Planning of Truck Platoons: a Literature Review and Directions for Future Research

A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

TABU SEARCH FOR THE MULTI-MODE RESOURCE CONSTARINED PROJECT SCHEDULING PROBLEM WHITH RESOURCE FLEXIBILITY

International audienceThe scheduling problem under study may be viewed as an extension of the standard Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) including Multi-Skilled Labor and will be called as MRCPSP-MS. This problem requires an integration of resource limitation, labor skills, and multiple possible execution modes for each task, and the objective is to minimize the overall project duration. This paper present a new tabu search (TS) algorithm using a powerful neighborhood function based on a flow graph representation in order to implement various search strategies. The search of the solution space is carried out via two types of moves. Furthermore, the TS algorithm is embedded in a decomposition based heuristic (DBH) which serve to reduce the solution space. The effectiveness of the new Tabu Search is demonstrated through extensive experimentation on different standard benchmark problem instances and proves that our results are competitive

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

An integrated framework for freight forwarders:exploitation of dynamic information for multimodal transportation

Advent of real-time information broadcasting technologies, growth in demand for air-cargo, and increased congestion and variability on air-road network, are the main forces compelling today\u27s air-freight forwarders to improve their operational decision-making to be more competitive and responsive to needs of customers. This research studies the air-cargo transportation on both road (short-haul) and air (long haul) network from the perspective of a mid-size freight forwarder. We develop a routing algorithm for congestion avoidance on air-network based on historical data and introduce an innovative approach to incorporate real-time information to enable dynamic routing of cargo on a stochastic air-network. In the road network, we introduce a new class of pickup and delivery problems to carry out the customer load pickups, fleet management, cargo-to-flight assignments, and airport deliveries in a multiple airport region under alternative access airport policy. The main contributions of this research to the air-cargo literature are the study of the value of real-time information and introduction of the concept of dynamic air-cargo routing. In addition, this is the first study that provides an operational framework to implement the alternative access airport policy. This research also contributes to operations research and logistics literature by introducing a new class of pickup and deliveries with time-sensitive and pair-dependent cost structure. It also contributes an innovative algorithm based on successive subproblem solving for Lagrangian decomposed mixed integer programming that shows to be efficient in obtaining near optimal solutions in reasonable time. The performances of the algorithms presented in this research are tested through experimental and real-world case studies. The results demonstrate that dynamic routing with real-time information can dramatically improve delivery reliability and reduce expected cost on the air-network. Moreover, they confirm that alternative access airport policy can greatly enhance a forwarder\u27s options and reduce the operational and service costs while improving the service levels

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans