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

Dynamic threshold policy for delaying and breaking commitments in transportation auctions

In this paper we consider a transportation procurement auction consisting of shippers and carriers. Shippers offer time sensitive pickup and delivery jobs and carriers bid on these jobs. We focus on revenue maximizing strategies for shippers in sequential auctions. For this purpose we propose two strategies, namely delaying and breaking commitments. The idea of delaying commitments is that a shipper will not agree with the best bid whenever it is above a certain reserve price. The idea of breaking commitments is that the shipper allows the carriers to break commitments against certain penalties. The benefits of both strategies are evaluated with simulation. In addition we provide insight in the distribution of the lowest bid, which is estimated by the shippers

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

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

Seneca’s Challenge:Genre and Intertextuality in Senecan Tragedy and Statius’ Thebaid

In this dissertation I study the intertextual relationship between the poetic genres of epic and tragedy in Latin literature. The focus of my research are Seneca’s tragedies and Statius’ Thebaid. I argue that Seneca’s tragedies should be read as a systematic reflection on the epics of his predecessors Vergil (Aeneid) and Ovid (Metamorphoses). In the Aeneid, Vergil frequently alludes to tragedy, a genre in which heroes and cities often are destroyed, thereby questioning and complicating his epic narrative of Rome’s glorious destiny. Ovid develops this approach further through clever allusions to Vergil. Ovid’s allusions to Vergil can be read as a commentary on Vergil’s epic. Seneca, in turn, would write tragedies that alluded to both Vergil and Ovid. Seneca’s tragedies test the epic genre to its breaking point: heroes self-destruct, history is reversed and the epic gods are driven away. I show this through a close intertextual reading of four of Seneca’s tragedies (Hercules Furens, Oedipus, Thyestes and Phoenissae), in which I trace a development from the “early” to the “late” tragedies. I then move on to the most sustained epic reponse to Seneca’s tragic challenge: Statius’ Thebaid. Statius has to reinvent the epic genre following Senecan tragedy. I argue that a Senecan narrative can be traced through the Thebaid, in which Statius alludes to Senecan tragedy and comments on these tragedies and their impact on epic. At the end of Statius’ experiment, epic can continue, but it is forever changed by Senecan tragedy

The Relationship between Wirk Pressure, Mobbing at Work, Health Complaints and Absenteeism

In deze studie is onderzocht in hoeverre werkdruk een negatieve relatie heeft met ervaren gezondheid en een positieve relatie heeft met ziekteverzuim en in hoeverre gepest worden op het werk hierbij mogelijk een mediërend effect heeft

A variable depth approach for the single-vehicle pickup and delivery problem with time windows

Consider a single depot and a set of customers with known demands, each of which must be picked up and delivered at specified locations and has two time windows in which the pickup and delivery must take place. We seek a route and a schedule for a single vehicle with known capacity, which minimizes the route duration, i.e., the difference between the arrival time and the departure time at the depot. In this paper we present a local search method for this problem based on a variable depth approach, similar to the Lin-Kernighan algorithm for the traveling salesman problem. The method consists of two phases. In the first phase a feasible route is constructed. In the second phase this solution is iteratively improved. In both phases we use a variable depth search built up out of seven basic types of arc-exchange procedures. When tested on real-life problems the method is shown to produce near-optimal solutions in a reasonable amount of computation time. Despite this practical evidence, there is the theoretical possibility that the method may end up with a poor or even infeasible solution. As a safeguard against such an emergency, we have developed an alternative algorithm based on simulated annealing. As a rule, it finds high quality solutions in a relatively large computation time. Keywords: dial-a-ride, pickup and delivery, routing, scheduling, local search, variable depth, simulated annealing

Allocating service parts in two-echelon networks at a utility company

We study a multi-item, two-echelon, continuous-review inventory problem at a Dutch utility company, Liander. We develop a model that optimizes the quantities of service parts and their allocation in the two-echelon network under an aggregate waiting time restriction. Specific aspects that we address are emergency shipments in case of stockout, and batching for regular replenishment orders at the central warehouse. We use column generation as a basic technique to solve this problem, and use various building blocks for single-item models as columns. Further, we study options to derive simple classification rules from the solution of our multi-item, two-echelon service part optimization problem using statistical techniques. Application of our models at Liander yields a solution that reduces costs by 15% and decreases the impact of waiting time for service parts by 52%