Development of a Multi-Objective Optimization Model Using NSGA-II for Flow Shop Production Machine Scheduling to Support Manufacturing Industry

In this paper, we propose a multi-objective non-linear reliability global optimization and system cost as two objective functions. As a generalized version of fuzzy set, intuitionistic fuzzy set, Pythagorean fuzzy set, (3,2)-fuzzy set is a very useful tool to express uncertainty, impreciseness in more general way. we have considered (3,2)-fuzzy optimization technique with linear and non-linear membership function to solve this multi-objective reliability optimization model. To demonstrate the methodology and applicability of the proposed approach, numerical examples are presented and evaluated by comparing the result obtained by (3,2)-fuzzy approach with the intuitionistic fuzzy optimization technique at the end of the paper. Introduction: Reliability engineering is one of the important tasks in designing and development of a technical system. The primary goal of the reliability engineer has been always to find the best way to increase system reliability. The diversity of system resources, resource constraints and options for reliability improvement lead to the construction and analysis of several optimization models. In daily life, due to some uncertainty in judgements of the decision maker (DM), there are some coefficients and parameters in the optimization model, which are always imprecise with vague in nature. In order to handle such type of nature in multi objective optimization model, fuzzy approach is used to evaluate this. Conclusions: The main purpose of this paper is to give a computational procedure for solving multi-objective reliability optimization model by (3,2)-fuzzy optimization technique to find the optimal solution, which maximize the system reliability and minimize the cost of the system. We have obtained the result in the (3,2)-fuzzy optimization technique was compared with the intuitionistic fuzzy optimization and Pythagorean fuzzy optimization and it shows that the (3,2)-fuzzy optimizations with non-linear membership function gives better reliable system. Thus, the proposed method is an efficient and modified optimization techniques and gives a highly reliable system than the other existing method.

Multi-objective Fuzzy Optimization Design of Helical Gear Drive

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies. DOI: 10.1061/(ASCE)WR .1943-5452.0000269. © 2013 American Society of Civil Engineers. CE Database subject headings: Rivers and streams; Streamflow; Groundwater management; Optimization; Algorithms; Economic factors; Environmental issues. Author keywords: Stream depletion; Tradeoffs; Spatial planning; Multiobjective optimization; Genetic algorithm.

The design optimization of a direct-drive permanent magnet synchronous generator (DDPMSG) is of great significance for wind turbines because of its unique advantages. This paper proposes a two-stage model to realize multi-objective design optimization for a 6 MW DDPMSG. In the first stage, a surrogate optimized response surface model based on an improved sparrow search algorithm (ISSA) was established for modeling the cogging torque and generator efficiency. In the second-stage model, a multi-objective optimization model is proposed to optimize the cogging torque and generator efficiency of the DDPMSG. Finally, the proposed two-stage model was used for a 6 MW DDPMSG design optimization, and the simulation results demonstrated the superiority and rationality of the proposed model. In the first-stage model, the proposed surrogate model based on the ISSA had a better modeling accuracy and lower errors. Compared with traditional response surface models and correlation analysis models, the proposed optimized surrogate model reduced errors in the cogging torque by 34.63% and 42.97%, respectively, while the errors in the efficiency models were reduced by 12.92% and 60.78%, respectively, which indicates the superiority of the first-stage model. In the second stage, compared with the single-objective optimization model, the multi-objective optimization model achieved a trade-off optimization between the cogging torque and the efficiency. Compared with the cogging torque optimization model, the proposed model optimized the efficiency by 101.41%. Compared with the efficiency optimization model, the proposed model reduced the cogging torque by 16.67%. These results verified the superiority and rationality of the second-stage model.

Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis

Autonomous drone charging station planning through solar energy harnessing for zero-emission operations

Evidence from ecological studies has suggested that alteration of river flows downstream of reservoirs can threaten native aquatic ecosystems. The Three Gorges Reservoir has been controversial since its design and construction stage, and the ecological damage downstream is an important concern. However, protecting long-term health of the river ecosystem has a low priority in reservoir operation compared to other human demands, and is traditionally treated as a constraint of minimum water release. A multi-objective reservoir optimization model incorporating ecological adaption is proposed. Range of variability approach is first used to quantify the hydrological alteration. A satisfying ecological flow scenario is then worked out if it is necessary to take ecological issues into consideration. With the aim of eco-compensation, the reservoir release should be as close to satisfying ecological flow as possible, which is set to be the objective for ecological adaption. Together with other objectives, such as flood control and power generation, a multi-objective optimization model is established, which is optimized by NSGA-II algorithm. Results not only provide the operational references in both wet and dry years, but also illustrate the negative impacts on the river ecosystem by reservoirs can be alleviated with low economic cost. Quantitative relationships among different objectives can also be used for trading markets.

The structure and trigger control strategy have become the most important factors that restrict the performance of the multistage synchronous induction coilgun (MSSICG). However, it is still a difficult task to design MSSICG under overload constraint due to coupling between the multiple parameters. In this paper, the maximization of the emission efficiency and acceleration stationarity is treated as a multiobjective optimization problem. By analyzing the relationship between the number of turns and the other structural parameters of the launch, the multiobjective optimization model of MSSICG is established by the current filament method which was verified by the experimental data and finite-element method. And then the second generation nondominated sorting genetic algorithm (NSGA-II) and multiobjective particle swarm optimization (MOPSO) were employed to optimize the model in order to maximize the energy transfer efficiency while achieving the smooth acceleration of the armature. With the formulated optimization model, a five-stage synchronous induction coilgun is optimized as a special case. A decision-making procedure based on the fuzzy membership function is used for obtaining best compromise solution from the set of Pareto-solutions obtained through NSGA-II and MOPSO. In addition, the optimization performance of the proposed multiobjective optimization model and the single-objective optimization model of the MSSICG was compared. The result of optimization shows that the proposed multiobjective optimization model of MSSICG can effectively improve the performance of the coilgun compared with the single-objective optimization model which takes of the launch velocity and overload acceleration as the combination objective function or only the launch velocity.

Structural analysis, a unified classical and matrix approach: By A. Ghali and A. M. Neville. Pp.779. Chapman & Hall. 1978. £8.50

A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete

This paper is concerned on the design optimization of axial flow hydraulic turbine runner blade geometry, and a new comprehensive performance Optimization procedure is presented by combining a multivariable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. The total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using weight factors. Optimization variables are taken from parameters describing the blade bound circulation distribution and positions of the blade leading and trailing edges in the meridional flow passage. The optimization procedure has been applied to the design optimization of a Kaplan runner. Numerical results show that the performance of the designed runner is successfully improved through optimization computations. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function.

The design of the timetable is essential to improve the service quality of the public transport system. A lot of random factors in the actual operation environment will affect the implementation of the synchronous timetable, and adjusting timetables to improve synchronization will break the order of normal service and increase the cost of operation. A multi-objective bus timetable optimization problem is characterized by considering the randomness of vehicle travel time and passenger transfer demand. A multi-objective optimization model is proposed, aiming at minimizing the total waiting time of passengers in the whole bus network and the inconsistency between the timetable after synchronous optimization and the original timetable. Through large sample analysis, it is found that the random variables in the model obey normal distribution, so the stochastic programming problem is transformed into the traditional deterministic programming problem by the chance-constrained programming method. A model solving method based on the augmented epsilon-constraint algorithm is designed. Examples show that when the random variables are considered, the proposed algorithm can obtain multiple high-quality Pareto optimal solutions in a short time, which can provide more practical benefits for decisionmakers. Ignoring the random influence will reduce the effectiveness of the schedule optimization scheme. Finally, sensitivity analysis of random variables and constraint confidence in the model is made.

Biofuel production from microalgae biomass has been considered a viable alternative to harmful fossil fuels; however, challenges are faced regarding its economic sustainability. Process integration to yield various high-value bioproducts is implemented to raise profitability and sustainability. By incorporating a circular economy outlook, recirculation of resource flows is maximized to yield economic and environmental benefits through waste minimization. However, previous modeling studies have not looked into the opportunity of integrating productivity reduction related to the continuous recirculation and reuse of resources until it reaches its end of life. In this work, a novel multi-objective optimization model is developed centered on an algal biorefinery that simultaneously optimizes cost and environmental impact, adopts the principle of resource recovery and recirculation, and incorporates the life cycle assessment methodology to properly account for the environmental impacts of the system. An algal biorefinery involving end-products such as biodiesel, glycerol, biochar, and fertilizer was used for a case study to validate the optimization model. The generated optimal results are assessed and further analyzed through scenario analysis. It was seen that demand fluctuations and process unit efficiencies have significant effect on the optimal results.

The Partner selection is an important decision problem in the formation of a dynamic cloud collaboration platform. Selecting suitable cloud partners to form a group will facilitate the success of collaborative cloud services. In this chapter, first, we present a promising multi-objective (MO) optimization model of partner selection considering individual information (INI) and past relationship information (PRI) with collaboration cost optimization among cloud providers in a DCC platform. Then to solve this MO optimization model, a general framework of multi-objective genetic algorithm (MOGA) that uses INI and PRI of cloud providers called MOGA-IC is presented. Finally, two algorithms called NSGA-II and SPEA2 are developed to implement MOGA-IC.KeywordsPareto Optimal SolutionCloud ProviderPartner SelectionBinary Tournament SelectionCollaboration PlatformThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The process route rules the whole machining process from blanks to parts. It can directly affect the low carbon production of the mechanical product. To achieve the low-carbon optimization decisions of machining process route, the concept of feature element and machining element is introduced to express the component characteristics, and a multi-objective optimization model is established, which takes the minimum total processing time and the lowest total carbon emissions as the optimization objectives. Then the optimization model is solved based on NSGA-II(Non-dominated sorting genetic algorithm II). An experiment case of a motor seat machining process is performed to verify the feasibility and practicability of the proposed model.