A Novel Formulation for the Sustainable Periodic Waste Collection Arc-Routing Problem: A Hybrid Multi-objective Optimization Algorithm

2020 ◽  
pp. 77-98
Author(s):  
Erfan Babaee Tirkolaee ◽  
Alireza Goli ◽  
Gerhard-Wilhelm Weber ◽  
Katarzyna Szwedzka
Keyword(s):  
Optimization Algorithm ◽  
Arc Routing ◽  
Multi Objective Optimization ◽  
Waste Collection ◽  
Routing Problem ◽  
Multi Objective

A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization

2019 ◽  
Vol 37 (11) ◽  
pp. 1089-1101 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Alireza Goli ◽  
Maryam Pahlevan ◽  
Ramina Malekalipour Kordestanizadeh
Keyword(s):  
Arc Routing ◽  
Waste Collection ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Urban Waste ◽  
Routing Problem ◽  
Multi Objective ◽  
Capacitated Arc Routing Problem ◽  
Constraint Method ◽  
Capacitated Arc Routing

Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles’ usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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