The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel optimum methodology for designin...
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The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel optimum methodology for designing a combined solar/battery/diesel system in Yarkant, Xinjiang Uyghur Autonomous Region of China. In the desired system, the green energy combined system is designed to reduce the use of diesel generators. The diesel generator has been used in the photovoltaic, diesel, and battery to support green energy resources and batteries, as well as function as a backup generator for critical times whenever the production of green energy resources is low or the load demand is high. The amount of CO2 emitted, the probability of load shortage and the system cost on yearly basis are the major goals in the process of optimization. Here, the single-objective problem is created by using the ε-constraint technique to combine the many objectives. An improved Henry gas solubility optimizer handles the problem of optimization. To demonstrate the superiority of the strategy, a comparison is conducted between the simulation outcomes of the offered system, HOMER, and particle swarm optimizer -based optimum systems from the literature. The sensitivity of each parameter is also examined using sensitivity analysis.
In this paper, we study the bi-objective prize-collecting Steiner tree problem, whose goal is to find a subtree that minimizes the edge costs for building that tree, and, at the same time, to maximize the collected no...
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The decomposition strategy and the ε-constraint method are two important strategies in the field of multi-objective optimization. DMOEA-εC first attempts to incorporate the ε-constraint method into the decompositio...
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The decomposition strategy and the ε-constraint method are two important strategies in the field of multi-objective optimization. DMOEA-εC first attempts to incorporate the ε-constraint method into the decomposition strategy and solve a multi-objective optimization problem(MOP) via optimizing a series of scalar constrained subproblems collaboratively with the help of information from neighboring subproblems. However, given the inefficiency of applying DMOEA-εC to deal with many-objective optimization problems(MaOPs), a two-stage upper bound vectors generation procedure is proposed to generate widely spread upper bound vectors in a high-dimensional space. Besides, a boundary points maintenance mechanism is put forward to remedy the diversity loss of a population in DMOEA-εC. Based on the above, DMOEA-εC with the two-stage upper bound vectors generation procedure and the boundary points maintenance mechanism, named as IDMOEA-εC, is presented for MaOPs. IDMOEA-εC is compared with four state-of-the-art many-objective evolutionary algorithms, including HypE, NSGA-Ⅲ, MOEADD, and Two Arch2. Experimental studies demonstrate that IDMOEA-εC outperforms or performs competitively against these algorithms on the majority of sixteen DTLZ test instances with up to 10 objectives.
Purpose: investigating the effect of lead time variability on the total cost of a supply chain network to gain more knowledge about the impact lead time variability has. Design/methodology/approach: A mixed integer li...
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Purpose: investigating the effect of lead time variability on the total cost of a supply chain network to gain more knowledge about the impact lead time variability has. Design/methodology/approach: A mixed integer linear program model has been developed to calculate Pareto efficient outcomes for different groups of lead time variability among different datasets. The Pareto frontiers between the objectives of minimizing cost and minimizing lead time are constructed using the ε-constraint method. These Pareto solution sets are compared and analysed. Furthermore an significance test has been executed to review the difference of average cost between lead time variability groups. Findings: a high change of Pareto improving solutions under the Pareto efficient solution sets, but no significant difference on average total cost between the benchmark and lead time variability groups. Research limitations/implications: Two research limitations were present in this thesis: The construction of the lead time could be enhanced and the approach on lead time and other parameters could be stochastic instead of deterministic. Practical implications: Two important insight regarding lead time variability are obtained. First lead time variability has a noteworthy impact when considering the Pareto efficient outcomes. Different groups can have vastly different outcomes. Second, there is no significant difference in the average total cost between the use of lead time variability and no lead time variability. Social implications: none. Originality/value: There is a large absence of the use of lead time variability in current supply chain network research. Most of the articles ignore the existence of lead time variability and neglect to mention the reason for not including variability into their research. It is therefore important to gain knowledge on the implication lead time variability has.
This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and probl...
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This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning *** model includes two terms:a problem-based term that is derived from the prior knowledge,and an image-driven regularization which is learned by some training *** model can be solved by classical ε-constraint *** results show that:the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof;the proposed method outperforms other PDE-based methods on image denoising and deblurring.
This study presents a mixed integer linear multi-objective model based on information gap decision theory, which is used to solve coordinated multiyear generation and transmission expansion planning problems. The mode...
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This study presents a mixed integer linear multi-objective model based on information gap decision theory, which is used to solve coordinated multiyear generation and transmission expansion planning problems. The model maximises the robustness of each uncertain parameter while a maximum allowable budget range is set. Fuel transportation price is considered. The results provide a numerical tool for system planner to help him adjust the appropriate level of robustness for each uncertain parameter of the problem. Extra limits on security, gaseous emission and fuel availability are considered. A multi-objective method called the.-constraintmethod is used here to maximise the robust region of load and investment costs simultaneously. The model is implemented on a six-bus Garver test system and 24-bus IEEE test system. The numerical results show the good performance of the model.
The industrial sector is the largest consumer of the world's total energy and most of its consumption form is electricity. To strengthen the grid's peak load regulation ability, time-of-use (TOU) electricity p...
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The industrial sector is the largest consumer of the world's total energy and most of its consumption form is electricity. To strengthen the grid's peak load regulation ability, time-of-use (TOU) electricity pricing policy has been implemented in many countries to encourage electricity users to shift their consumption from on-peak periods to off-peak periods. This strategy provides a good opportunity for manufacturers to reduce their energy bills, especially for energy-intensive ones, where batch scheduling is often involved. In this thesis, several bi-objective batch scheduling prob- lems under TOU tariffs are studied. We first investigate a single machine batch scheduling problem under TOU tariffs with the objectives of mini- mizing total electricity cost and makespan. This primary work is extended by further considering machine on/off switching. Finally, a parallel batch machines scheduling problem under TOU tariffs with non-identical job sizes to minimize total electricity cost and number of enabled machines is studied. For each of the considered problems, appropriate mathematical models are established, their complexities are demonstrated. Different bi- objective resolution methods are developed, including knapsack heuristic based ε-constraint method, multiple knapsack heuristic based ε-constraint method, bin packing heuristic based ε-constraint method and two-stage heuristic based iterative search algorithm. The performance of the pro- posed methods is evaluated by randomly generated instances. Extensive numerical results show that the proposed algorithms are more efficient and/or effective for the studied problems than the commercial software CPLEX.
Shelters have a very critical role in disaster relief since they provide accommo- dation and necessary services for the disaster victims who lost their homes. The selection of their locations among many candidate poin...
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Shelters have a very critical role in disaster relief since they provide accommo- dation and necessary services for the disaster victims who lost their homes. The selection of their locations among many candidate points is a task that should be carried out with a proper methodology that generates applicable and fairness- based plans. Since this selection process is done before the occurrence of disas- ters, it is important to take demand variability into account. Motivated by this, the problem of determining shelter site locations under demand uncertainty is addressed. In particular, a chance-constrained mathematical model that takes demand as a stochastic input is developed. By using a linearization approach that utilizes special ordered set of type 2 (SOS2) variables, a mixed-integer linear programming model is formulated. Using the proposed formulation, instances of the problem using data associated with Istanbul are solved. The results in- dicate that capturing uncertainty in the shelter site location problem by means of chance constraints may lead to solutions that are much different from those obtained from a deterministic setting. During these computational analysis, it is observed that the single-objective model is prone to generate many alternative so- lutions with different characteristics of important quality measures. Motivated by this, a multi-objective framework is developed for this problem in order to have a stronger modeling approach that generates only non-dominated solutions for the selected performance measures. The ε-constraint method is used for scalar- ization of the model. Bi-objective and 3-objective algorithms are presented for detecting all the efficient solutions of a given setting. Unlike the single-objective configuration, the decision makers could be supplied with much richer informa- tion by reporting many non-dominated solutions and allowing them to evaluate the trade-offs based on their preferences.
During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the de...
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In recent years, along with the more and more serious pension issues, nursing home construction becomes an important project. Meanwhile, it is crucial for decision makers to determine the location of a nursing home be...
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ISBN:
(纸本)9781509028436
In recent years, along with the more and more serious pension issues, nursing home construction becomes an important project. Meanwhile, it is crucial for decision makers to determine the location of a nursing home before its construction. An effective location planning could achieve reasonable resources allocation and social fairness. This paper considers a nursing home location and allocation problem with two objectives. The first objective is to minimize the total construction costs, which is considered from the perspective of the government. For the elderly people, their expectations are their allocated nursing homes close enough to their children's communities. Therefore, the second objective is to minimize the total distances. A bi-objective mixed-integer programming(MIP) model is formulated and an exact ε-constraint method is adopted to obtain a set of optimal Pareto solutions. A case study is conducted with comparisons. The obtained results demonstrate the proposed model and adopted method are suitable for the real-world nursing home location and allocation problem.
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