Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

Multi-Objective Optimization of Green Transportation Operations in Supply Chain Management

Supply chain is the integration of manufacturing process where raw materials are converted into final products, then delivered to customers. Supply chains consists of two basic integrated process that interact together: (1) production and inventory and (2) distribution and logistics. Maximizing competitiveness and profitability are of the main goals of a supply chain. Accounting only for economic impacts as variable and fixed costs does not serve the main goal of the supply chain. Therefore, considering customer satisfaction measures in distribution models is essential in supply chain management. Models that addressed the three objectives simultaneously handled one of the objectives as a constraint with a certain threshold in the problem, while others used weighted utility functions to address the problem objective in deterministic environment. This thesis focuses on the multi-objective Vehicle Routing Problem (VRP) in green environment. The proposed Green VRP (GVRP) deals with three different objectives simultaneously that considers economic, environmental, and social aspects. A new hybrid search algorithm to solve the capacitated VRP is presented and validated in Chapter 2. The developed algorithm combines the evolutionary genetic search with a new local search heuristic that considers both locations and demand quantities of the nodes to be visited in routing decisions, not just the distances travelled. The algorithm is then used to solve the multi-objective GVRP in Chapter 3. The objectives of the developed GVRP model are minimizing the total transportation operations cost, minimizing the fuel consumption, and maximizing customer satisfaction. Moreover, a new overlap index is developed to measure the amount of overlap between customers’ time windows that provides an indication of how tight/constrained the problem is. The model is then adapted to consider the uncertainty in travel times, service times, and unpredictable demands of customers in Chapter 4. Pareto fronts were obtained and trade-offs between the three objectives are presented in both deterministic and stochastic forms. Furthermore, analysis of the effects of changing vehicle capacity and customer time windows relaxation are presented

Vehicle sharing and workforce scheduling to perform service tasks at customer sites

Most of the research done in the Vehicle Routing Problem (VRP) assumes that each driver is assigned to one and only one vehicle. However, in recent years, research in the VRP has increased its scope to further accommodate more restrictions and real-life features. In this line, vehicle sharing has grown in importance inside large companies with the aim of reducing vehicle emissions. The aim of this thesis is to study different situations where sharing vehicles brings an improvement. Our main study focuses on developing a framework that is capable of assigning different workers to a common vehicle, allowing them to share their journey. We introduce a mathematical programming model that combines the vehicle routing and the scheduling problem with time constraints that allows workers to share vehicles to perform their activities. To deal with bigger instances of the problem an algorithm capable of solving large scenarios needs to be implemented. A multi-phase algorithm is introduced, Phase 1 allows us to solve the non-sharing scheduling/routing problem whose aim is to find the best schedule for workers. Phase 2 will merge the allocated workers into common vehicles when possible, while Phase 3 is the improvement procedure of the algorithm. The algorithm is tested in three different settings; using workers as drivers, hiring dedicated drivers, and allowing workers to walk between jobs when possible. Results show that sharing vehicles is practicable under specific conditions, and it is able to reduce both the number of vehicles and the total distance, without affecting the performance of workers schedule

Transportation optimization for the collection of end-of-life vehicles

Firms operating in the purchasing of end-of-life vehicles (ELVs) have significant challenges related to the fact that most of the purchased ELVs must be collected efficiently in order to minimize their transportation costs. In this project, we study a reverse logistics problem of a Canadian firm that collects ELVs from a group of dealers and accumulates them at its warehouse for part resale or recycling. This problem can be modeled as a vehicle routing problem (VRP) with different side-constraints. Although prior research has made several contributions to model and solve different variants of the VRP, the specific issue in this project considers solving a VRP with a new combination of constraints, such as customer assignment to the private fleet or an external carrier, time-windows, multi-trip, and loading sequences. We propose a mixed-integer linear programming (MILP) model as well as a heuristic algorithm capable of finding the routes’ planning that minimizes the total transportation costs. The performance of the proposed methods is assessed by generated instances using the data obtained from the company

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

A study on the heterogeneous fleet of alternative fuel vehicles: Reducing CO2 emissions by means of biodiesel fuel

In the context of home healthcare services, patients may need to be visited multiple times by different healthcare specialists who may use a fleet of heterogeneous vehicles. In addition, some of these visits may need to be synchronized with each other for performing a treatment at the same time. We call this problem the Heterogeneous Fleet Vehicle Routing Problem with Synchronized visits (HF-VRPS). It consists of planning a set of routes for a set of light duty vehicles running on alternative fuels. We propose three population-based hybrid Artificial Bee Colony metaheuristic algorithms for the HF-VRPS. These algorithms are tested on newly generated instances and on a set of homogeneous VRPS instances from the literature. Besides producing quality solutions, our experimental results illustrate the trade-offs between important factors, such as CO2 emissions and driver wage. The computational results also demonstrate the advantages of adopting a heterogeneous fleet rather than a homogeneous one for the use in home healthcare services

Mixed Integer Programming Approaches to Novel Vehicle Routing Problems

This thesis explores two main topics. The first is how to incorporate data on meteorological forecasts, traffic patterns, and road network topology to utilize deicing resources more efficiently. Many municipalities throughout the United States find themselves unable to treat their road networks fully during winter snow events. Further, as the global climate continues to change, it is expected that both the number and severity of extreme winter weather events will increase for large portions of the US.We propose to use network flows, resource allocation, and vehicle routing mixed integer programming approaches to be able to incorporate all of these data in a winter road maintenance framework. We also show that solution approaches which have proved useful in network flows and vehicle routing problems can be adapted to construct high-quality solutions to this new problem quickly. These approaches are validated on both random and real-world instances using data from Knoxville, TN.In addition to showing that these approaches can be used to allocate resources effectively given a certain deicing budget, we also show that these same approaches can be used to help determine a resource budget given some allocation utility score. As before, we validate these approaches using random and real-world instances in Knoxville, TN.The second topic considered is formulating mixed integer programming models which can be used to route automated electric shuttles. We show that these models can also be used to determine fleet composition and optimal vehicle characteristics to accommodate various demand scenarios. We adapt popular vehicle routing solution techniques to these models, showing that these strategies continue to be relevant and robust. Lastly, we validate these techniques by looking at a case study in Greenville, SC, which recently received a grant from the Federal Highway Administration to deploy a fleet of automated electric shuttles in three neighborhoods

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process