1,875 research outputs found
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
Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments
The increased penetration of uncertain and variable renewable energy presents
various resource and operational electric grid challenges. Micro-level
(household and small commercial) demand-side grid flexibility could be a
cost-effective strategy to integrate high penetrations of wind and solar
energy, but literature and field deployments exploring the necessary
information and communication technologies (ICTs) are scant. This paper
presents an exploratory framework for enabling information driven grid
flexibility through the Internet of Things (IoT), and a proof-of-concept
wireless sensor gateway (FlexBox) to collect the necessary parameters for
adequately monitoring and actuating the micro-level demand-side. In the summer
of 2015, thirty sensor gateways were deployed in the city of Managua
(Nicaragua) to develop a baseline for a near future small-scale demand response
pilot implementation. FlexBox field data has begun shedding light on
relationships between ambient temperature and load energy consumption, load and
building envelope energy efficiency challenges, latency communication network
challenges, and opportunities to engage existing demand-side user behavioral
patterns. Information driven grid flexibility strategies present great
opportunity to develop new technologies, system architectures, and
implementation approaches that can easily scale across regions, incomes, and
levels of development
How 5G wireless (and concomitant technologies) will revolutionize healthcare?
The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution
A concise guide to existing and emerging vehicle routing problem variants
Vehicle routing problems have been the focus of extensive research over the
past sixty years, driven by their economic importance and their theoretical
interest. The diversity of applications has motivated the study of a myriad of
problem variants with different attributes. In this article, we provide a
concise overview of existing and emerging problem variants. Models are
typically refined along three lines: considering more relevant objectives and
performance metrics, integrating vehicle routing evaluations with other
tactical decisions, and capturing fine-grained yet essential aspects of modern
supply chains. We organize the main problem attributes within this structured
framework. We discuss recent research directions and pinpoint current
shortcomings, recent successes, and emerging challenges
Traffic assignment optimization models
Optimalizace toku v síti je klasickou aplikací matematického programování. Tyto modely mají, mimo jiné, široké uplatnění také v logistice, kde se tak snažíme docílit optimálního rozdělení dopravy, např. vzhledem k maximalizaci zisku, či minimalizaci nákladů. Toto pojetí ovšem často problém idealizuje, poněvadž předpokládá existenci jediného rozhodovatele. Takový přístup je možný ve striktně organizovaných sítích jako např. v logistických sítích přepravních společností, železničních sítích či armádním zásobování. Úloha ''Traffic Assignment Problem'' (TAP) se zaměřuje na dopady teorie her na optimalizaci toku, tj. zkoumá vliv více rozhodovatelů na celkové využití sítě. V práci se zaobíráme úlohou TAP s působením náhodných vlivů, k čemuž využíváme metod stochastické a vícestupňové optimalizace. Dále zkoumáme možnosti zlepšení stávajícího využití sítě za rozhodnutí autoritativního rozhodovatele, kterému je umožněn zásah do samotné struktury sítě, k čemuž využíváme víceúrovňové programování.The class of network flow problems is one of the traditional applications of mathematical optimization. Such problems are widely applicable for example in logistics to achieve an optimal distribution of flow with respect to maximization of profit, or minimization of costs. This approach often leads to simplified models of real problems as it supposes the existence of only one decision maker. Such approach is possible in centralised networks, where an authority exists (such as railway network, military supply, or logistic network used by any company). The Traffic Assignment Problem (TAP) deals with impact of game theory to the network flow problem. Hence, we assume multiple decision makers, where each one of them wants to find his optimal behaviour. In this thesis, we focus on stochastic influences in TAP, for which we use methods of stochastic and multi-stage programming. Further, we concentrate on improvement options for the utilization of the system. Hereby, we consider possible actions of the master decision maker, and discuss them by the presence of multi-level mathematical programming.
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Improving shared access to Cloud of Things resources.
Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed.
Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants.
The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability
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