We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that h...
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ISBN:
(纸本)9781467365963
We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that helps cope with the EVs' limited range. The goal of the system is to maximise the number of customers that are serviced. Thus, we first model and solve this problem optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop a greedy heuristic algorithm that is shown to generate near-optimal solutions and can scale to thousands of consumer requests and EVs. Both algorithms are evaluated in a setting using data of real locations of shared vehicle pick-up and drop-off stations and the greedyalgorithm is shown to be on average 90% of the optimal in terms of average task completion.
Emerging Internet architecture utilizes a pluralistic architectural paradigm in which multiple virtual overlay service networks are built on top of the existing interconnected Autonomous System (multi-AS) networks acr...
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ISBN:
(纸本)9798350371000;9798350370997
Emerging Internet architecture utilizes a pluralistic architectural paradigm in which multiple virtual overlay service networks are built on top of the existing interconnected Autonomous System (multi-AS) networks across the world to satisfy diverse and ever-demanding service requirements from end users as well as emerging network applications. However, optimal provisioning and management of virtual networks in the multi-AS environment is still a challenging and unsolved problem. The problem has been extensively studied within the research community as a so-called multi-domain virtual network embedding (MD-VNE) optimization problem, but the current research results so far have been unsatisfactory and lack practical use. This paper proposes a bottom-up virtual network synthesis (VNS) approach to the MD-VNE problem in multi-AS network environment and formulates it into a combinatorial optimization problem. We then propose two trajectory-based meta-heuristic approximation algorithms based on greedy Randomized Adaptive Search Procedure (GRASP) and Iterative Local Search (ILS) methods together with a simple greedyheuristic (GH)-based algorithm as approximate solutions to the multi-AS VNS optimization problem. Experiments with these three approximation algorithms were conducted, and their performances were analyzed quantitatively based on well-defined metrics. The analysis results show the feasibility of the developed approximation solutions for practical solutions to the MD-VNE problem.
Efficient scheduling of heat-treatment furnace (HTF), a batch processor (BP), is very important to meet both throughput benefits as well as the committed due date to the customer, as the heat-treatment operations requ...
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ISBN:
(纸本)9783030299965;9783030299958
Efficient scheduling of heat-treatment furnace (HTF), a batch processor (BP), is very important to meet both throughput benefits as well as the committed due date to the customer, as the heat-treatment operations require very long processing time in the entire steel casting manufacturing process and accounts for large part of the total casting processing time required. In the recent time, there are good number of studies reported in the literature related to scheduling of BP associated with many discrete parts manufacturing. However, still there is very scant treatment has been given in scheduling of HTF problem, which has one of the unique job-characteristic: non-identical job-dimensions. This characteristic differentiates most of the other BP problems reported in the literature. Thus, this study considers a scheduling HTF, close to real-life problem characteristics, and proposes efficient heuristic solution methodologies.
Cognitive radio (CR), which is a common form of wireless communication, consists of a transceiver that is intelligently capable of detecting which communication channels are available to use and which are not. After t...
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Cognitive radio (CR), which is a common form of wireless communication, consists of a transceiver that is intelligently capable of detecting which communication channels are available to use and which are not. After this detection process, the transceiver avoids the occupied channels while simultaneously moving into the empty ones. Hence, spectrum shortage and underutilization are key problems that the CR can be proposed to address. In order to obtain a good idea of the spectrum usage in the area where the CRs are located, cooperative spectrum sensing (CSS) can be used. Hence, the primary objective of this research work is to increase the realizable throughput via the cluster-based cooperative spectrum sensing (CBCSS) algorithm. The proposed scheme is anticipated to acquire advanced achievable throughput for 5G and beyond-5G Internet of Things (IoT) applications. Performance parameters, such as achievable throughput, the average number of clusters and energy, have been analyzed for the proposed CBCSS and compared with optimal algorithms.
Locating automatic vehicle identification (AVI) sensors to recognize Origin-Destination (OD) demand has been attracting extensive attentions in academia and industry. Although most scholars determined OD demand after ...
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Locating automatic vehicle identification (AVI) sensors to recognize Origin-Destination (OD) demand has been attracting extensive attentions in academia and industry. Although most scholars determined OD demand after deriving unique route flow based on AVI sensors, this traditional method does not necessarily obtain the smallest number of sensors to ensure the uniqueness of OD demand. Moreover, the sensors can fail in reality, which results in missing some information of observed links, thus the uniqueness of OD demand cannot be guaranteed. In this paper, we propose a new AVI sensor location model considering sensor failures to ensure the uniqueness of OD demand directly, without determining route flows. Typically, given the observation order of AVI sensors, this method can minimize the number of sensors to determine OD demand uniquely, in the meanwhile satisfying certain reliability given sensors' failure. Moreover, under budget constraints, we develop a sensor location model to estimate OD demand under sensor failures, which maximizes information value of the differentiated OD pairs. Then we design several greedy heuristic algorithms to solve these two sensor location problems. Through numerical experiments, we show that the proposed models and algorithms can effectively determine the AVI sensor locations to recognize the OD demand and its uniqueness in the event of uncertain sensor failures.
This article studies the location problem of maritime emergency supplies repertories under the joint of government and enterprises, and it proposes the two-stage optimization location model by integrating the location...
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This article studies the location problem of maritime emergency supplies repertories under the joint of government and enterprises, and it proposes the two-stage optimization location model by integrating the location of maritime emergency supplies repertories, the distribution of emergency supplies, and the cooperation game between the government and enterprises. The first-stage model solves the location-allocation problem of maritime emergency supplies repertories;the second-stage model solves the cooperation game. In view of the model, it designs a genetic, greedy heuristic algorithm. The numerical example results indicate that the joint of government and enterprises can reduce the total cost, the cooperation enterprises can benefit from the alliance, and the alliances are stable, so the model and the algorithm are feasible.
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