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Multiobjective Ant Colony System Algorithm for Component-Level Construction Schedule Optimization

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2025年第3期151卷

作者： Shen, Zhaozheng Wu, Jie Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Automatic generation of construction schedules has emerged as a key solution to address the inefficiencies and instabilities arising from the over-reliance on empirical judgment. However, traditional construction scheduling has been predominantly limited to regional levels, inadequately addressing the lean construction requirements of component-based prefabricated steel frame (PSF) structures. To bridge this gap, this study formulates an optimization model for the component-level resource-constrained project scheduling problem for PSF structures (C-RCPSP-PSF), which realizes the automatic extraction of precedence relationships from building information modeling three-dimensional (BIM 3D) models and the minimization of construction duration, costs, and carbon emissions. To address the C-RCPSP-PSF model, a novel multiobjective ant colony system (MOACS) algorithm is developed that utilizes three distinct colonies to individually tackle the objectives and combines taboo lists and global archives to enhance the search. Experimental results show the superior convergence and diversity of the MOACS over that of other competitive multiobjective optimization algorithms in solving the proposed model.

关键词： Component-level resource-constrained project scheduling problem Ant colony system algorithm Multiobjective optimization Carbon emission

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Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling

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COMPUTERS IN INDUSTRY 2005年第2期56卷 143-160页

作者： Kim, KW Yun, YS Yoon, JM Gen, M Yamazaki, G Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Tokyo Metropolitan Inst Technol Dept Intelligent Syst Tokyo 1900065 Japan Daegu Univ Sch Automot Ind & Mech Engn Kyungbuk 712714 South Korea Seoul Natl Univ Technol Dept Comp Sci & Engn Seoul 139743 South Korea

In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-hGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known NP-hard problem. Objectives described in this paper are to minimize total project time and to minimize total tardiness penalty. However, it is difficult to treat the rc-mPSP problems with traditional optimization techniques. The proposed new approach is based on the design of genetic operators with fuzzy logic controller (FLC) through initializing the revised serial method which outperforms the non-preemptive scheduling with precedence and resources constraints. For these rc-mPSP problems, we demonstrate that the proposed flc-hGA yields better results than conventional genetic algorithms and adaptive genetic algorithm. (C) 2004 Elsevier B.V. All rights reserved.

关键词： resource-constrained project scheduling problem scheduling hybrid genetic algorithm fuzzy logic controller

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Multi-objective multi-mode resource-constrained project scheduling with fuzzy activity durations in prefabricated building construction

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 158卷

作者： Yuan, Yisong Ye, Sudong Lin, Lin Gen, Mitsuo Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Dalian Univ Technol Int Sch Informat Sci & Engn Econ & Technol Dev Area Dalian 116620 Peoples R China Fuzzy Log Syst Inst FLSI Fukuoka 8200067 Japan Tokyo Univ Sci Res Inst Sci & Tech Tokyo Japan

Different from traditional project management, the prefabricated building (PB) construction project has a complex distributed supply chain model, and the overall project is completed by multi-stage cooperation. Therefore, the implementation process will be restricted by many constraints, and various uncertain factors will also interfere with the smooth implementation of the project. In order to improve the stability and reliability of the PB construction project implementation process, it is very important to study an effective robust project scheduling method considering resource constraints in an uncertain environment. In this paper, we formulate a PB construction resource-constrained project scheduling with multi-objective multi-mode, focus on the uncertainty of the execution time of the execution activity, and constructs the interval value of the execution time to express it through fuzzy theory;also considers the multiple objectives of PB construction project, including time-based profit, and cost-based profit. Secondly, we propose a hybrid cooperative co-evolution algorithm (HCOEA) to obtain the highly robust project scheduling, reduce the impact of the uncertainty of the execution time of the activity on the overall project. resource-constrained project scheduling problem (RCPSP) is an NP-hard combinatorial optimization problem. This paper also needs to consider the complex combination of time-resource and/or time-cost constraints. At the same time, it is necessary to consider the impact of time changes in different mode combinations. Therefore, how to design an effective multi-objective optimization algorithm is very difficult. This paper design a Hybrid Cooperative Co-evolution Algorithm (HCOEA) with multi-stage representation for the activity sequencing and the resource allocation, further improve the search efficiency. We improve the cooperative co-evolution framework with a self-adaptive mechanism and a self-adaptive selection process. Finally, benchmark

关键词： resource-constrained project scheduling problem Prefabricated building construction Evolutionary algorithm Multi-objective optimization

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The resource-constrained project scheduling model of Bianco and Caramia: clarifications and an alternative model formulation

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2014年第3期26卷 454-459页

作者： Naber, Anulark Kolisch, Rainer Bianco, Lucio Caramia, Massimiliano Tech Univ Munich TUM Sch Management D-80333 Munich Germany Univ Roma Tor Vergata Dept Enterprise Engn I-00133 Rome Italy

Recently, Bianco and Caramia (Flex Serv Manuf J 25(1-2), 6-24, 2013) proposed a new model for the resource-constrained project scheduling problem. Despite its potential, the presentation of the mixed-integer programming model contains some ambiguity which may create misunderstanding in the implementation phase. Here, we clarify the definitions of the decision variables and illustrate their corresponding values using a numerical example. Furthermore, we propose a different interpretation of two decision variables which gives rise to an alternative model formulation also presented using the same numerical example.

关键词： resource-constrained project scheduling problem Time-indexed model formulation Mixed-integer programming models Clarifications

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A model for resource-constrained project scheduling using adaptive PSO

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SOFT COMPUTING 2016年第4期20卷 1565-1580页

作者： Kumar, Neetesh Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

resource-constrained project scheduling problem (RCPSP) is an important, but computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly used meta-heuristics to solve such problems. In this work, a simple, effective and improved version of PSO i.e. adaptive-PSO (A-PSO) is proposed to solve the RCPSP. Conventional canonical PSO is improved at two points;during the particle's position and velocity updation, due to dependent activities in RCPSP, a high possibility arises for the particle to become invalid. To overcome this, an important operator named valid particle generator (VPG) is proposed and embedded into the PSO which converts an invalid particle into a valid particle effectively with the knowledge of the in-degree and out-degree of the activities depicted by the directed acyclic graph. Second, inertia weight that plays a significant role in the quick convergence of the PSO is adaptively tuned by considering the effects of fitness value, previous value of and iteration counter. Performance of the model is evaluated on the standard benchmark data of the RCPSP problem. Results show the effectiveness of the proposed model in comparison to other existing state of the art model that uses heuristics/meta-heuristics. The proposed model has the potential to be applied to other similar problems.

关键词： Particle swarm optimization NP-hard Heuristics resource-constrained project scheduling problem Makespan

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Effective genetic algorithm for resource-constrained project scheduling with limited preemptions

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2011年第2期2卷 55-65页

作者： Zhu, Jie Li, Xiaoping Shen, Weiming Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Jiangsu Peoples R China CNR Ctr Computer Assisted Construct Technol London ON Canada

In this paper, a specific preemptive resource-constrained project scheduling problem (PRCPSP) with makespan minimization is considered of which each activity could be interrupted at most M times. According to activity requirements and resource availability, resources are allocated to activities in different intervals. A resource-fragment chain is constructed to keep resource states dynamically. The resource allocation problem is transferred to the well-known 0-1 knapsack problem and solved by dynamic programming in pseudo-polynomial time complexity. The schedule enhancement method is developed to further improve the quality of obtained schedules by removing and rescheduling each activity in the activity list. By integrating the resource allocation and the schedule enhancement method, a genetic algorithm is proposed for the considered problem with the objective to minimize makespan. Computational experiments on the standard J30 and J120 sets show that the proposed algorithm is amongst the most competitive algorithms in literature for the pre-emptive cases.

关键词： Pre-emption resource-constrained project scheduling problem resource allocation Genetic algorithm

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An effective evolutionary algorithm for Pre-emptive resource-constrained project scheduling problems

An effective evolutionary algorithm for Pre-emptive Resource...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jie Zhu Xiaoping Li School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781424465866

In this paper, the Pre-emptive resource-constrained project scheduling project (PRCPSP) is considered. The paper mainly focuses on the problem 1_PRCPSP, where a maximum of one interruption per activity is allowed. A time-fragment linked-list method (TFLLM) is proposed to generate an effective solution for a given precedence-feasible activity list. Based on the TFLLM, an evolutionary algorithm is developed with the objective of makespan minimization. Computational experiments on the standard J30 and J60 sets show that the proposed algorithm can perform better than the compared approach in literature for the pre-emptive cases.

关键词： Pre-emption evolutionary algorithm resource-constrained project scheduling problem time-fragment linked-list

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A Lazy-Constraints Approach to resource-constrained project scheduling

A Lazy-Constraints Approach to Resource-Constrained Project ...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Lerch, D. Trautmann, N. Univ Bern Dept Business Adm Bern Switzerland

ISBN: (纸本)9781728138046

We study the complex combinatorial optimization problem to schedule the activities of a single project with the objective to complete the project within the shortest-possible amount of time such that the limited resource capacities as well as the prescribed precedence relations between pairs of the activities are taken into account. In addition to various specific solution algorithms, the related literature proposes several Mixed-Integer Linear Programming (MILP) models, but these models remain complex to solve even for small-sized instances. We present a novel approach based on an MILP model in which the resource-capacity constraints are formulated for all inclusion-minimal sets of activities which, due to the limited capacities of the resources, cannot be processed simultaneously. We propose to remove these constraints from the model and iteratively add those constraints back which are violated in the solutions obtained. For a set of test instances from the literature, our computational results indicate that with respect to both, the deviation of the project duration obtained from the lower bound devised from the critical-path length and the number of instances solved to optimality, the novel lazy-constraints approach outperforms ten state-of-the-art MILP models.

关键词： Operations Research resource-constrained project scheduling problem Lazy Constraints

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An Activity-List Based Nested Partitions Algorithm for resource-constrained project scheduling 11

An Activity-List Based Nested Partitions Algorithm for Resou...

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11th World Congress on Intelligent Control and Automation

作者： Xiao, Lei Tian, Jing Liu, Zhenyuan Huazhong Univ Sci & Technol Sch Automat Wuhan Hubei Province Peoples R China Key Lab Educ Minist Image Proc & Intelligent Wuhan Hubei Province Peoples R China

ISBN: (纸本)9781479958252

In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the resource-constrained project scheduling problem(RCPSP). This algorithm is based on traditional Serial scheduling scheme (SSS) and partitions the feasible solution space which is formulated by activity-lists into subregions by the nested partitions approach. We also utilize Double Justification as local search to improve the solutions. The algorithm is tested on J120 in PSPLIB with the result that the algorithm is relatively effective for solving large-scale, complex RCPSPs.

关键词： resource-constrained project scheduling problem Nested Partitions Activity-List Serial scheduling Schema Double Justification

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resource-constrained project scheduling with Autonomous Learning Effects

Resource-Constrained Project Scheduling with Autonomous Lear...

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作者： Jordan M. Ticktin California Polytechnic State University San Luis Obispo

学位级别：硕士

It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. This thesis considers the idea that learning effects could allow selected activities to be completed within reduced time, if they're scheduled after activities where workers learn relevant skills. This paper computationally explores the effect of this autonomous, intra-project learning on optimal makespan and problem difficulty. A learning extension is proposed to the standard RCPSP scheduling problem. Multiple parameters are considered, including project size, learning frequency, and learning intensity. A test instance generator is developed to adapt the popular PSPLIB library of scheduling problems to this model. Four different Constraint Programming model formulations are developed to efficiently solve the model. Bounding techniques are proposed for tightening optimality gaps, including four lower bounding model relaxations, an upper bounding model relaxation, and a Destructive Lower Bounding method. Hundreds of thousands of scenarios are tested to empirically determine the most efficient solution approaches and the impact of learning on project schedules. Potential makespan reduction as high as 50% is discovered, with the learning effects resembling a learning curve with a point of diminishing returns. A combination of bounding techniques is proven to produce significantly tighter optimality gaps.

关键词： Operations research Constraint programming IBM ILOG CP Optimizer resource-constrained project scheduling problem RCPSP Lower and upper bounding

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