A Multiplex Theory of Urban Service Distribution: The Case of School Expenditures

2010 ◽  
Vol 45 (5) ◽  
pp. 608-643 ◽  
Author(s):  
Aaron M. Pallas ◽  
Jennifer L. Jennings
Keyword(s):  
School Expenditures ◽  
Urban Service ◽  
Service Distribution

scholarly journals An Efficient Hybrid Algorithm for Energy Expenditure Estimation for Electric Vehicles in Urban Service Enterprises

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 2004
Author(s):  
Mariusz Izdebski ◽  
Marianna Jacyna
Keyword(s):  
Energy Expenditure ◽  
Electric Vehicles ◽  
Hybrid Algorithm ◽  
Ant Colony Algorithm ◽  
Environmental Safety ◽  
Utility Company ◽  
Service Companies ◽  
Charging Time ◽  
Urban Service ◽  
High Emission

The article deals with the decision problems of estimating the energy expenditure of low-emission fleets in urban service companies due to environmental safety. One of the most important problems of today’s transport policy of many city authorities is the ecological safety of its inhabitants. The basic measures are aimed at banning high-emission vehicles from city centers and promoting the introduction of zero-emission vehicles, such as electric or hybrid cars. The authors proposed an original approach to the decision model, in which the energy expenditure from the use of electric vehicles was defined as a criterion function. The boundary conditions took into account limitations typical of an electric vehicle, e.g., maximum range or battery charging time. To solve the problem, the authors proposed an efficient hybrid algorithm based on ant colony algorithm and genetic algorithm. The verification was made for the example of a utility company serving a medium-sized city in the eastern part of Poland.

scholarly journals Method for Dynamic Service Orchestration in Fog Computing

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1796
Author(s):  
Nerijus Morkevicius ◽  
Algimantas Venčkauskas ◽  
Nerijus Šatkauskas ◽  
Jevgenijus Toldinas
Keyword(s):  
Fog Computing ◽  
Optimization Method ◽  
Service Orchestration ◽  
Multi Objective ◽  
System A ◽  
Service Distribution ◽  
Judgment Matrix ◽  
Hierarchy Process ◽  
Application Specific ◽  
Constrained Devices

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

A QUEUE WITH POSITIVE AND NEGATIVE ARRIVALS GOVERNED BY A MARKOV CHAIN

2003 ◽  
Vol 17 (4) ◽  
pp. 487-501 ◽  
Author(s):  
Yang Woo Shin ◽  
Bong Dae Choi
Keyword(s):  
Markov Chain ◽  
Transient State ◽  
Single Server ◽  
Negative Customers ◽  
Service Distribution ◽  
Finite State ◽  
Sojourn Time Distribution ◽  
Absorbing States ◽  
Negative Arrivals ◽  
Selection Of

We consider a single-server queue with exponential service time and two types of arrivals: positive and negative. Positive customers are regular ones who form a queue and a negative arrival has the effect of removing a positive customer in the system. In many applications, it might be more appropriate to assume the dependence between positive arrival and negative arrival. In order to reflect the dependence, we assume that the positive arrivals and negative arrivals are governed by a finite-state Markov chain with two absorbing states, say 0 and 0′. The epoch of absorption to the states 0 and 0′ corresponds to an arrival of positive and negative customers, respectively. The Markov chain is then instantly restarted in a transient state, where the selection of the new state is allowed to depend on the state from which absorption occurred.The Laplace–Stieltjes transforms (LSTs) of the sojourn time distribution of a customer, jointly with the probability that the customer completes his service without being removed, are derived under the combinations of service disciplines FCFS and LCFS and the removal strategies RCE and RCH. The service distribution of phase type is also considered.

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