Optimization of an energy based bi-objective multi skilled resource investment project scheduling problem

Javanmard, Sh.; Afshar-Nadjafi, B.; Akhavan-Niaki, S.T.

doi:10.22060/miscj.2018.13848.5084

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	Optimization of an energy based bi-objective multi skilled resource investment project scheduling problem
Article 4, Volume 50, Issue 2, Summer and Autumn 2018, Page 129-140 PDF (717 K)
Document Type: Research Article
DOI: 10.22060/miscj.2018.13848.5084
Authors
Sh. Javanmard¹; B. Afshar-Nadjafi ¹; S.T. Akhavan-Niaki²
¹Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
²Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Abstract
Growing concern in management of energy due to the increasing energy costs, has forced managers to optimize the amount of energy required to provide products and services. This research integrates an energy-based resource investment project-scheduling problem (RIP) under a multi-skilled structure of the resources. The proposed energy-based multi-skilled resource investment problem (EB-MSRIP) consists of a single project with a set of tasks that require several skills to be competed. Each skill could be applied in several levels of efficiency, each including significant energy and implementation costs. Similar to RIPs, in the EB-MSRIP the required levels of skills are considered as decision variables and a bi-objective formulation is proposed for the problem. The first objective of the model minimizes total cost with regards to energy consumption cost and implementation cost of required multi-skilled resources, and the second one minimizes the project’s makespan. The epsilon constraint method has been used to validate the developed formulation on several small-size instances. For larger problem instances, as epsilon constraint method fails to obtain a solution, the multi-objective ant colony optimization (MOACO) algorithm has been implemented to tackle the problems. The key control parameters of the proposed MOACO are tuned by Taguchi method. Computational results in terms of several measures, including MID, DM, NPS and SNS, determine notable advantages of proposed MOACO.
Keywords
Multi-skilled project scheduling; resource investment problem; Energy usage; Ant colony optimization
Main Subjects
Computational Algorithms; Mathematical (Analytical) Modeling


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