Evolving priority rules for resource constrained project scheduling problem with genetic programming

https://apo.org.au/sites/default/files/resource-files/2020-08/apo-nid309030.pdf

Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction

Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.

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Scheduling optimization of prefabricated construction projects by genetic algorithm

The net present value (NPV)-based resource constrained project scheduling problem (RCPSP) is a well-known scheduling problem in many industries, such as construction, software development, and manufacturing Over the last five decades, although different approaches have been proposed to solve the problem, no single approach has been shown to achieve satisfactory performances with quality solutions for a wide range of problems This study presents a hybrid immune genetic algorithm (IGA) to solve NPV-based RCPSPs Hybridizing a genetic algorithm (GA) with an immune algorithm (IA) enhances the overall performance of their standalone components (ie, only GA or IA) Performance of the proposed IGA is further improved by applying a variable insertion based local search (VINS) and forward-backward improvement (FBI) A restart mechanism is presented to the algorithm which induces diversity and helps to avoid becoming trapped in local optima Moreover, an activity move rule (AMR) is implemented to shift the negative cash flow associated activities to further improve the NPV Taguchi Design of Experiment (DOE) is conducted to investigate the impact of various parameters and to determine the appropriate set of parameters for the proposed IGA The performances of the proposed algorithms are tested on 17,280 standard benchmark instances ranging from 25 to 100 activities Comparison with the state-of-art algorithms through extensive numerical experiments reveal the effectiveness of the proposed algorithms Overall, the proposed algorithm outperforms existing algorithms, particularly the projects with 0% and 100% negative cash flow associated activities, the 75-activity instances, and the projects with two resources usage in terms of a lower value of average percentage deviation

/pdf/an-immune-genetic-algorithm-for-solving-npv-based-resource-38382f08yu.pdf

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

A recurring problem in project management involves the allocation of scarce resources to the individual jobs comprising the project. In many situations such as audit scheduling, the resources correspond to individuals (skilled labour). This naturally leads to an assignment type project scheduling problem, i.e. a project has to be processed by assigning one of several individuals (resources) to each job. In this paper we consider the nonpreemptive variant of a resource-constrained project job-assignment problem, where job durations as well as costs depend upon the assigned resource. Regarding precedence relations as well as release dates and deadlines, the question arises, to which jobs resources should be assigned in order to minimize overall costs. For solving this time-resource-cost-tradeoff problem we present a hybrid branch and bound / dynamic programming algorithm with a (rather efficient Monte Carlo type) heuristic upper bounding technique as well as various relaxation procedures for determining lower bounds. Computational results are presented as well.

Scheduling of project networks by job assignment

The resource-constrained project scheduling problem (RCPSP) is an important and challenging problem in the field of construction management. This paper presents a genetic algorithm (GA) for...

Jian Li

Papers

Solving Resource-Constrained Project Scheduling Problem via Genetic Algorithm