Abstract
This research focuses on an Order Acceptance Scheduling (OAS) problem on a single machine under time-of-use (TOU) tariffs and taxed carbon emissions periods with the objective to maximize total profit minus tardiness penalties and environmental costs. Due to the NP-hardness of the considered problem especially in presence of sequence-dependent setup-times, two fix-and-relax (FR) heuristics based on different time-indexed (TI) formulations are proposed. A metaheuristic based on the Dynamic Island Model (DIM) framework is also employed to tackle this optimization problem. These approached methods show promising results both in terms of solution quality and solving time compared to state-of-the-art exact solving approaches.
Highlights
In this period of economic recession stemming from the COVID-19 pandemic [1] and coupled with the climate emergency, the implementation of effective policies and tools remains crucial to tackle current challenges
Experimental designs aim at determining levels of influence and interaction of external factors on a process. Both the FR heuristics and the metaheuristic depend on many factors such as the Observation Window (OW) length or the setup values for the FR heuristics and such as the mutation, crossover or learning rates for the Dynamic Island Model (DIM) metaheuristic
The reasons are that this easy-to-implement method has proven itself to be efficient and robust to tune genetic algorithm (GA) and heuristics in this domain
Summary
In this period of economic recession stemming from the COVID-19 pandemic [1] and coupled with the climate emergency, the implementation of effective policies and tools remains crucial to tackle current challenges. Order acceptance scheduling (OAS) is an abstraction to model this particular trend In this vein, this paper investigates a single machine OAS problem with release date and sequence-dependent setup times under TOU tariffs and taxed carbon emissions. This paper investigates a single machine OAS problem with release date and sequence-dependent setup times under TOU tariffs and taxed carbon emissions In this problem, the company has to decide which order to produce, among n, and establish a schedule . Bouzid et al [13] consider an arc-time-indexed (ATI) MILP to cope with the high complexity of this NP-hard problem and successfully solve some large instances These approaches are limited by design and require the use of heuristics.
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