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In existing consensus models with optimization approaches, the modified preferences are often virtual values unrelated to the original rating scale. Furthermore, few optimization approaches have been developed to specifically deal with the consensus reaching process in uncertain multiple-criteria group decision making (MCGDM) problems. To investigate these issues, this paper is primarily interested in MCGDM optimization consensus models, in which the uncertain information can be represented using interval numbers. Integer optimization consensus models are established based on both the Manhattan distance and the Euclidean distance, and an antithetic-method-based particle swarm optimization algorithm is used to solve the nonlinear integer programming problems. Compared to existing approaches, the main characteristic of the proposed consensus models is that the generated preferences all belong to the original scales. A numerical example is given to validate the proposed models, and further simulation analyses shed light on the behavior of the developed models. [ABSTRACT FROM AUTHOR]

Abstract Due to rapid urbanization and modernization, the construction sector now generates one third of all greenhouse gas emissions in China. Using an equilibrium strategy combined with the carbon allowance allocation, this study presents a synergistic Stackelberg model based on a construction project planning framework to deal with cumulative CO 2 emissions. The bi-level model simultaneously considers the sequential decision-making relationship between the authority (leader) and the enterprises' interactive objectives and constraints. Unlike previous research, this bi-level model gives a holistic analysis of the interactivity of multiple stakeholders, thereby enabling the inherent conflicts and equilibrium between environmental protection and decision makers' profits to be reconciled and balanced. To deal with the bi-level model complexity, an interactive solution method that integrates an evolutionary mechanism and improved particle swarm optimization (IPSO) is designed for solving a construction supply planning problem. The robustness and practicality of the proposed methodology are then validated in a real world case, and sensitivity analyses under different carbon emissions quotas are also given. The results indicate that the methodology can systematically reduce carbon emissions in the Chinese construction sector, and when the authority has a strict emissions reduction altitude, construction practitioners are able to attain high carbon efficiencies; therefore, the model provides valuable strategy guidance for policymakers and business executives. Highlights • A leader-follower interactive method is designed to research the carbon-economy tradeoff. • The analysis and optimization in CSP is handled with a bi-level modeling technique. • A comparative carbon allowance discussion is demonstrated with different control scenarios. • The proposed novel model is effective and can be used in other pollutants reduction problems. [ABSTRACT FROM AUTHOR]

Coal mining activities have caused serious environmental problems; however, almost 30% of global primary energy demand is met using coal, making it difficult to replace in the near future. Under these circumstances, as it is not possible to completely cease coal mining activities, coal mines need to invest in cleaner production techniques to reduce environmental damage. To achieve pollution emissions and energy reductions, this paper proposes a dynamic multi-objective mixed 0–1 model based on ecological coal mining, in which environmental technological investment and production adjustments are integrated into a dynamic system to seek ecological and economic equilibrium, and emissions reductions and energy conservation are controlled using double dynamic transfer equations. Different from previous papers, this model considers the effect of production adjustments and energy conservation on the environment and the economy with the aim of achieving global optimization through integrated planning. To solve the proposed model, a hybrid algorithm with a standard and an antithetic method-based Particle Swarm Optimization (PSOs) is developed. A case study from Chaohua Colliery is presented to demonstrate the practicality and efficiency of the optimization model. The results and comparison analyses showed that as the dynamic adjustments strengthened the production and investment coordination for global optimization, integrated production and environmental investment planning provided coal mines with a superior method to solve conflicts between the ecology and the economy. [ABSTRACT FROM AUTHOR]

Due to environmental carrying capacity limitations, accelerated economic development can result in serious damage to watershed ecosystems. Using planned watershed-scale constructed wetlands (CW), this paper describes how balance can be achieved between the watershed ecosystem and economic development. To accomplish this goal, a bi-level optimization model is established, in which fuzzy random variables are used to describe the uncertainties. To solve the inherent complexity and uncertainty in the proposed model, a fuzzy random simulation-based nested adaptive particle swarm optimization is designed. The model is then applied to a real-world case, the Chaohu Lake watershed in China. The model results confirmed that; CW was an effective method to balance the watershed ecosystem and economic development, environmental policy plays a key role in controlling this balance and the integrated impact of the environmental policies and economic development on the watershed ecosystem were nonlinear. These practical results should be of assistance to watershed managers when making environmental policy decisions. [ABSTRACT FROM AUTHOR]

This paper discusses a two-stage based dynamic transportation assignment problem (TS-based DTAP) under a fuzzy random environment in an earth-rock transportation system. This problem is a multi-objective dynamic programming optimization process for minimizing total operational cost, transportation duration and total waste. Triangular fuzzy random numbers are used for the uncertain parameters, and a hybrid crisp approach and an expected value operator are introduced to deal with these uncertainties. A dynamic programming based contraction particle swarm optimization is developed to solve the proposed expected value model for TS-based DTAP. Then, the earth-rock dam construction at Pubugou Hydropower project is used as a practical application to verify the proposed approach. Results and analysis are presented to highlight the performance of the proposed TS-based DTAP model and the optimization method, which proves to be effective and relatively efficient compared to the models under other environments and a standard PSO algorithm. [ABSTRACT FROM AUTHOR]

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency. [ABSTRACT FROM PUBLISHER]

An optimal layout problem for temporary construction facilities (TFLP) is very important in large-scale construction projects. This problem involves the planning of temporary construction facilities within the boundaries of the restricted sites so that materials transportation and rearrangement costs are minimized and distances between the various departments are optimized. As construction continues, however, the temporary facilities may need to be dynamically relocated several times to accommodate the various operational demands. Thus, this study proposes a new method for dynamic TFLP with unequal-area departments. To begin with, a multi-objective dynamic optimization layout model based on facilities coordinates is presented, in which the transportation costs between the facilities are described as fuzzy random variables and the temporary facilities, represented as rectangles or squares, are restricted to two-dimensional geometric constraints. Subsequently, the multi-objective position-based adaptive particle swarm optimization (p-based MOPSO) is developed to obtain feasible optimization solutions for the proposed problem. In order to evaluate the performance of the proposed method, the Jinping-I Hydropower construction project is used as a practical example. The results and further analyses of the model, algorithm evaluation and effectiveness proved that satisfactory solutions were able to be obtained. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present an administrative and market-based optimization method for solving a problem of regional water resources allocation by considering a hierarchical structure under multiple uncertainties. To accomplish this, a multi-objective bi-level programming model is developed based on the water right distribution in a river basin. In this model, the stream flow ( i.e. , water supply) and water demand are considered as a fuzzy random variable and a random fuzzy variable, respectively. The regional authority, the leader in the hierarchy, seeks to maximize the total benefit to society while simultaneously minimizing pollution emissions. The sub-areas, the followers in the hierarchy, seek to maximize their own economic benefits. To deal with the inherent uncertainty, a transformation of variables into fuzzy variables is done, and through the expected value operation, the fuzzy variables are subsequently transformed into determined ones. For solving the complex non-linear bi-level programming model, a bi-level interactive method based on satisfactory solution with global–local–neighbor adaptive particle swarm optimization (GLN-aPSO) is designed as a combined solution method. A case study is presented to demonstrate the applicability and efficiency of this method. The interactive solutions associated with different minimal satisfactory degrees of the two objectives in the upper level have been generated. They can help the regional authority and the sub-areas to identify desired water allocation schemes according to their preferences and practical conditions, as well as facilitate in-depth analyses of tradeoffs between the objectives in the two levels. Finally, to verify that it is reasonable to use bi-level programming the results are compared with those of using single level programming. [ABSTRACT FROM AUTHOR]

The aim of this article is to develop an antithetic method-based particle swarm optimization to solve a queuing network problem with fuzzy data for concrete transportation systems. The concrete transportation system at the Jinping-I Hydropower Project is considered the prototype and is extended to a generalized queuing network problem. The decision maker needs to allocate a limited number of vehicles and unloading equipment in multiple stages to the different queuing network transportation paths to improve construction efficiency by minimizing both the total operational costs and the construction duration. A multiple objective decision-making model is established which takes into account the constraints and the fuzzy data. To deal with the fuzzy variables in the model, a fuzzy expected value operator, which uses an optimistic-pessimistic index, is introduced to reflect the decision maker's attitude. The particular nature of this model requires the development of an antithetic method-based particle swarm optimization algorithm. Instead of using a traditional updating method, an antithetic particle-updating mechanism is designed to automatically control the particle-updating in the feasible solution space. Results and a sensitivity analysis for the Jinping-I Hydropower Project are presented to demonstrate the performance of our optimization method, which was proved to be very effective and efficient compared to the actual data from the project and other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

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 article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic–pessimistic index. The iterative nature of the authors’ model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors’ optimization method, which is very effective as compared to the standard PSO algorithm. [ABSTRACT FROM AUTHOR]

The aim of this paper is to deal with the optimal navigation coordinated scheduling (NCS) problem in ship transportation of the Three Gorges Project in China, i.e. the Three Gorges Dam and the Gezhouba Dam. The NCS includes operational scheduling for two five-step locks in Three Gorges Dam and three single-step locks in Gezhouba Dam. A birandom multiple objective decision-making model is first proposed for the NCS problem to cope with hybrid uncertain environment where twofold randomness exists in practice. Then, particle swarm optimization is applied to search for the optimal solution. Based on real execution data, the results generated by a computer validate effectiveness of the proposed model and algorithm in solving large-scale practical problems is presented. [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]

Abstract: This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this paper, we focus on the vendor selection problem (VSP) for material supply in large-scale water conservancy and hydropower construction projects, and establish a corresponding model to solve this problem. After stating the problem, we introduce some basic knowledge of level-2 fuzzy sets and the way to transform them to normal fuzzy sets. Then we use the CCP model to handle the bifuzzy objective functions and constraints, based on a series of preparations, we transform the multi-objective integer model under bifuzzy environment into a deterministic model, then use particle swarm optimization (PSO) to solve this problem. The model is illustrated with an actual application to construction material supply. [Copyright &y& Elsevier]

Construction site layout planning (CSLP) is important to ensure that cost, time, quality and environmental impacts are strictly controlled. Even though extensive research has been conducted on CSLP problems, the effect of the multi-stakeholder interactions on optimal site layout plans has been less studied. Therefore, this paper proposes an agent-based decentralized bi-level mathematical model that incorporates decisions from three stakeholders, to examine the CSLP multi-stakeholder interactions. To solve the model, a particle swarm optimization (PSO)-based solution approach is developed to generate the CSLP, construction material logistics planning (CMLP) and security planning (SP) solutions in an integrated CSLP-CMLP/SP system. Specifically, the stakeholders are modelled as individual agents with respective decisions and objective functions, and the inter-dependencies between the layout planner and the other two stakeholders (i.e. the logistics planner and the safety manager) are simultaneously incorporated and dealt with using the bi-level model. The multi-stakeholder interactions are realized by iteratively updating the individual decisions to determine an optimal integrated solution in which the potential stakeholder conflicts in the construction stage are minimized. A case study is given to validate the practicality of the designed model in addressing the CSLP-CMLP and CSLP-SP interactions. The contributions of this research are its applicability to model the effect of multi-stakeholder interactions in agent-based decentralized models, the design of a PSO-based solution approach to achieve an integrated CSLP-CMLP/SP solution, and the provision of academic and practical guidance for decision makers to further enrich global decentralized CSLP research. • An agent-based decentralized model is proposed to study the effect of multi-stakeholder interactions on CSLP. • The system stakeholders are modeled as individual agents with their respective decisions and objective functions. • Agent-based modelling and bi-level programming are integrated to model the mutual interactions. • The paper enriches CSLP research by guiding academic and practical decision makers in a decentralized and coordinated manner. [ABSTRACT FROM AUTHOR]