Title

Tsp-Based Model For On-Site Material Handling Operations With Tower Cranes

Keywords

Automation; Decision support system; Optimization; Scheduling; Tower crane

Abstract

Today, with the necessity of timely, on-budget and high quality completion of projects, proper use of construction equipment is essential to ensure project success. Construction equipment places a large monetary burden on the project and if not utilized efficiently, it can result in economic losses. Cranes are one of the most important and expensive operational devices on construction sites. They play a central role, and often activities that rely on crane services fall on the project's critical path. Currently, material-handling scheduling is done by the crane operator or by an on-duty superintendent using his/hers personal judgment, making the process manual and time-consuming. Thus, developing an optimal schedule, which considers different dynamic constraints in construction job site, may not be possible based on the current practice. This leads to longer operation times and a negative impact on project cost. This paper presents the latest results of an ongoing study, which aims to design and implement a near-real time crane operation decision support system to be utilized directly by the crane operator or as an aid for the superintendent in scheduling optimal operation services. This system has several advantages such as maximizing the efficiency of crane operations, guaranteeing the best operation possible to reduce the crane's travel time, reducing crews and equipment idle time, and minimizing the dependence on subjective human judgments.

Publication Date

1-1-2013

Publication Title

ISARC 2013 - 30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress

Number of Pages

1288-1295

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.22260/isarc2013/0145

Socpus ID

84893560964 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893560964

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