Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learning effect

Abstract

This study investigates an energy-aware flow shop scheduling problem with a time-dependent learning effect. The relationship between the traditional and the proposed scheduling problem is shown and objective is to determine a job sequence in which the total energy consumption is minimized. To provide an efficient solution framework, composite lower bounds are proposed to be used in a solution approach with the name of Boundsbased Nested Partition (BBNP). A worst-case analysis on shortest process time heuristic is conducted for theoretical measurement. Computational experiments are performed on randomly generated test instances to evaluate the proposed algorithms. Results show that BBNP has better performance than conventional heuristics and provides considerable computational advantage.

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

“…Another method, rarely used but also reported, is the bounds-based nested partition (BBNP) method applied by Liu et al [99] and He [100], and studies related to worst-case and lowerbound definitions are also addressed. Despite the high frequency of use of heuristics leading to optimal solutions, these methods are not suitable for solving large-instance problems, or situations with specific characteristics of human behavior such as experience, fatigue, forgetting, etc., that constitute real problems (due to the uncertainty involved).…”

A concise guide to scheduling with learning and deteriorating effects

“…Ma, Shao, and Wang ( 2014) Liu, Shi, and Shi (2018) 1|p Wu and Lee (2008) 1|p Wu, and Lee (2008 Low and Lin (2011) 1|p…”