دانلود رایگان ترجمه مقاله زمان بندی و سطح بندی منابع پروژه با مدل های تصمیم گیری چند ویژگی (نشریه الزویر 2017)

زمان بندی و سطح بندی منابع پروژه با استفاده از مدل های تصمیم گیری چند ویژگی: اجرای مدل ها و مطالعه موردی

Project resources scheduling and leveling using Multi-Attribute Decision Models: Models implementation and case study

Abstract

Project scheduling is one of the most vital processes in Project Management. It is a widely discussed topic in academic and practical circles due to its importance and complexity. Manpower, machines, materials and equipment are used for the execution of project activities, but these mostly have limited availability, which can constrain project scheduling procedures. Project resources might exceed or fall short of the resource demand in a project’s time horizon. These considerations present issues to project managers who must try to properly allocate among these demands in order to achieve a near optimal utilization during a project’s lifetime. Resource leveling is among the greatest challenges faced by project managers as the success of a project largely depends on it. This is because peaks and valleys in the resource usage histogram are responsible for cost overruns due to the necessary recruitment, dismissal and training of the personnel Moreover, issues may arise regarding the efficient management of available resources given that large peaks correspond to fluctuations in resource allocation during a project’s life cycle or construction period. To address these issues, resource leveling provides procedures and frameworks that ensure the efficient management of resources to obtain smooth resource usage profiles. These procedures attempt to identify activities that should be delayed to resolve resource over-allocations under time and cost constraints. Given the existence of a variety of available rules that could be followed by project managers to prioritize activities, the paper at hand examines the implementation of five Multi-Attribute Decision Making models and how they perform in the scheduling of a solar park construction project. Namely these models are the Weighted Sum Method, Analytic Hierarchy Process, PROMETHEE, TOPSIS, Ordered Weighted Average (OWA) and Hybrid Weighted Average (HWA). Finally, the derived results are discussed in comparison with those obtained by the standard resource leveling procedures of MS-Project.

1 Introduction

The efficient management of available resources is one of the greatest and most complex problems that project managers (PMs) have to overcome. The Resource Leveling Problem (RLP) is a classic resource management problem faced by practitioners, managers and researchers. Resource leveling aims to minimize peaks and valleys in the resource histogram without increasing the project duration beyond the original critical path duration (Harris, 1990; Shtub, Bard, & Globerson, 2005). However, the problem can also come to bear in cases of limited resources, which often lead to extensions of the initial project duration (Hiyassat, 2001; Neumann & Zimmermann, 2000). During the past six decades, several different approaches have been developed for solving the RLP. Exact algorithms have been proposed in the literature, including integer and dynamic programming techniques (Bandelloni, Tucci, & Rinaldi, 1994; Neumann & Zimmermann, 2000). These approaches are suitable for small-sized networks due to the so-called “combinatorial explosion” phenomenon. Several heuristic procedures have also been developed to overcome the RLP, most of them are based on shifting heuristics or priority rule methods (Burgess & Killebrew, 1962; Neumann & Zimmermann, 2000). In addition, metaheuristic approaches such as genetic algorithms (Kyriklidis & Dounias, 2016; Kyriklidis, Vassiliadis, Kirytopoulos, & Dounias, 2014; Leu, Yang, & Huang, 2000; Li & Demeulemeester, 2016; Ponz-Tienda, Yepes, Pellicer, & Moreno-Flores, 2013) and simulated annealing algorithms (Anagnostopoulos & Koulinas, 2010; Son & Skibniewski, 1999) have attempted to find an optimum solution to this problem. Recently, hyperheuristic algorithms proposed to treat the RLP and resource allocation problems (Anagnostopoulos & Koulinas, 2010; Koulinas & Anagnostopoulos, 2011; Koulinas, Kotsikas, & Anagnostopoulos, 2014) have offered some promising results. The basic idea of these approaches is to create a resource profile based on the early start schedule calculated from the Critical Path Method (CPM), and then shift noncritical activities according to fixed heuristic rules.

This paper describes the application of Multi-Attribute Decision Making (MADM) methods to define the activity priorities to treat resource leveling under constraints when priority rule methods are implemented. The proposed framework aims to optimize resource usage without exceeding a pre-determined resource limit. This goal is accomplished by allowing PMs’ participation to the priorities determination phase of. A variety of well-established MADM models is implemented enabling the performance of a variety of decision attitudes.