Green parallel machines scheduling problem: A bi-objective model and a heuristic algorithm to obtain Pareto frontier

Arash Zandi , Reza Ramezanin , Leslie Monplaisir


Sustainability consciousness in manufacturing has become an interesting topic for many researchers in recent years. There is also more concern in many companies about reducing energy consumption in manufacturing. Improving environmental health and safety, production cost saving, access to governmental incentives such as grants and tax credits and also improving the brand image are the most important reasons which is leading many companies to an environmental-friendly production planning. For example, one of the most applicable scheduling problems deals with planning jobs on numbers of parallel machines. In such an application, different machines have different technologies and different speed and power of energy consumption in manufacturing similar jobs. This article introduced a mathematical formulation which models the green parallel machines scheduling problem with total energy consumption and total completion time as objectives. Due to high computational complexity of the proposed model, a heuristic algorithm is developed to obtain the exact Pareto frontier of these two objectives with a polynomial complexity. Numerical experiments are presented to show the efficiency and speed of the proposed algorithm compared to solving the model using the optimisation software directly.
Author Arash Zandi (BM / DoL) - [K. N. Toosi University of Technology (KNTU)]
Arash Zandi,,
- Department of Logistics
, Reza Ramezanin - K. N. Toosi University of Technology (KNTU)
Reza Ramezanin,,
, Leslie Monplaisir - Wayne State University
Leslie Monplaisir,,
Journal seriesJournal of the Operational Research Society, ISSN 0160-5682, (N/A 70 pkt)
Issue year2020
Pages 967-978
Publication size in sheets0.55
Keywords in Englishgreen scheduling, sustainable manufacturing, parallel machines, energy consumption, heuristic algorithm
ASJC Classification1404 Management Information Systems; 1406 Marketing; 1408 Strategy and Management; 1803 Management Science and Operations Research
Languageen angielski
Not used for evaluationyes
Score (nominal)0
Score sourcejournalList
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.071; WoS Impact Factor: 2018 = 1.754 (2) - 2018=1.921 (5)
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