An exact solution framework for multitrip vehicle-routing problems with time windows

Multitrip vehicle-routing problems (MTVRPs) generalize the well-known VRP by allowing vehicles to perform multiple trips per day. MTVRPs have received a lot of attention lately because of their relevance in real-life applications - for example, in city logistics and last-mile delivery. Several variants of the MTVRP have been investigated in the literature, and a number of exact methods have been proposed. Nevertheless, the computational results currently available suggest that MTVRPs with different side constraints require ad hoc formulations and solution methods to be solved. Moreover, solving instances with just 25 customers can be out of reach for such solution methods. In this paper, we proposed an exact solution framework to address four different MTVRPs proposed in the literature. The exact solution framework is based on a novel formulation that has an exponential number of variables and constraints. It relies on column generation, column enumeration, and cutting plane. We show that this solution framework can solve instances with up to 50 customers of four MTVRP variants and outperforms the state-of-the-art methods from the literature

Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by the lack of efficient solution schemes. In this paper we study two-stage robust optimization problems with decision dependent information discovery where uncertainty appears in the objective function. The contributions of the paper are twofold: (i) we develop an exact solution scheme based on a nested decomposition algorithm, and (ii) we improve upon the existing K-adaptability approximate by strengthening its formulation using techniques from the integer programming literature. Throughout the paper we use the orienteering problem as our working example, a challenging problem from the logistics literature which naturally fits within this framework. The complex structure of the routing recourse problem forms a challenging test bed for the proposed solution schemes, in which we show that exact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting. We leverage the effectiveness of the proposed solution schemes and the orienteering problem in a case study from Alrijne hospital in the Netherlands, where we try to improve the collection process of empty medicine delivery crates by co-optimizing sensor placement and routing decisions

The role of individual compensation and acceptance decisions in crowdsourced delivery

High demand, rising customer expectations, and government regulations are forcing companies to increase the efficiency and sustainability of urban (last-mile) distribution. Consequently, several new delivery concepts have been proposed that increase flexibility for customers and other stakeholders. One of these innovations is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. In addition to reducing delivery costs, the potential benefits of crowdsourced delivery include better utilization of transport capacity, a reduction in overall traffic, and increased flexibility (by scaling up and down delivery capacity as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process of assigning delivery tasks to occasional drivers. We propose a mixed-integer nonlinear model that minimizes the expected delivery costs while identifying optimal assignments of tasks to a mix of traditional and occasional drivers and their compensation. We propose exact linearization schemes for two practically relevant probability functions and an approximate linearization scheme for the general case. The results of our computational study show clear advantages of our new approach over existing ones

Spatially-resolved analysis of edge-channel equilibration in quantum Hall circuits

We demonstrate an innovative quantum Hall circuit with variable geometry employing the moveable electrostatic potential induced by a biased atomic force microscope tip. We exploit this additional degree of freedom to identify the microscopic mechanisms that allow two co-propagating edge channels to equilibrate their charge imbalance. Experimental results are compared with tight-binding simulations based on a realistic model for the disorder potential. This work provides also an experimental realization of a beam mixer between co-propagating edge channels, a still elusive building block of a recently proposed new class of quantum interferometers

Distribution pattern of hepatitis C virus genotypes and correlation with viral load and risk factors in chronic positive patients.

Objective: Hepatitis C virus (HCV) has emerged as a leading cause of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma worldwide. The purpose of this study was to describe the distribution pattern of HCV genotypes in chronic hepatitis patients in the Campania region of southern Italy and estimate their association with risk factors and viral load. Materials and Methods: 404 consecutive HCV ribonucleic acid-positive patients were included in the study. HCV genotyping was carried out by the HCV line probe assay test and viral load estimation by the TaqMan real-time PCR system. Results: The predominant genotype was 1 (63.6%), followed by genotype 2 (29.4%), 3 (6.2%) and 4 (0.8%). Subtype 1b was more frequent in females than in males. Conversely, genotype 3 was more frequent in males. No significant difference was observed in age distribution of HCV genotypes. Surgery and dental therapy were the most frequent risk factors for genotype 1 and intravenous drug abuse and tattooing for genotype 3. Patients with genotype 1 more frequently showed high HCV viral load when compared to those with genotypes 2 and 3. Conclusion: The present study revealed that HCV genotypes 1 and 2 accounted for over 95% of all HCV infections in the Campania region, and genotype 1 was more frequently associated with a higher viral load when compared to genotypes 2 and 3

The inventory routing problem under uncertainty with perishable products:an application in the agri-food supply chain

In this paper, we propose a dynamic and stochastic approach for an inventory routing problem in which products with a high perishability must be delivered from a supplier to a set of customers. This problem falls within the agri-food supply chain (ASC) management field, which includes all the activities from production to distribution. The need for high-quality products that are subject to perishability is a critical issue to consider in the ASC optimization. Moreover, the demand uncertainty makes the problem very challenging. In order to effectively manage all these features, a rolling horizon approach based on a multistage stochastic linear program is proposed. Computational experiments over medium-size instances designed on the basis of the real data provided by an agri-food company operating in Southern Italy show the effectiveness of the proposed approach

Dynamic Time Window Assignment for Next-Day Service Routing

We consider a problem where customers dynamically request next-day home service, e.g., repair or instalment. Unlike attended home delivery, customers cannot select a time window (TW), but the service provider assigns a next-day TW to each new customer if the customer can feasibly be inserted in the service route of the next day without violating the TWs of the existing customers. Otherwise, the customer service is postponed to another day (which is outside of the scope of this work). For fast service and efficient operations, the provider aims to serve many customers the next day. Thus, TWs have to be assigned to keep the flexibility of the fleet for future requests. For such anticipatory assignments, we propose a stochastic lookahead method that samples a set of future request scenarios, solves the corresponding team orienteering problems with TWs, and uses the solutions to evaluate current TW-assignment decisions. For real-time solutions of the TOP, we propose to approximate its optimal solution value with a tight upper bound. The bound is obtained by solving the linear relaxation of a set packing reformulation via column generation. We test our algorithm on Iowa City data and compare it to several benchmark policies. The results show that our method increases customer service significantly and that our relaxation is essential for effective decisions. We further show that our policy does not lead to observable discrimination against inconveniently located customers

Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by the lack of efficient solution schemes. In this paper we study two-stage robust optimization problems with decision dependent information discovery where uncertainty appears in the objective function. The contributions of the paper are twofold: (i) we develop an exact solution scheme based on a nested decomposition algorithm, and (ii) we improve upon the existing K-adaptability approximate by strengthening its formulation using techniques from the integer programming literature. Throughout the paper we use the orienteering problem as our working example, a challenging problem from the logistics literature which naturally fits within this framework. The complex structure of the routing recourse problem forms a challenging test bed for the proposed solution schemes, in which we show that exact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting. We leverage the effectiveness of the proposed solution schemes and the orienteering problem in a case study from Alrijne hospital in the Netherlands, where we try to improve the collection process of empty medicine delivery crates by co-optimizing sensor placement and routing decisions

Fault rupture and aseismic creep accompanying the December 26, 2018, Mw 4.9 Fleri earthquake (Mt. Etna, Italy): Factors affecting the surface faulting in a volcano-tectonic environment

On December 26, 2018 (2:19 UTC), during a volcanic eruption on the Mt. Etna eastern flank (Sicily, southern Italy), the largest instrumental earthquake ever recorded in the volcano ruptured the Fiandaca Fault, with epicenter between Fleri and Pennisi villages (hypocenter at ca. 300 m a. s. l., Mw 4.9). This was the mainshock of an earthquake swarm and it was accompanied by widespread surface faulting and extensive damage along a narrow belt near the fault trace. Few hours after the mainshock, an episodic aseismic creep event occurred along the Aci Platani Fault, a SE extension of the Fiandaca Fault, which caused several damages in the Aci Platani village. We surveyed and mapped the coseismic and aseismic ground ruptures, and collected structural data on their geometry, displacement, and fault zone fabric. We compared the mapped surface ruptures with topography, lithology, and morphology of the buried top of the sedimentary basement. We conclude that the geometry of the volcanic pile influenced the surface expression of faulting during the December 26, 2018 event. The top surface of the marly clay basement should be considered as a detachment surface for shallow sliding blocks. The earthquake occurred on top of a depression of the sedimentary basement forcing the sliding eastward, causing at surface the re-arrangement of the fault strand pattern and deformation style, switching from shear faulting to a tensile failure. The Fleri earthquake therefore provides an unprecedented dataset for 1) understanding active faulting in the European largest onshore volcano, 2) modeling its complex dynamics, and 3) contributing to a more refined surface faulting hazard assessment at Mt. Etna. Results from this investigation might be useful for characterizing capable faulting in similar volcano-tectonic settings worldwide