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.
Crowdsourced shipping can result in significant economic and social benefits. For a shipping company, it has a potential cost advantage and creates opportunities for faster deliveries. For the society, it can provide desirable results by reducing congestion and air pollution. Despite the great potential, crowdsourced shipping is not well studied. With the aim of using the spare capacities along the existing transportation flows of the crowd to deliver small-to-medium freight volumes, this paper defines the multi-driver multi-parcel matching problem and proposes a general ILP formulation, which incorporates drivers' maximum detour, capacity limits, and the option of transferring parcels between drivers. Due to the high computational complexity, we develop two heuristics to solve the problem. The numerical study shows that crowdsourced shipping can be an economic viable and sustainable option, depending on the spatial characteristics of the network and drivers' schedules. Furthermore, the added benefits increase with an increasing number of participating drivers and parcels.
In this chapter, we consider synchromodal planning of transport orders with the objective to minimize costs, delays, and CO 2 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 CO 2 reduction of 14.2 % can be achieved with synchromodal planning.
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.
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