Martijn R.K. Mes

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 multi-agent 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.

A generalized simulation model of an integrated emergency post

This paper discusses the development of a discrete-event simulation model for an integrated emergency post. This post is a collaboration between a general practitioners post and an emergency department within a hospital. We present a generalized and flexible simulation model, which can easily be adapted to several emergency departments as well as to other departments within the hospital, as we demonstrate with our application to the integrated emergency post. Here, generalization relates to the way we model patient flow, patient prioritization, resource allocation, and process handling. After presenting the modeling approach, we shortly describe the implemented and validated model of the integrated emergency post, and describe how it is currently being used by health care managers to analyze the effects of organizational interventions.

Simulation framework to analyze operating room release mechanisms

The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties, causes inflexibility for scheduling outside the BT, which negatively affects new surgeons, new specialties, and specialties that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept of releasing ORs, and present a generic simulation and evaluation framework that can be used by hospitals to evaluate various release mechanisms. The simulation and evaluation framework is illustrated by a case study at Vanderbilt Medical Center and University (VUMC) in Nashville. The results show that introducing a release policy has benefits in decreasing the number of unscheduled patients and decreasing access time, without affecting the specialties originally assigned to the released rooms.

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.

Inventory routing for dynamic waste collection

We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal effects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters.

Using Simulation to Assess the Opportunities of Dynamic Waste Collection

In this chapter, we illustrate the use of discrete event simulation to evaluate how dynamic planning methodologies can be best applied for the collection of waste from underground containers. We present a case study that took place at the waste collection company Twente Milieu, located in The Netherlands. Even though the underground containers are already equipped with motion sensors, the planning of container emptying’s is still based on static cyclic schedules. It is expected that the use of a dynamic planning methodology, that employs sensor information, will result in a more efficient collection process with respect to customer satisfaction, profits, and CO2 emissions. In this research we use simulation to (i) evaluate the current planning methodology, (ii) evaluate various dynamic planning possibilities, (iii) quantify the benefits of switching to a dynamic collection process, and (iv) quantify the benefits of investing in fill-level sensors. After simulating all scenarios, we conclude that major improvements can be achieved, both with respect to logistical costs as well as customer satisfaction.

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 different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a 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 strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach.

Sequential auctions for full truckload allocation

In this thesis we examine the use of sequential auctions for the dynamic allocation of transportation jobs. For all players, buyers and sellers, we develop strategies and examine their performance both in terms of individual benefits and with respect to the global logistical performance (resource utilization and delivery reliability).

Synchromodal Transport Planning at a Logistics Service Provider

In this chapter, we consider synchromodal planning of transport orders with the objective to minimize costs, delays, and CO2 emissions. Synchromodal planning is a form of multimodal planning in which the best possible combination of transport modes is selected for every transport order. The underlying problem is known as the multi-objective k-shortest path problem, in which we search for the k-shortest paths through a multimodal network, taking into account time-windows of orders, schedules for trains and barges, and closing times of hubs. We present a synchromodal planning algorithm that is implemented at a 4PL service provider located in the Netherlands. We illustrate our approach using simulation with order and network data from this logistics service provider. On the corridor from the Netherlands to Italy, an average cost reduction of 10.1 % and a CO2 reduction of 14.2 % can be achieved with synchromodal planning.

Towards an Approach for Long Term AIS-Based Prediction of Vessel Arrival Times

The goal of this paper is to conduct a review of existing solutions and related algorithms on maritime route prediction using Automatic Information System (AIS) data, determine to what extent they can be applied to solve the prediction problem, and identify areas that have to be improved in order to get an industry-acceptable solution to enhance various logistics planning processes. The contributions of this paper are: (i) to present the available solutions for trajectory prediction of a vessel; (ii) to identify components that can be used for finding a solution for the identified problem as well as showing the strengths and weaknesses of each available option; and (iii) to propose a new concept for arrival time estimation based on trajectory prediction and the use of algorithms from the included literature review.