A Multi-Objective Optimization Model for Solid Waste Disposal Under Uncertainty: A Case Study of Bangkok, Thailand

2016 ◽  
Vol 15 (2) ◽  
pp. 205-212 ◽  
Author(s):  
Laemthong Laokhongthavorn ◽  
Chalida U-tapao
2012 ◽  
Vol 66 (2) ◽  
pp. 267-274 ◽  
Author(s):  
X. Dong ◽  
S. Zeng ◽  
J. Chen

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.


2021 ◽  
Vol 27 (1) ◽  
pp. 45-59
Author(s):  
Hong Zhang ◽  
Lu Yu

Delivery of the prefabricated components may be disrupted by low productivity and various of traffic restrictions, thus delaying the prefabricated construction project. However, planning of the prefabricated component supply chain (PCSC) under disruptions has seldom been studied. This paper studies the construction schedule-dependent resilience for the PCSC plan by considering transportation costs and proposes a multi-objective optimization model. First, the PCSC planning problem regarding schedule-dependent resilience and resultant transportation cost is analyzed. Second, a quantification scheme of the schedule-dependent resilience of the PCSC plan is proposed. Third, formulation of the resilience-cost tradeoff optimization model for the PCSC planning is developed. Fourth, the multi-objective particle swarm optimization (MOPSO)-based method for solving the resilience-cost tradeoff model is presented. Finally, a case study is presented to demonstrate and justify the developed method. This study contributes to the knowledge and methodologies for PCSC management by addressing resilience at the planning stage.


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