Multi-objective Optimization Approaches for Design, Planning, and Management of Water Resource Systems

Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

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Efficient multi-objective optimization methods for computationally intensive urban water resources models

Journal of Hydroinformatics ◽

10.2166/hydro.2014.204 ◽

2014 ◽

Vol 17 (1) ◽

pp. 36-55 ◽

Cited By ~ 6

Author(s):

Mohammad Mortazavi-Naeini ◽

George Kuczera ◽

Lijie Cui

Keyword(s):

Ant Colony Optimization ◽

Water Resource ◽

Optimization Methods ◽

Optimization Method ◽

Ant Colony ◽

Urban Water ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

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Planning and management of water resource systems for livable cities: current issues and options

Australasian Journal of Water Resources ◽

10.1080/13241583.2017.1449768 ◽

2017 ◽

Vol 21 (2) ◽

pp. 51-52

Author(s):

Trevor M. Daniell ◽

Katherine A. Daniell

Keyword(s):

Water Resource ◽

Planning And Management ◽

Resource Systems

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