Control of mobile networks using dynamic vehicle routing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 141-144).This thesis considers the Dynamic Pickup and Delivery Problem (DPDP), a dynamic multi-stage vehicle routing problem in which each demand requires two spatially separated services: pickup service at its source location and then delivery service at its destination location. The Dynamic Pickup and Delivery Problem arises in many practical applications, including taxi and courier services, manufacturing and inventory routing, emergency services, mobile sensor networks, Unmanned Aerial Vehicle (UAV) routing, and delay tolerant wireless networks. The main contribution of this thesis is the quantification of the delay performance of the Dynamic Pickup and Delivery Problem as a function of the number of vehicles, the total arrival rate of messages, the required message service times, the vehicle velocity, and the network area. Two lower bounds are derived. First, the Universal Lower Bound quantifies the impact of spatially separated service locations and system loading on average delay. The second lower bound is derived by reducing the two-stage Dynamic Pickup and Delivery Problem to the single-stage Dynamic Traveling Repairperson Problem (DTRP). Policies are then presented for which these lower bounds are tight as a function of the system scaling parameters (up to a constant). The impact of information and inter-vehicle relays is also studied. The last part of this thesis examines the application of the Dynamic Pickup and Delivery Problem to mobile multi-agent wireless networks from a physical layer perspective, seeking insights for the control of the network to achieve trade-offs between throughput and delay.by Holly A. Waisanen-Hatipoglu.Ph.D

Integrated Forward and Reverse Logistics Network Design

Many manufacturers are moving towards green manufacturing. One of the actions for environment friendly manufacturing is collection of end-of-life products (EOL). EOL products are transported to the proper facilities for reprocessing or proper disposal. Movement of collected products is performed through reverse logistics networks. Reverse logistics networks may be designed independent of forward logistics networks, or as integrated networks, known as integrated forward and reverse logistics (IFRL) networks. Recent research shows that IFRL networks are more efficient than independent networks. In this work, we study a number of IFRL networks. We present a comprehensive mathematical model to represent an assignment and location-routing IFRL network. Afterwards, this model is decomposed into a number of sub-models that represent different IFRL networks. For each network we develop a solution methodology to solve practical size problems. Two sub-models based on the comprehensive model are presented to design two IFRL location-routing networks. The first network considers decision on the location to establish a disassembly plant. The second network considers decisions on the location to establish a manufacturing facility. For both networks, routing decisions are assigning customers to vehicles, and establishing vehicles’ routes. We develop two heuristic methods to solve the models. The heuristics are able to reach optimal or near optimal solutions in reasonable computational times. The vehicle routing problem with simultaneous pickup and delivery and time windows (VRPSPD-TW) is studied in this work. We use a sub-model of the comprehensive model to represent the problem. Classic heuristics and intelligent optimization or metaheuristics are widely used to solve similar problems. Therefore, we develop a heuristic method to solve the VRPSPD-TW. Results of the heuristic serve as initial solutions for a simulated annealing (SA) approach. For most tested problems, the SA approach is able to improve the heuristic solutions, and reach optimal solutions. Computational times are reasonable for the heuristic and SA. We also study the multi-depot vehicle routing problem with simultaneous pickup and delivery and time windows (MDVRPSPS-TW). A sub-model of the comprehensive model represents the problem. The network considers assignment of customers and vehicles to depots, assignment of customers to vehicles and routing of vehicles within customers’ time windows. We develop a 2-phase heuristic and a SA approach to solve the problem. Heuristic solutions serve as initial solutions for the SA approach. SA is able to reach optimum or near optimum solutions. Computational times are reasonable for the heuristic and S

Look-ahead strategies for dynamic pickup and delivery problems

In this paper we consider a dynamic full truckload pickup and delivery problem with time-windows. Jobs arrive over time and are offered in a second-price auction. Individual vehicles bid on these jobs and maintain a schedule of the jobs they have won. We propose a pricing and scheduling strategy based on dynamic programming where not only the direct costs of a job insertion are taken into account, but also the impact on future opportunities. Simulation is used to evaluate the benefits of pricing opportunities compared to simple pricing strategies in various market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization, and delivery reliability

Cargo Consolidation and Distribution Through a Terminals-Network: A Branch-And-Price Approach

Less-than-truckload is a transport modality that includes many practical variations to convey a number of transportation-requests from the origin locations to their destinations by using the possibility of goods-transshipments on the carrier?s terminals-network. In this way logistics companies are required to consolidate shipments from different suppliers in the outbound vehicles at a terminal of the network. We present a methodology for finding near-optimal solutions to a less-than-truckload shipping modality used for cargo consolidation and distribution through a terminals-network. The methodology uses column generation combined with an incomplete branch-and-price procedure.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Opportunity costs calculation in agent-based vehicle routing and scheduling

In this paper we consider a real-time, dynamic pickup and delivery problem with timewindows where orders should be assigned to one of a set of competing transportation companies. Our approach decomposes the problem into a multi-agent structure where vehicle agents are responsible for the routing and scheduling decisions and the assignment of orders to vehicles is done by using a second-price auction. Therefore the system performance will be heavily dependent on the pricing strategy of the vehicle agents. We propose a pricing strategy for vehicle agents based on dynamic programming where not only the direct cost of a job insertion is taken into account, but also its impact on future opportunities. We also propose a waiting strategy based on the same opportunity valuation. Simulation is used to evaluate the benefit of pricing opportunities compared to simple pricing strategies in different market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization and delivery reliability

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level