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EWGT 2021, 24th Euro Working Group on Transportation Meeting, Aveiro, PORTUGAL, 08-/09/2021 - 10/09/2021; In a railway network, traffic is often perturbed and trains must be rerouted and rescheduled. Doing so in large networks is a challenging task, which has been tackled through various decomposition approaches in the literature. In this paper, we propose an algorithm for managing traffic considering dynamic problem decompositions. It is an asynchronous algorithm based on the decomposition of the problem considering at each time step the smallest possible portion of the network and subset of trains. We prove that this algorithm guarantees to find an overall feasible solution if it exists, for single track networks with passing loops.

In this paper, we study the Periodic Petrol Station Replenishment Problem (PPSRP), an important real problem relative to logistics in the fuel oil distribution. It consists, under suitable assumptions and subject to certain operational constraints, of determining the fuel oil procurement plans of a set of petrol stations and planning the delivering routes of petrol products to the stations along a certain planning horizon. The considered problem belongs to the class of Inventory Routing Problems (IRP), of which it is a particularization. In particular, it draws its concepts from two different classes of distribution problems, the Periodic Vehicle Routing Problem (PVRP) and the Petrol Station Replenishment Problem (PSRP). We provide a mathematical formulation of the problem, and due to the large size of the real instances which in general an oil company has to deal with we heuristically solve it. We propose different heuristic strategies based on a common partitioning-then-routing paradigm, and in order to assess their performances we test them on a set of scenarios coming from Italian fuel oil distribution real cases and compare the results with those obtained by using other known heuristic approaches.

In this paper the authors consider the final distribution of fuel oil from a storage depot to a set of petrol stations faced by an oil company, which has to decide the weekly replenishment plan for each station, and determine petrol station visiting sequences (vehicle routes) for each day of the week, assuming a fleet of homogeneous vehicles (tankers). The aim is to minimize the total distance travelled by tankers during the week, while loading tankers possibly near to their capacity in order to maximize the resource utilization. The problem is modelled as a generalization of the Periodic Vehicle Routing Problem (PVRP). Due to the large size of the real instances which the company has to deal with, the authors solve the problem heuristically. The authors propose a hybrid genetic algorithm that successfully addresses the problem inspired to a known hybrid genetic algorithm from the literature for the PVRP. However, the proposed algorithm adopts some techniques and features tailored for the particular fuel oil distribution problem, and it is specifically designed to deal with real instances derived from the fuel oil distribution in the European context that are profoundly different from the PVRP instances available from literature. The proposed algorithm is evaluated on a set of real case studies and on a set of randomly generated instances that hold the same characteristics of the former.

One of the most important objectives of a manufacturing company is the optimization of the distribution of the produced goods considering the whole value chain. Unfortunately, in many companies the performance of the supply chain depends on many uncertain factors that are difficult to predict. The only way to face them is to adopt innovative solutions and tools that allow a swift response to the market changes. This paper analyzes the distribution processes managed by the logistics department of a large company producing and distributing petroleum products through the following main steps: crude oil's transportation typically from many countries to a refinery; refining process; maritime transportation from the refinery to three costal depots; road transport from depots to gas stations. The analyzed process is the primary supply, consisting in the maritime transport from the refinery to the coastal depots, liable to stochastic activities and events as weather condition. Through simulating the primary supply, we study the effects that the ship traffic generates on the overall variance of inventory levels at the costal depots with respect to specific inventory level targets, and analyze the impact of different tactical decision choices on the variance reduction. Reducing inventory's variance, through a better control of the distribution, allows the company to reduce inventory target levels and hence to reduce inventory costs in term of capital stock, while keeping the same risk level of stock out. The project is made of many phases: map all relevant processes to have a complete vision of transport's structure; conduct a statistical analysis to identify specific statistical distributions of every ships’ process (delay, mooring, loading, etc.); model and simulate the primary supply using simulation software; use the model to make a “what-if” analysis. Within this project, it has been possible to realize a model that presents stochastic elements. All these phases are supported by six-sigma methodology, which focalizes on defects’ process reduction by the control of its mean square deviation and following the stages of the DMAIC (Define Measure Analyze Improve Control). One of the what-if analysis which has been done consists in simulating the opening refinery's jetties h24, because currently these are closed during the night. Opening the jetties, will increase the capacity of some of the bottleneck resources for the oil distribution process, and thanks to the simulation model we can estimate quickly the effects on the oil transport system.