Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems

Abstract

Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

“…Over 94% of the solution approaches proposed for multi-objective EEJSPs are metaheuristic. The remaining ones propose game theory [12,136,137], simulation [28,[138][139][140][141], multi-agent systems [17], constraint programming [142,143], MIP-based heuristics [144,145], and deep learning methods [124].…”

Exact Methods and Heuristics for Order Acceptance Scheduling Problem under Time-of-Use Costs and Carbon Emissions

“…Indeed, integrating energy aspects into planning and scheduling, besides replacing obsolete equipment, is one of the most cost-effective ways to attain sustainability objectives [5] such as reducing GHG emissions and energy consumption. However, a trade-off between reducing energy consumption and productivity is always noticeable [6]. Nevertheless, energy sobriety is beneficial for both economic and environmental reasons.…”

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

“…In addition, CPT is a commonly used soil exploration survey in engineering applications because it is quick, economic, and could continuously obtain physical soil parameters (Eslami, 1997; Abu-Farsakh and Titi, 2004). In recent years, some researchers developed concepts of advanced artificial intelligence (AI) in different engineering applications, such as engineering mechanics, design of energy-saving buildings, automation, and construction (Coello and Aguirre, 2002; Nariman-Zadeh et al ., 2003; Hanna, 2007; Padmini et al ., 2008; Rossi and Lanzetta, 2013; Hu et al ., 2014; Zeng et al ., 2014; Kontovourkis et al ., 2015; Salido et al ., 2015; Adeniran and El-Ferik, 2017; Persson and Ölvander, 2017; Thimmisetty et al ., 2017; Al-Refaie et al ., 2018; Keles, 2018; Lin and Huang, 2018; Mirabi and Seddighi, 2018; Zimmermann et al ., 2018). In recent years, applications of AI models have become widespread through civil engineering projects (e.g., Armaghani et al ., 2017, 2018; Koopialipoor et al ., 2018).…”