage corresponds to a variable neighborhood descent over a parameterizable very large-scale neighborhood. These neighborhoods contain an exponential number of neighbors, as opposed to classical local search neighborhoods. Often, searching very large-scale neighborhoods can produce local optima of a higher quality than polynomial-sized neighborhoods can. Furthermore, we use a sophisticated heuristic to determine service start times allowing us to minimize the number of violated time windows. We test our approach on a number of different problem types, and compare the results to the relevant state of the art. The experimental results show that our algorithm improves best known solutions on 53% of the most studied instances. Many of these improvements stem from a reduction of the number of vehicles, a critical objective in vehicle routing problems.

Robust Formulation for Optimizing Sustainable Ship Routing and Scheduling Problem

Increasing trade globally necessitates the requirement of developing effective heuristics to resolve the challenging ship routing and scheduling problem among multiple ports. The posing restrictions related to multiple time windows within a planning horizon, varying demand and supply at different ports increases the existing complexity. Here, sustainability issues associated with carbon emission is incorporated. Owing to the inherent complexity, the formulation developed moves towards a non-linear mixed integer programming model. The aforementioned problem assuming to be NP-Hard in nature and can be solved using an intelligent search heuristics named particle swarm optimization for composite particle (PSO-CP).

A Hybrid Approach for Milk Collection Using Trucks and Trailers

(ProQuest: ... denotes formulae omitted.)1. IntroductionCollection of milk from farms is a challenging vehicle routing problem. Milk is perishable so it must be collected from the farms and transported to the production plants as soon as possible. This problem is handled differently from country to country due to the varying nature of geographical and environmental conditions.Geographically, milk farms are mostly located in the rural areas of a country. Due to the perishable nature of milk and the limited storage capacities at farms, it must be collected and transported to production factories within a given time limit. The production quantity of milk depends on the size of each farm, as the number of cows varies.Trucks and trailers with different capacities/sizes are used for collection and are owned or hired by the company. Transportation companies usually desire to acquire fleet of vehicles which are not too expensive but have a high degree of reliability and durability and that will be optimally utilized. It is not economical to invest in a fleet mix which remains idle or unutilized in a given period. Thus the objective of the company is to choose an appropriate combination of trucks and trailers which could reduce the total transportation cost. In addition to the fleet composition problem, there are also weights and accessibility restrictions on the roads that make this problem more difficult. As a result of this added complexity in the problem constraints, the distribution planning decisions are harder to make. Transportation cost is a central aspect to be considered in distribution planning. It generally involves driver's cost, truck and trailer's acquisition cost, variable routing cost, toll road/ferry taxes and others. Thus, milk collection normally incurs a major transportation cost. Still, this type of problem is solved manually in most countries, like many other problems. In this paper, we provide a simple cost effective technique to this vehicle routing planning and distribution problem.This paper describes a real world problem where TINE SA, the leading dairy company in Norway, collects and delivers raw milk from different farms to the production factories. The methodology developed and explained in this paper provides an economic solution to this type of problem. The special case of Truck and Trailer Vehicle Routing Problem considers four major aspects: fleet composition, multiple depot, routing decisions and location of parking places for trailers. Due to road limitations, a loaded truck with a trailer cannot be driven on roads leading to the farms and consequently the routes are constructed such that the trailer must be temporarily parked in designated parking places while the truck has to be driven on a sub route to collect milk at the farms.Routing decisions are concerned with assigning farms to a truck that is able to fulfill the farm's demand while satisfying the road restriction constraints. Farms may be serviced by one of three possible depots in this specific region. Here, depot is represented as a dairy/plant. Various types of trucks with or without trailers are available on the production factory. The capacity of each truck varies in terms of volume and weight.The remainder of this paper is structured as follows. In Section 2, a brief overview of research related to the milk collection problem is presented. Section 3 gives a detailed description of the real world problem while the solution methodology is explained in Section 4. Information about real world data and tuning of parameters are demonstrated in section 5. Computational results are presented in Section 6, and conclusions and future recommendations have been made in Section 7.2. Literature ReviewThe real world problem explained in this paper is a special type of truck and trailer vehicle routing problem which has not been directly addressed in the research literature. In the last two decades, only a few research works with some indirect relationship with the problem have been published. …

An improved evolutionary approach to the Extended Capacitated Arc Routing Problem

The Extended Capacitated Arc Routing Problem (ECARP) is a challenging vehicle routing problem with numerous real-world applications. We propose an improved evolutionary approach to cope with the ECARP in this research. The exploitation of heuristic information characterizes our approach. Two kinds of heuristic information, Arc Assignment Priority Information and Performance Information of Operators, are learned from the obtained near-optimal ECARP solutions. The Arc Assignment Priority Information is employed to decide one suitable broken position for the crossover and mutation operation, while the Performance Information of Operators is used to select an appropriate operator for the operations of selection, crossover and mutation. The effectiveness of our approach is demonstrated by 20 instances with up to 100 nodes and 360 arcs.

An Evolutionary Approach to the Multidepot Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real world applications. In this paper, an extended version of CARP, the multidepot capacitated arc routing problem (MCARP), is presented to tackle practical requirements. Existing CARP heuristics are extended to cope with MCARP and are integrated into a novel evolutionary framework: the initial population is constructed either by random generation, the extended random path-scanning heuristic, or the extended random Ulusoy's heuristic. Subsequently, multiple distinct operators are employed to perform selection, crossover, and mutation. Finally, the partial replacement procedure is implemented to maintain population diversity. The proposed evolutionary approach (EA) is primarily characterized by the exploitation of attributes found in near-optimal MCARP solutions that are obtained throughout the execution of the algorithm. Two techniques are employed toward this end: the performance information of an operator is applied to select from a range of operators for selection, crossover, and mutation. Furthermore, the arc assignment priority information is employed to determine promising positions along the genome for operations of crossover and mutation. The EA is evaluated on 107 instances with up to 140 nodes and 380 arcs. The experimental results suggest that the integrated evolutionary framework significantly outperforms these individual extended heuristics.

Adding more intelligence to the network routing problem: AntNet and Ga-agents

AntNet and GA-agent algorithms are benchmarked against a series of dynamic network routing problems. Performance is characterized using multiple performance metrics on the Japanese backbone (NTTNET). NTTNET is used on account of the elongated topology presenting a more challenging routing problem than in the case of the American backbone, which is basically square. The AntNet scheme is found to provide the best routing ability providing global information is available and network security is not a factor. The GA-agent algorithm is shown to provide routing performance between the AntNet algorithm with global information and that without, whilst avoiding global information requirements and satisfying typical models of network security.