Discrete lot-sizing and scheduling problems considering renewable energy and CO2 emissions

Abstract

Scheduling research increasingly focuses on reducing carbon emissions. Curbing carbon emissions during production and operation processes based on renewable energy sources is thus of priority concern. Therefore, this study analyzes two variants of the discrete lot-sizing and scheduling problem (DLSP): (1) a bi-objective DLSP in which renewable energy is considered and earliness tardiness and CO2 emissions are minimized simultaneously; (2) a DLSP in which renewable energy is considered and earliness tardiness is minimized, subject to a constraint on the CO2 emissions. Non-dominated solutions for the bi-objective DLSP are subsequently derived using the lexicographic weighted Tchebycheff (LWT) method. Experimental results clearly demonstrate that the LWT method is superior to the conventionally used weighted-sum method. In terms of practical applications, guidelines on how to set the number of periods, battery capacity, and carbon emissions constraints are also studied. Results of this study have significant managerial implications for actual production.