A Critical Path Search and The Project Activities Scheduling

The purpose of the article is to plan a project of an electric vehicle technicalassistance point. the methodology of the study is to use the critical path method.the scientific novelty of the obtained results is that the article describes the practicalaspects of project planning of the creation of an electric vehicle technical assistancepoint. In particular, the tasks of the project are described, labor, material and financialresources necessary for its realization are specified. conclusions. The implementationof the developed project will effectively manage the content, time and resources ofthe project of the creation of an electric vehicle technical assistance point.

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The Generalized Fuzzy Critical Path Method: Analysis, Synthesis, Experiments

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v33.i5-8.150 ◽

2001 ◽

Vol 33 (5-8) ◽

pp. 8

Author(s):

Tatyana A. Tyshchuk ◽

Anatoliy I. Sleptsov

Keyword(s):

Critical Path ◽

Critical Path Method ◽

Method Analysis

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Breakthrough on Routine Service Cycle Time Improvement – Leap Frog Concept, Duri Field, Indonesia

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-e-303 ◽

2020 ◽

Author(s):

R. Irawan

Keyword(s):

Cycle Time ◽

Critical Path ◽

Well Control ◽

Routine Service ◽

New Process ◽

Work Concept ◽

Preparation Step ◽

One Step ◽

Time Average ◽

Moving Process

Leap frog concept was created to address the loss of single joint rig agility and drive the cycle time average lower than ever. The idea is to move the preparation step into a background activity that includes moving the equipment, killing the well, dismantling the wellhead and installing the well control equipment/BOP before the rig came in. To realize the idea, a second set of equipment is provided along with the manpower. By moving the preparation step, the goal is to eliminate a 50% portion of the job from the critical path. The practice is currently performed in tubing pump wells on land operations. However, the work concept could be implemented for other type of wells, especially ESP wells. After implementation, the cycle time average went down from 18 hours to 11 hours per job, or down by ~40%. The toolpusher also reports more focused operations due to reduced scope and less crew to work with, making the leap frog operation safer and more reliable. Splitting the routine services into 2 parts not only shortened the process but it also reduces noise that usually appear in the preparation process. The team are rarely seen waiting on moving support problems that were usually seen in the conventional process. Having the new process implemented, the team had successfully not only lowered cycle time, but also eliminated several problems in one step. Other benefits from leap frog implementation is adding rig count virtually to the actual physical rig available on location, and also adding rig capacity and completing more jobs compared to the conventional rig. In other parts, leap frog faced some limitation and challenges, such as: limited equipment capability for leap frog remote team to work on stuck plunger, thus hindering its leap frog capability, and working in un-restricted/un-clustered area which disturb the moving process and operation safety.

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Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1651-07 ◽

1998 ◽

Vol 1651 (1) ◽

pp. 48-52 ◽

Cited By ~ 1

Author(s):

Jeffrey L. Adler

Keyword(s):

Heuristic Search ◽

Transportation Networks ◽

Transportation Network ◽

Search Problems ◽

A Algorithm ◽

Running Time ◽

Search Effort ◽

Wide Range ◽

Path Search ◽

Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

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