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 (716.25 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


References
[1] F. Shrouf, J. Ordieres-Meré, A. García-Sánchez, M. Ortega-Mier, Optimizing the production scheduling of a single machine to minimize total energy consumption costs, Journal of Cleaner Production, 67 (2014) 197-207. [2] A. Sharma, F. Zhao, J.W. Sutherland, Econological scheduling of a manufacturing enterprise operating under a time-of-use electricity tariff, Journal of Cleaner Production, 108 (2015) 256-270. [3] A. Rezvani, M. Gandomkar, M. Izadbakhsh, A. Ahmadi, Environmental/economic scheduling of a micro-grid with renewable energy resources, Journal of cleaner production, 87 (2015) 216-226. [4] B. Khoshnevisan, E. Bolandnazar, S. Barak, S. Shamshirband, H. Maghsoudlou, T.A. Altameem, A. Gani, A clustering model based on an evolutionary algorithm for better energy use in crop production, Stochastic environmental research and risk assessment, 29(8) (2015) 1921-1935. [5] S. Barak, M. Yousefi, H. Maghsoudlou, S. Jahangiri, Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study, Stochastic environmental research and risk assessment, 30(4) (2016) 1167-1187. [6] R.H. Möhring, Minimizing costs of resource requirements in project networks subject to a fixed completion time, Operations Research, 32(1) (1984) 89-120. [7] S.-H. Cho, S.D. Eppinger, A simulation-based process model for managing complex design projects, IEEE Transactions on engineering management, 52(3) (2005) 316-328. [8] L.L. Minku, D. Sudholt, X. Yao, Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design, in: Proceedings of the 14th annual conference on Genetic and evolutionary computation, ACM, 2012, pp. 1221-1228. [9] L.T. Bui, M. Barlow, H.A. Abbass, A multi-objective risk-based framework for mission capability planning, New Mathematics and Natural Computation, 5(02) (2009) 459-485. [10] B. De Reyck, R. Leus, R&D project scheduling when activities may fail, IIE transactions, 40(4) (2008) 367-384. [11] M. Sabzehparvar, S. Seyed-Hosseini, S. Nouri, A mathematical model for the multi-mode resource investment problem, Journal of Industrial Engineering International, 4(7) (2008) 25-32. [12] A.A. Najafi, F. Azimi, A priority rule-based heuristic for resource investment project scheduling problem with discounted cash flows and tardiness penalties, Mathematical Problems in Engineering, 2009 (2009). [13] M. Ranjbar, F. Kianfar, S. Shadrokh, Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm, Applied Mathematics and Computation, 196(2) (2008) 879-888. [14] A. Hattab, M. Haouari, Robust resource investment problem with time-dependent resource cost and tardiness penalty, in: Industrial Engineering and Engineering Management (IEEM), 2016 IEEE International Conference on, IEEE, 2016, pp. 1693-1698. [15] O. Bellenguez, E. Néron, Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills, in: International Conference on the Practice and Theory of Automated Timetabling, Springer, 2004, pp. 229-243. [16] O. Bellenguez-Morineau, Methods to solve multi-skill project scheduling problem, 4OR: A Quarterly Journal of Operations Research, 6(1) (2008) 85-88. [17] C. Heimerl, R. Kolisch, Work assignment to and qualification of multi-skilled human resources under knowledge depreciation and company skill level targets, International Journal of Production Research, 48(13) (2010) 3759-3781. [18] M. Walter, J. Zimmermann, Minimizing average project team size given multi-skilled workers with heterogeneous skill levels, Computers & Operations Research, 70 (2016) 163-179. 19] S.B. Othman, S. Hammadi, A. Quilliot, Multi-objective evolutionary for multi-skill health care tasks scheduling, IFAC-PapersOnLine, 48(3) (2015) 704-709. [20] S. Javanmard, B. Afshar-Nadjafi, S.T.A. Niaki, Preemptive multi-skilled resource investment project scheduling problem: Mathematical modelling and solution approaches, Computers & Chemical Engineering, 96 (2017) 55-68. [21] J. Xiao, X.-T. Ao, Y. Tang, Solving software project scheduling problems with ant colony optimization, Computers & Operations Research, 40(1) (2013) 33-46. [22] G. Campos Ciro, F. Dugardin, F. Yalaoui, R. Kelly, Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach, International Journal of Production Research, 54(16) (2016) 4854-4881. [23] Y.-C. Liang, A. Chen, W.-C. Kao, C.-C. Chyu, An ant colony approach to resource-constrained project scheduling problems, in: Proceedings of the fifth Asia Pacific industrial engineering and management systems conference, 2004, pp. 31.35.
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