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Besides saving money and providing convenience, carpooling can result in environmental benefits to society; therefore, it is important to promote and improve daily carpooling services. However, some conflicts related to carpooling match rates and environmental protection have been influencing user satisfaction. To broaden the carpooling service scope and improve service flexibility, an environment and user benefit-oriented carpooling service model was developed that included mixed user groups, different user types, multiple origin and destination pairs, a time window and user attitudes, and considered the cost and time efficiency requirements of the users to guarantee individual benefits and provide more reasonable matches. A 0–1 nonlinear optimization model was built to describe the model and an improved genetic algorithm based on a nearby matching principle designed to find the optimal solution. Finally, a case simulation from Chengdu city was conducted to demonstrate the effectiveness of the proposed model and the influence of different user attitudes on the carbon emissions and match rates, from which it was found that the proposed carpooling mode was able to provide a more systematic carpooling plan, avoid the increased carbon emissions from potential mismatching due to time and efficiency losses, and guarante the maximum social benefit. [ABSTRACT FROM AUTHOR]

With the rapid development of the aviation industry over the last decade, carbon emissions in air passenger transport have become an increasing concern. By combining the equilibrium strategy with a cap and trade mechanism, this paper proposes a bi-level multi-objective model for carbon emission allowance allocations in air passenger transport, in which a government and airlines are considered as the leader and follower decision makers respectively. Compared with previous studies, this model has the ability to describe the interactions of multiple stakeholders and balance their conflicts. To solve the proposed model, a solution method which integrates an interactive evolutionary mechanism and a fuzzy logic controlled genetic algorithm is developed. Then a numerical example is provided to demonstrate the practical applicability and effectiveness of the method for carbon emission mitigation. Results show that the cap and trade mechanism plays a vital role in mitigating air passenger transport carbon emissions and thus it should be effectively applied to air passenger transport. Results of carbon emission allowance allocations also indicate that the proposed method can provide efficient ways of mitigating carbon emissions for air passenger transport, and therefore assisting decision makers in formulating relevant strategies under multiple scenarios. [ABSTRACT FROM AUTHOR]

The location of temporary facilities on construction sites is essential to the enhancement of productivity and safety, but it is complex due to the unique issues associated with construction. To positively contribute to the dynamic construction site layout planning field, this paper proposes a new fuzzy multi-objective decision making model. The proposed hybrid optimization model utilizes fuzzy numbers and logic to represent the closeness relationship between temporary facilities. The two main phases presented represent a specific advance in knowledge in through: (1) an opti- mization model that considers both uncertainty and dynamic elements; (2) the application of this optimization to a spe- cial project. A multi-objective simulated annealing-based genetic algorithm (MOSA-based GA) is proposed to solve the model and the case of Jinping-II hydroelectric station is studied to evaluate the model's performance. The computational study was carried out to demonstrate the practicality and efficiency of the proposed optimization method. The study is applicable and useful to the profession. [ABSTRACT FROM PUBLISHER]

Prognostics and health management (PHM) is a widely used effective discipline consisting of advanced technologies and methods to assess the reliability of a system under its actual application conditions. It can be used to improve the safety and reliability of aircraft engines (AEs). In a PHM system for AEs, a large number of sensors are installed to provide health condition information. A selection of a minimal subset of the most informative and cost-effective sensors is required to monitor the optimum performance. In this paper, a PHM-oriented sensor optimization selection model is developed, which considers both sensor cost and monitoring performance as objective functions, and takes the PHM requirements as the constraints. A numerical example is given to demonstrate the sensor selection approach for an aircraft gas turbine engine. The results demonstrated that the proposed model and algorithm were effective and feasible, and were able to effectively guide the sensor selection for AEs. [ABSTRACT FROM PUBLISHER]

A new multiple decision-maker model using bi-level programming is proposed for a resource-constrained project scheduling problem in a fuzzy random environment. In the model, activity duration is assumed to be a fuzzy random variable because of the complex uncertainties in project scheduling problems. The project owner, who is the upper-level decision maker, seeks to maximize profits whereas the lower-level contractor attempts to minimize cost. A global-local-neighbor particle swarm optimization with a fuzzy random simulation is then proposed to solve the advanced model. Finally, a sub-project of Nuozhadu Hydropower Station Construction Project in China is used to illustrate an application of the developed model. A comparison with other approaches is made and the generated results validate the viability and effectiveness of the proposed model and method. [ABSTRACT FROM AUTHOR]

This paper considers a bi-level multi-objective quarry location and stone materials transportation problem for a large-scale construction project with random phenomenon. Based on the characteristics and mechanisms of the problem, the objective functions and constraints are established. To deal with the problem uncertainty, an expected value operator is employed to deal with the random coefficients in the objective functions, and a rough approximation method is adopted to deal with the feasible region, which features constraints with random coefficients. Then a rough approximation-based bi-level multi-objective model is developed as an equivalent crisp model. To solve the complex and nonlinear bi-level multi-objective model, a hybrid genetic algorithm embedded with an interactive fuzzy programming technique is designed as a combined solution method. Finally, the results and a comparison analysis of a case study at the Xiluodu dam construction project are presented to demonstrate the practicality and efficiency of the optimization method. [ABSTRACT FROM AUTHOR]

Abstract: The remaining useful life (RUL) of a system is defined as the length of time from when an anomaly or failure symptom occurs to the time when the component or module cannot be used. Prognostics plays a crucial role in integrated system health management (ISHM) for space avionics. In this paper, a new RUL definition is given, and RUL prognostics based on ISHM for space avionics, a complex electronics system composed of many components and modules, is focused on. An intelligent fusion prognostics approach is proposed, the GASVM, which is based on support vector machine theory and the genetic algorithm. The fusion prognostics approach incorporates the advantages of these two methods. Finally, a numerical example is given to show the efficiency of the proposed method. Compared to standard Support Vector Machines (SVM) and Artificial Neural Networks (ANN), the proposed approach has a better predictive performance. [Copyright &y& Elsevier]

The allocation of water resources has long been recognized as a critical optimization problem. In this study, a bilevel programming model with fuzzy random variables is developed for tackling a regional water resources allocation problem on the basis of water rights distribution in a river basin. The bilevel programming model takes the optimal total benefit of the society and the optimal economic benefit of each subarea as the upper and lower targets, respectively. In contrast to previous studies, the balance of the satisfactory degree between the upper and lower decision makers is considered in the bilevel optimization for ensuring the equity of the water resource allocation. To deal with inherent uncertainties, the fuzzy random variables are first transformed into trapezoidal fuzzy numbers, and by taking advantage of the expected value operation, the trapezoidal fuzzy numbers are subsequently defuzzified. For solving the complex and nonlinear bilevel programming model, an interactive fuzzy programming technique and an entropy-Boltzmann selection-based genetic algorithm are designed as a combined solution method. Finally, the results and comparisons analysis of a case study are presented to demonstrate the practicality and efficiency of the optimization method. [ABSTRACT FROM AUTHOR]

This paper presents a resource sharing-based construction equipment allocation problem with multiple stages under a fuzzy environment. A multiobjective multistage decision-making model is established in which the equipment failure rate is regarded as a fuzzy variable. In contrast to traditional assignment problems, the principle of resource sharing modes including space and time sharing is considered to be based on the project practice. In addition, a dynamic programming-based genetic algorithm is developed to find feasible solutions and a dynamic programming-based initialization, and crossover and mutation are designed to avoid infeasible solutions. Finally, the Jinping-I Hydropower Project is used as a practical example to demonstrate the practicality and efficiency of the model. The comparisons and analysis of results are presented to highlight the performance of the optimization method, which is proved to be more effective and efficient than the standard genetic algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method. [Copyright &y& Elsevier]

The aim of this paper is to deal with resource-constrained multiple project scheduling problems (rc-mPSP) under a fuzzy random environment by a hybrid genetic algorithm with fuzzy logic controller (flc-hGA), to a large-scale water conservancy and hydropower construction project in the southwest region of China, whose main project is a dam embankment. The objective functions in this paper are to minimize the total project time (that is the sum of the completion time for all projects) and to minimize the total tardiness penalty of multiple projects, which is the sum of penalty costs for all the projects. After describing the problem of the working procedure in the project and presenting the mathematical formulation model of a resource-constrained project scheduling problem under a fuzzy random environment, we give some definitions and discuss some properties of fuzzy random variables. Then, a method of solving solution sets of fuzzy random multiple objective programming problems is proposed. Because traditional optimization techniques could not cope with the rc-mPSP under a fuzzy random environment effectively, we present a new approach based on the hybrid genetic algorithm (hGA). In order to improve its efficiency, the proposed method hybridized with the fuzzy logic controller (flc) concept for auto-tuning the GA parameters is presented. For the practical problems in this paper, flc-hGA is proved the most effective and most appropriate compared with other approaches. The computer generated results validate the effectiveness of the proposed model and algorithm in solving large-scale practical problems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we have developed a model that integrates system dynamics with fuzzy multiple objective programming (SD-FMOP). This model can be used to study the complex interactions in a industry system. In the process of confirming sensitive parameters and fuzzy variables of the SD model, we made use of fuzzy multi-objective programming to help yield the solution. We adopted the chance-constraint programming model to convert the fuzzy variables into precise values. We use genetic algorithm to solve FMOP model, and obtain the Pareto solution through the programming models. It is evident that FMOP is effective in optimizing the given system to obtain the decision objectives of the SD model. The results recorded from the SD model are in our option, reasonable and credible. These results may help governments to establish more effective policy related to the coal industry development. [Copyright &y& Elsevier]

In this paper, we integrated the forward logistics and the reuse reverse logistics to set up a closed-loop integrated logistics system under a random fuzzy environment. A random fuzzy multi-objective programming model was established which can describe the integrated logistics as a cycle of production, distribution, consumption, collection, transportation, recycling, disposal, reuse and redistribution. We then used the expected value operator and the chance-constrained operator to handle the random fuzzy objective functions and the random fuzzy constraints. The solution scheme was pursued by a genetic algorithm based on random fuzzy simulation and compromise approach. And an application to a Chinese beer company was given as an illustration. [ABSTRACT FROM AUTHOR]

Abstract: The emphasis of this paper is to introduce two kinds of random rough variables. One is the discrete random rough variable, the other is the continuous random rough variable. The expected value model with random rough coefficients is proposed and some special models are discussed. In order to compute the expected value of some complicated functions, the technique of the random rough simulation is presented. Combined with genetic algorithm, it is applied to check the objective function and constraint function, and to obtain the optimal solution. Finally, some numerical examples are provided to show the effectiveness of the proposed technique. [Copyright &y& Elsevier]

Abstract: In the present paper, we concentrate on dealing with a class of multiobjective programming problems with random rough coefficients. We first discuss how to turn a constrained model with random rough variables into crisp equivalent models. Then an interactive algorithm which is similar to the interactive fuzzy satisfying method is introduced to obtain the decision maker’s satisfying solution. In addition, the technique of random rough simulation is applied to deal with general random rough objective functions and random rough constraints which are usually hard to convert into their crisp equivalents. Furthermore, combined with the techniques of random rough simulation, a genetic algorithm using the compromise approach is designed for solving a random rough multiobjective programming problem. Finally, illustrative examples are given in order to show the application of the proposed models and algorithms. [Copyright &y& Elsevier]

To optimize regional economies, social employment, and water quality protection, a multiobjective bilevel optimization model under a fuzzy random environment based on constructed wetland planning is developed in this study. Fuzzy random variables are used to describe the uncertainties in the system, and three special fuzzy random simulation methods are proposed to address the fuzzy random variable calculations. From the inherent bilevel model interaction, a new algorithm called the fuzzy random simulation-based nested genetic algorithm (FRS-based NGA) is designed as an intelligent solution to solve the model. Then, the model is applied to a real-world case: the Chaohu Lake watershed, China. The results under different objective values, probability levels, and possibility levels are compared so that an optimal scheme for regional economic development, social employment, and the wetland construction scale can be identified. Finally, the comparative analysis, sensitivity analysis, and convergence analysis are provided to illustrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]