Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

In this paper, we have investigated a drone delivery problem to address the tactical decisions arising in last-mile applications where the connection with operational plans is taken into account. The problem deals with the tactical selection of a subset of FCs to launch and retrieve the drones, and the fleet sizing decisions on the optimal number of drones to be employed. We have incorporated the non-linear and load-dependent energy consumption function into the definition of a load-indexed layered network, leading to the definition of a MILP that can be efficiently solved for instances with 50 and 75 customers. There are several fruitful directions for future research. The use of shared depots implies for the drones the freedom to choose different FCs for departure and arrival. Anyway, a drawback may exist in the considered scenario, since we should have enough drones in each FC for the next period. The extension of the present model to the multi-period location routing case, where the location decisions are taken once and the routing plans are addressed within each period, is an interesting issue for future research. Moreover, the design of heuristic and self-adaptive approaches to alleviate the computational burden for larger instances deserves further attention, as well as the extension of the present model to en-route drone charging

the optimal electric energy procurement problem under reliability constraints

Abstract We consider the problem faced by a large consumer that has to define the procurement plan to cover its energy needs. The uncertain nature of the problem, related to the spot price and energy needs, is dealt by the stochastic programming framework. The proposed approach provides the decision maker with a proactive strategy that covers the energy needs with a high reliability level and integrates the Conditional Value at Risk (CVaR) measure to control potential losses. We apply the approach to a real case study and emphasize the effect of the reliability value choice and the difference between risk neutral and adverse positions

A machine learning optimization approach for last-mile delivery and third-party logistics

Third-party logistics is now an essential component of efficient delivery systems, enabling companies to purchase carrier services instead of an expensive fleet of vehicles. However, carrier contracts have to be booked in advance without exact knowledge of what orders will be available for dispatch. The model describing this problem is the variable cost and size bin packing problem with stochastic items. Since it cannot be solved for realistic instances by means of exact solvers, in this paper, we present a new heuristic algorithm able to do so based on machine learning techniques. Several numerical experiments show that the proposed heuristics achieve good performance in a short computational time, thus enabling its real-world usage. Moreover, the comparison against a new and efficient version of progressive hedging proves that the proposed heuristic achieves better results. Finally, we present managerial insights for a case study on parcel delivery in Turin, Italy

The multi-vehicle profitable pick up and delivery routing problem with uncertain travel times

Abstract This paper addresses a variant of the known selective pickup and delivery problem with time windows. In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of customers requests consisting in transporting goods from a supplier (pickup location) to a customer (delivery location). The selective aspect consists in choosing the customers to be served on the basis of the profit collected for the service. Motivated by urban settings, wherein road congestion is an important issue, in this paper, we address the profitable pickup and delivery problem with time windows with uncertain travel times. The problem under this assumption, becomes much more involved. The goal is to find the solution that maximizes the net profit, expressed as the difference between the collected revenue, the route cost and the cost associated to the violation the time windows. This study introduces the problem and develops a solution approach to solve it. Very preliminary tests are performed in order to show the efficiency of developed method to cope with the problem at hand

Decentralizing Electric Vehicle Supply Chains: Value Proposition and System Design

Distributed ledger technologies are transforming existing business models and business relationships. In particular, blockchain allows non-trusting parties to manage a shared database in a decentralized way and improve the transparency, authenticity, and reliability of the exchanged data. Nonetheless, decentralized paradigms are not yet well established, resulting in only a fraction of blockchain-based applications being successful in the long term.In this paper, we present a blockchain-based solution for the electric vehicle supply chain that we designed in the context of the CONCORDIA project of the European Cybersecurity Competence Network. We describe the goals, the value proposition, the main design choices, and the architecture of our system. Moreover, we discuss the electric vehicle supply chain, analyzing the improvements and limitations introduced by our blockchain-based solution. We analyze our solution from the managerial and technical points of view through a lean business methodology for blockchain solutions. In particular, we developed an economic impact assessment to evaluate the potential costs and revenues of the application of blockchain technology in a supply chain context. Although the blockchain system is inspired by the supply chain of a multinational automotive company, it can be applied to any other multi-actor supply chain

Antimicrobial Activity of Lactoferrin-Related Peptides and Applications in Human and Veterinary Medicine

Antimicrobial peptides (AMPs) represent a vast array of molecules produced by virtually all living organisms as natural barriers against infection. Among AMP sources, an interesting class regards the food-derived bioactive agents. The whey protein lactoferrin (Lf) is an iron-binding glycoprotein that plays a significant role in the innate immune system, and is considered as an important host defense molecule. In search for novel antimicrobial agents, Lf offers a new source with potential pharmaceutical applications. The Lf-derived peptides Lf(1–11), lactoferricin (Lfcin) and lactoferrampin exhibit interesting and more potent antimicrobial actions than intact protein. Particularly, Lfcin has demonstrated strong antibacterial, anti-fungal and antiparasitic activity with promising applications both in human and veterinary diseases (from ocular infections to osteo-articular, gastrointestinal and dermatological diseases)

A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process

<p>Abstract</p> <p>Background</p> <p>The E1 protein of Hepatitis C Virus (HCV) can be dissected into two distinct hydrophobic regions: a central domain containing an hypothetical fusion peptide (FP), and a C-terminal domain (CT) comprising two segments, a pre-anchor and a trans-membrane (TM) region. In the currently accepted model of the viral fusion process, the FP and the TM regions are considered to be closely juxtaposed in the post-fusion structure and their physical interaction cannot be excluded. In the present study, we took advantage of the natural sequence variability present among HCV strains to test, by purely sequence-based computational tools, the hypothesis that in this virus the fusion process involves the physical interaction of the FP and CT regions of E1.</p> <p>Results</p> <p>Two computational approaches were applied. The first one is based on the co-evolution paradigm of interacting peptides and consequently on the correlation between the distance matrices generated by the sequence alignment method applied to FP and CT primary structures, respectively. In spite of the relatively low random genetic drift between genotypes, co-evolution analysis of sequences from five HCV genotypes revealed a greater correlation between the FP and CT domains than respect to a control HCV sequence from Core protein, so giving a clear, albeit still inconclusive, support to the physical interaction hypothesis.</p> <p>The second approach relies upon a non-linear signal analysis method widely used in protein science called Recurrence Quantification Analysis (RQA). This method allows for a direct comparison of domains for the presence of common hydrophobicity patterns, on which the physical interaction is based upon. RQA greatly strengthened the reliability of the hypothesis by the scoring of a lot of cross-recurrences between FP and CT peptides hydrophobicity patterning largely outnumbering chance expectations and pointing to putative interaction sites. Intriguingly, mutations in the CT region of E1, reducing the fusion process <it>in vitro</it>, strongly reduced the amount of cross-recurrence further supporting interaction between this region and FP.</p> <p>Conclusion</p> <p>Our results support a fusion model for HCV in which the FP and the C-terminal region of E1 are juxtaposed and interact in the post-fusion structure. These findings have general implications for viruses, as any visualization of the post-fusion FP-TM complex has been precluded by the impossibility to obtain crystallised viral fusion proteins containing the trans-membrane region. This limitation gives to sequence based modelling efforts a crucial role in the sketching of a molecular interpretation of the fusion process. Moreover, our data also have a more general relevance for cell biology as the mechanism of intracellular fusion showed remarkable similarities with viral fusion</p