Repetitive Project Modelling With Penalty and Incentive

Repetitive Project Modelling With Penalty and Incentive

Mohammed Shurrab (Department of Mechanical Engineering, Kyushu University, Fukuoka, Japan), Ghaleb Y. Abbasi (Department of Industrial Engineering, University of Jordan, Amman, Jordan), Osama Eljamal (Department of Earth System Science and Technology, Kyushu University, Fukuoka, Japan) and Jalal T. Tanatrah (Department of Industrial Engineering, University of Jordan, Amman, Jordan)
DOI: 10.4018/IJORIS.2018010101

Abstract

Repetitive construction activities have the same activities which are performed repeatedly. Repetitive projects include: pipelines, highways, and multi-story buildings. Repetitive projects have been modelled widely using the traditional network techniques although, they have some disadvantages. Furthermore, different approached have been developed for repetitive activities including the graphical and analytical techniques. The objective of this research is to add new enhancements on an approach called Repetitive Project Model (RPM) which is related to the repetitive construction projects. The enhancements incorporating the incentives and penalties within the RPM. This model incorporates a network technique, a graphical technique, and an analytical technique. A numerical example was demonstrated in this research paper to aid on using the suggested model in the real-life application.
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Introduction

A project is a collaborative enterprise contains a set of interrelated activities to produce the required outcome (Heagney, 2016). Repetitive projects are those projects that have repeated activities the same precedence relationships and the same duration to obtain similar units of outcome comprising the whole project (Sears et al., 2015).

A multi-stores construction building is one of the repetitive project’s application (Srisuwanrat, 2009; Schwindt & Zimmermann, 2015). The quantity of resources for each activity is carefully selected to: maintaining constant production rate and continuity of work for each crew on each activity throughout the project, allow all the time buffers required between activities of the same stage.

In any construction activity, if we select a certain quantity of resources for a particular construction activity then we have to calculate the activity duration and the associated direct cost. The duration of the activity could be reduced by allocating more resources and this in return will increase the direct cost (De Schepper, 2015; Cohen et al., 2012; Everett & Farghal, 1994). The activity production rate can be calculated by:

Production rate = required quantity of work/duration(1)

Hence, the duration and the direct associated costs are the most important elements since the quantity of work is usually fixed and known (Batselier & Vanhoucke, 2015; Maji & Jha 2013; Maravas & Pantouvakis, 2012) the objective is usually to finish the project ata certain duration within minimum direct cost. Hence, the constraints are to maintain production rates and continuity of work for each crew (Reda, 1990).

Different techniques were developed to model the repetitive construction projects. O'Brien et al. (1985) used the traditional critical path method (CPM) to plan large repetitive projects (e.g. schools). However, using CPM has some disadvantages for example: large number of activities is needed to idealize and exemplify the project, CPM does not maintain the continuity of work. Other graphical approaches were also used, for example, Arditi and Albulak (1986) developed a Line-of-balance scheduling approach in pavement construction.

The linear scheduling method was presented in several real life repetitive projects applications (Ipsilandis, 2006; Chrzanowski & Johnston, 1986; Johnston 1981). Although the mentioned graphical approaches are simple and easy to visualize the project, the production rates are not considered to be decision variables.

Moreover, several researches have been presented to develop analytical models for repetitive projects. Russell and Caselton (1988) presented extensions to linear scheduling optimization including two-state variables by using dynamic programming. The model objective was to minimize the project duration. Yang and Ioannou (2001) proposed a resource-driven scheduling for repetitive projects (i.e. a pull-system approach). Optimizing strategies were also provided for repetitive construction projects (Aziz, 2014; Aziz, 2013).

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