Delivery by Drones with Arbitrary Energy Consumption Models: A New Formulation Approach

This paper presents a new approach for formulating the delivery problem by drones with general energy consumption models where the drones visit a set of places to deliver parcels to customers. Drones can perform multiple trips that start and end at a central depot while visiting several customers along their paths. The problem determines the routing and scheduling decisions of the drones in order to minimize the total transportation cost of serving customers. For the first time, the new formulation approach enables us to use the best available energy consumption model without the need of any extra approximations. Though the approach works in a very general setting including non-convex energy consumption models, it is also computationally efficient as the resulting optimization model has a linear relaxation. A numerical study on 255 benchmark instances with up to 50 customers and a specific energy function indicate that all the instances can be solved 20 times faster on average using the new formulation when compared to the best existing branch-and-cut algorithm. All the 15 benchmark instances with 50 customers are solved exactly, whereas none of them has been solved optimally before. Moreover, new instances with up to 150 customers are solved with small error bounds within a few hours. The new approach can be simply applied to consider the extra energy required when a drone needs to continue hovering until opening the delivery time window. It can also be applied to the case where the flight time is dependent on the drone's payload weight. Owing to the flexibility of the new approach, these challenging extensions are formulated as linear optimization models for the first time

Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion

Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of mixed-inter convex programs, which can be solved very efficiently due to the recent advances in optimization solvers. Our paper bridges the gap between modeling a class of optimization problems and using MISOCP solvers. It is shown how various performance metrics of M/G/1 queues can be molded by different MISOCPs. To motivate our method practically, it is first applied to a challenging stochastic location problem with congestion, which is broadly used to design socially optimal service networks. Four different MISOCPs are developed and compared on sets of benchmark test problems. The new formulations efficiently solve large-size test problems, which cannot be solved by the best existing method. Then, the general applicability of our method is shown for similar optimization problems that use queue-theoretic performance measures to address customer satisfaction and service quality

Relationship between IoT Service User Quality and Network QoS Factors

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such as packet loss, latency, and jitter. The results of this investigation open up possibilities for additional research into the quality of experience of Internet of Things services

Use of Central Nervous System (CNS) Medicines in Aged Care Homes: A Systematic Review and Meta-Analysis

Background: Both old age and institutionalization in aged care homes come with a significant risk of developing several long-term mental and neurological disorders, but there has been no definitive meta-analysis of data from studies to determine the pooled estimate of central nervous system (CNS) medicines use in aged care homes. We conducted this systematic review to summarize the use of CNS drugs among aged care homes residents. Methods: MEDLINE, EMBASE, CINAHL, Scopus, and International Pharmaceutical Abstracts (IPA) databases were searched (between 1 January 2000 and 31 December 2018) to identify population-based studies that reported the use of CNS medicines in aged care homes. Pooled proportions (with 95% confidence interval), according to study location were calculated. Results: A total of 89 studies reported the use of CNS medicines use in aged care. The pooled estimate of CNS drugs use varied according to country (from 20.3% in Ireland to 49.0% in Belgium) and region (from 31.7% in North America to 42.5% in Scandinavia). The overall pooled estimate of psychotropic medicines use was highest in Europe (72.2%, 95% CI, 67.1–77.1%) and lowest in ANZ region (56.9%, 95% CI, 52.2–61.4%). The pooled estimate of benzodiazepines use varied widely from 18.9% in North America to 44.8% in Europe. The pooled estimate of antidepressants use from 47 studies was 38.3% (95% CI 35.1% to 41.6%) with highest proportion in North America (44.9%, 95% CI, 35.3–54.5%). Conclusion: The overall use of CNS drugs varied among countries, with studies from Australia-New Zealand reported the lowest use of CNS drugs. The criteria for prescribing CNS drugs in clinical practice should be evidence-based. The criteria should be used not to prohibit the use of the listed medications but to support the clinical judgement as well as patient safety

The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

Collaborative collection effort strategies based on “Internet + recycling” business model

"Internet + recycling", a new and emerging collecting mode, is booming in conjunction with widespread Internet use in China. For the recycling of waste electrical and electronic equipment (WEEE), this paper studies collaborative collection effort strategies in a collection system consisting of a third-party and an e-tailer based on the "Internet + recycling" business model. Considering the collaboration occurring during collecting and selling and mutual influences of partners on the recycling of old products, the paper applies collection effort cost sharing mechanisms to promote recycling. Four models, namely, the centralized model (C-Model), unit transfer price model (P-Model), unilateral cost sharing model (U-Model) and bilateral cost sharing model (B-Model), are established, and optimal decisions and members' profits in various collaborative models are derived and compared. The results show that there exists an interval of profit sharing proportions in which each of the two cost sharing models is a Pareto improvement of the P-Model, and the total collection volume and profit of the collecting system increase in the B-Model relative to those in the U-Model under the same proportion of profit sharing. However, the B-Model is not necessarily a Pareto improvement of the U-Model. The results also show that profit improvements of both parties can be achieved without the third-party sharing the e-tailer's collection effort cost in the B-Model when the collaborative marginal profit is large enough. The paper further explores the impact of the collaborative marginal profit and third-party's market influence on the total collection volume and the efficiency of the collecting system. This study provides insight into the promotion of WEEE recycling and into the selection of collaborative strategies for Internet recycling enterprises. The work will prove beneficial to the development of the WEEE "Internet + recycling" industry