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Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively.

Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem

Time, cost, and quality are three important but often conflicting factors that must be optimally balanced during the planning and management of construction projects. Tradeoff optimization among these three factors within the project scope is necessary to maximize overall project success. In this paper, the MOABCDE-TCQT, a new hybrid multiple objective evolutionary algorithm that is based on hybridization of artificial bee colony and differential evolution, is proposed to solve time–cost–quality tradeoff problems. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. A numerical construction project case study demonstrates the ability of MOABCDE-generated, non-dominated solutions to assist project managers to select an appropriate plan to optimize TCQT, which is an operation that is typically difficult and time-consuming. Comparisons between the MOABCDE and four currently used algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC), verify the efficiency and effectiveness of the developed algorithm.

Two-Phase Differential Evolution for the Multiobjective Optimization of Time–Cost Tradeoffs in Resource-Constrained Construction Projects

Concurrent minimization of project time and project cost is an important issue in construction planning and management. Tradeoff optimization between these two variables is necessary to maximize overall construction project benefit. This paper presents a two-phase differential evolution (DE) model to resolve these problems. This model is able to effectively consider both time-cost effects and resource constraints. First, we introduce a novel multiple-objective algorithm, the chaotic initialized multiple objective differential evolution with adaptive-mutation strategy-based time-cost tradeoff, to determine the execution mode that best optimizes the time-cost balance. Subsequently, we introduce a DE-based resource-constrained method to generate a feasible schedule. A real construction case study is then used to illustrate the application of the proposed algorithm. Performance comparisons done with the nondominated sorting genetic algorithm, multiple objective particle swarm optimization, and multiple objective differential evolution further verify the efficiency and effectiveness of the proposed algorithm.

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

AbstractProject scheduling is an important part of project planning in many management companies. Resource leveling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid undesirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-parent crossover operators to utilize the information of the population efficiently to en...

Integrating Chaotic Initialized Opposition Multiple-Objective Differential Evolution and Stochastic Simulation to Optimize Ready-Mixed Concrete Truck Dispatch Schedule

AbstractDelivering ready-mixed concrete (RMC) efficiently to construction sites is a practical concern and one of the most challenging tasks for RMC batch managers. Batch plant managers must consider both time and order factors in order to set an RMC truck dispatch schedule that successfully balances batch plant (supplier) and construction site (customer) priorities. This paper develops an optimization framework that integrates discrete event simulation (DES) and multiobjective differential evolution (MODE) to determine the solutions for RMC truck dispatch scheduling. The model takes into consideration uncertainties as well as unexpected situations such as truck breakdowns during delivery. In addition, the chaotic initialized opposition multiobjective differential evolution (COMODE) algorithm is used to optimize the dispatching schedule, which minimizes the total waiting duration both of RMC trucks at construction sites and of construction sites for trucks. A batch plant case study is used to illustrate t...

Solving Resource-Constrained Project Scheduling Problems Using Hybrid Artificial Bee Colony with Differential Evolution

AbstractSolving resource-constrained (RC) project scheduling problems is one the most important tasks in the project planning process. This study presents a new hybrid approach, named Artificial Bee Colony with Differential Evolution, to handle resource-constrained problems (ABCDE-RC). The proposed algorithm integrates crossover operations from differential evolution (DE) with original artificial bee colony (ABC) to balance exploration and exploitation phases of the optimization process. Furthermore, this study applies a serial method to reflect individual-vector priorities into the active schedule to calculate project duration. The ABCDE-RC algorithm is compared with benchmark algorithms considered using a real construction case study and a set of standard problem available in the literature. The experimental results demonstrate the efficiency and effectiveness of the proposed model. The ABCDE-RC is a promising alternative approach to handling resource-constrained project scheduling problems.

Opposition-Based Multiple-Objective Differential Evolution to Solve the Time-Cost-Environment Impact Trade-Off Problem in Construction Projects

AbstractCurrent competitive conditions in the construction market require that construction companies satisfy customer needs using increasingly tight project budgets. The key indicators of success currently used on most construction projects include meeting project duration, cost, and quality targets. Project decision makers seldom consider customer expectations related to project-related environmental impact. Quantitative assessments of project-related emissions should be conducted during the project-planning phase. Trade-off optimization among project duration (time), project cost, and project environmental impact is necessary to enhance the overall construction project benefit. This paper develops a novel optimization algorithm, the opposition-based multiple-objective differential evolution (OMODE), to solve the time–cost–environmental impact tradeoff (TCET) problem. This novel algorithm uses an opposition-based learning technique for population initialization and for generation jumping. Opposition num...

Opposition Multiple Objective Symbiotic Organisms Search (OMOSOS) for Time, Cost, Quality and Work Continuity Tradeoff in Repetitive Projects

Abstract Construction managers often face with projects containing multiple units wherein activities repeat from unit to unit. Therefore effective resource management is crucial in terms of project duration, cost and quality. Accordingly, researchers have developed several models to aid planners in developing practical and near-optimal schedules for repetitive projects. Despite their undeniable benefits, such models lack the ability of pure simultaneous optimization because existing methodologies optimize the schedule with respect to a single factor, to achieve minimum duration, total cost, resource work breaks or various combinations, respectively. This study introduces a novel approach called “opposition multiple objective symbiotic organisms search” (OMOSOS) for scheduling repetitive projects. The proposed algorithm used an opposition-based learning technique for population initialization and for generation jumping. Further, this study integrated a scheduling module (M1) to determine all project objectives including time, cost, quality and interruption. The proposed algorithm was implemented on two application examples in order to demonstrate its capabilities in optimizing the scheduling of repetitive construction projects. The results indicate that the OMOSOS approach is a powerful optimization technique and can assist project managers in selecting appropriate plan for project.

Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time-cost-quality trade-off

AbstractTime, cost and quality are three factors playing an important role in the planning and controlling of construction. Trade-off optimization among them is significant for the improvement of the overall benefits of construction projects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project manag...