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Thousands of aging education buildings in the US are in urgent need of maintenance to ensure their operational performance. The planning and scheduling of these maintenance activities need to be optimized to minimize their total maintenance costs and reduce the challenging deferred maintenance problems confronting education institutions in the US. This paper presents the development of a novel model for optimizing the planning of maintenance activities in education buildings. The computations of the developed model are implemented utilizing an optimization module that identifies an optimal maintenance schedule for minimizing total maintenance costs; a scheduling module that calculates the start day, start hour, finish day, and finish hour of all maintenance activities; and a cost estimating module that estimates the total maintenance cost for each generated schedule. An application example of three education buildings that include plumbing, HVAC, and electrical maintenance activities was analyzed to illustrate the use of the model and evaluate its performance by comparing its results to those generated by widely used existing methods for planning maintenance activities of education buildings. The outcome of this analysis confirms the model was capable of outperforming existing methods in reducing the total maintenance cost by more than 12%. The main contributions of the developed novel model to the body of knowledge include its innovative methodology for identifying optimal ranking of maintenance activities, selecting optimal overtime use and crew size for all maintenance activities, generating an optimal maintenance schedule that complies with all practical constraints such as the availability of classrooms during their nonoperational hours, and minimizing total maintenance cost of all scheduled activities. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects. [ABSTRACT FROM AUTHOR]

Procuring the correct materials at the right time and for the lowest cost is essential in construction. This fact underlines the importance of an optimized material procurement schedule integrated with the construction schedule/program. However, few studies examine the development of a material procurement schedule by integrating the construction schedule and optimizing material costs as well as any material shortage impact. In addition, budget constraints and maximum storage capacity are rarely captured in existing models for material procurement optimization. To address these shortcomings, an optimization model is developed using the nondominated sorting genetic algorithm II (NSGA-II) and executed in MATLAB R2017a. This model was implemented in a construction project that developed a material delivery schedule based on the budget constraints and maximum storage capacity for five majorly used building materials, resulting in a costs saving of 31.17% and a reduction in shortage impact of 83.47% compared with the actual delivery schedule. The developed model also incorporates the minimum order quantity and standard shipping size as well as avoiding surplus materials, leading to sustainable procurement of materials. Construction practitioners can use this model for procuring materials when facing budget constraints in a construction project with the lowest cost and least shortage and without excess or insufficient purchasing. The model will aid in completing the project within the stipulated time and budgeted cost. [ABSTRACT FROM AUTHOR]

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction. [ABSTRACT FROM AUTHOR]

Despite many research efforts that have focused on the multimode resource-constrained project scheduling problem (MRCPSP), very little success has been achieved in solving the problem for large-scale projects. In this paper a new hybrid optimization method is presented to achieve an advancement in optimal planning and scheduling of large-scale construction projects with multiple duration/resource execution modes and resource constraints. The proposed method consists of a novel heuristic and unique genetic optimization algorithm. The heuristic is designed to achieve schedules with efficient resource utilizations through dynamic selection of modes. A genetic optimization algorithm is integrated to the proposed optimization method to further improve the quality of the solutions obtained in the heuristic phase. The computational experiment results reveal that the new hybrid method outperforms the state-of-the-art methods and achieves high-quality solutions in significantly less computation time, especially for large projects. The main contribution of the proposed hybrid optimization method is that it enables significant savings by optimal planning and scheduling of medium- and large-scale construction projects with multiple duration/resource execution modes and resource constraints. [ABSTRACT FROM AUTHOR]

A construction schedule must satisfy multiple project objectives that often conflict with each other. While several earlier approaches attempted to generate optimal schedules in terms of several criteria, most of their optimization processes were segmented into multiple steps. Owing to such a lack of simultaneous optimization, limited alternative solutions could be searched and some trade-offs between goals could not be identified. This paper presents an optimization approach that enables a simultaneous search for an optimal construction schedule in terms of three objectives: minimization of construction duration, cost, and resource fluctuation. A multiobjective optimization (MOO) approach was adopted to generate scheduling solutions considering all those objectives. To enable a simultaneous optimization, we propose a new data structure that can compute the performances of solutions in terms of all the objectives at the same time. A Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate the optimization procedure. Then the proposed optimization approach is implemented in an existing case study. The result indicates that the proposed approach has the capability to explore and generate a greater range of solutions compared to existing models. Trade-offs between all three objectives are identified, limitations and further research needs are discussed. [ABSTRACT FROM AUTHOR]

Despite the fact that companies manage multiple projects simultaneously, most research on resource-constrained project scheduling has focused on single projects. This paper presents a backward-forward hybrid genetic algorithm (BFHGA) for optimal scheduling of a resource-constrained multiproject scheduling problem (RCMPSP). The new approach combines complementary strengths of the backwardforward scheduling method, genetic algorithms, and simulated annealing. BFHGA was tested on four single-project case examples, one portfolio case example, one real portfolio, and 26 test portfolio instances. The proposed algorithm obtained the best solution for all of the single-project case examples, and outperformed five state-of-the-art meta-heuristics and five popular heuristics for the resourceconstrained multiproject scheduling problems. The computational results show that the BFHGA is a fast and effective algorithm for scheduling multiple projects with common limited resources. The performance gap between the BFHGA and popular heuristics reveals the potential for improving the existing heuristics for the RCMPSP. [ABSTRACT FROM AUTHOR]

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge. [ABSTRACT FROM AUTHOR]

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme. [ABSTRACT FROM AUTHOR]

In the metropolitan cities of developed and developing countries, longer journeys are mostly performed by two or more modes. In the event of availability of suburban trains and public buses, commuters prefer to travel a longer stretch of their journeys by train, so as to avoid traffic congestion on roads, and the remaining part by buses to reach local areas if their final destination is not in close proximity to railway stations. Normally suburban trains have fixed corridors and buses have the flexibility to serve remote local areas. Thus design of feeder routes from railway stations to various destinations and the transfer time from trains to buses plays a very important role and can be controlled by transport planners. A considerable amount of research has been done on the independent design of a bus route network without considering the effect of train services. Researchers have made attempts using heuristics, simulation, expert systems, artificial intelligence, and optimization techniques for design of routes and schedules. So far, limited effort has been made in modeling coordinated operations. In this research, a new hybrid algorithm which exploits the benefits of genetic algorithms and a well tested heuristic algorithm for the study area is discussed. More convincing results in terms of feeder routes and coordinated schedules at the selected railway station are obtained by the proposed hybrid algorithm as compared to earlier approaches adopted by the writers for the same study area. [ABSTRACT FROM AUTHOR]

The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [ABSTRACT FROM AUTHOR]

The scarcity of water resources is the driving force behind modernizing irrigation systems in order to guarantee equal rights to all beneficiaries and to save water. Traditional distribution systems have the common shortcoming that water must be distributed through some rotational criteria. This type of distribution is necessary to spread the benefits of scarce resources. Irrigation systems based on on-demand delivery scheduling offer flexibility to farmers and greater potential profit than other types of irrigation schedules. However, in this type of irrigation system, the network design has to be adequate for delivering the demand during the peak period whilst satisfying minimum pressure constraints along with minimum and maximum velocity constraints at the farm delivery points (hydrants) and in the pipes, respectively. In this paper, optimum design and management of pressurized irrigation systems are considered to be based on rotation and on-demand delivery scheduling using a genetic algorithm. Comparison is made between the two scheduling techniques by application to two real irrigation systems. Performance criteria are formulated for the optimum design of a new irrigation system and better management of an existing irrigation system. The design and management problems are highly constrained optimization problems. Special operators are developed for handling the large number of constraints in the representation and fitness evaluation stages of the genetic algorithm. The performance of the developed genetic algorithm is assessed in comparison to traditional optimization techniques. It is shown that the methodology developed performs better than the linear programming method and that solutions generated by the modified genetic algorithm show an improvement in capital cost. The method is also shown to perform better in satisfying the constraints. Comparison between on-demand and rotation delivery scheduling shows that a greater than 50% saving can be achieved in total cost at the cost of reducing flexibility in the irrigation time. Finally, it is shown that minimizing standard deviation of flow in pipes does not result in the best distribution, and therefore minimum cost, neither for systems with uniform flows or those with large variations in discharge at hydrants. [ABSTRACT FROM AUTHOR]

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects. [ABSTRACT FROM AUTHOR]

Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model. [ABSTRACT FROM AUTHOR]