Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times. (15th October 2020)

• Record Type: Journal Article
• Authors: Zhou, Shengchao; Jin, Mingzhou; Du, Ni
• Is Part Of: Energy. Volume 209(2020)
• Publication Date: 2020-10-15
• Subjects: Scheduling -- Energy efficiency -- Sustainable manufacturing -- Batch processing -- Heuristics; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118420
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Research on manufacturing scheduling has historically emphasized production efficiency. With rising environmental consciousness, manufacturing companies are paying increasing attention to energy efficiency on the shop floor. Manufacturing consumes a large amount of electricity globally. The mismatch between electric supply and demand has been a huge problem, which even becomes worse when renewable energy becomes more popular. The time-of-use (TOU) pricing policy is a widely used demand response (DR) approach, trying to align demand to supply. This paper considers energy-efficient scheduling of a single batch processing machine with non-identical job sizes and release times under a TOU electric tariff so as to simultaneously minimize total electricity cost, a criterion of environmental and energy sustainability, and makespan, a criterion of productivity. A mathematical formulation is developed to optimize electricity cost and makespan. Due to computational complexity, a hybrid multi-objective meta-heuristic algorithm is developed to find the Pareto front. Two constructive heuristics are presented to group jobs into batches. Two different approaches are presented to improve total electricity costs. The performance of the proposed model and algorithms is evaluated through extensive numerical experiments. Production managers can use the model and algorithms provided in this work to make a trade-off between productivity and sustainability. Highlights: A new energy-efficient scheduling tool for batch processing machine with arbitrary job sizes and release times. Consideration on the time-of-use pricing policy for tradeoff between productivity and total electricity cost. A fast hybrid multi-objective meta-heuristic algorithm to find the Pareto front for practical applications.