Energy-oriented scheduling based on Evolutionary Algorithms

Rager, Markus; Gahm, Christian; Denz, Florian

doi:10.1016/j.cor.2014.05.002

Cited by 70 publications

(32 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concerning this matter, Rager, Gahm, and Denz (2015) analyze energy cost minimizing parallel machine scheduling by means of a centralized evolutionary approach that levels resource utilization. Furthermore, Liu, Dong, Lohse, Petrovic, and Gindy (2014) applied a central NSGA-II for a job shop scheduling problem addressing the two objectives of total weighted tardiness and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling

Lang

Fink

Brandt

2016

European Journal of Operational Research

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling

Lang

Fink

Brandt

2016

European Journal of Operational Research

View full text Add to dashboard Cite

“…Such problems are well studied in project scheduling (see e.g., [9], [19], [2], [21]), but there are only a few results for machine scheduling problems, see e.g., Rager et al [20]. Notice that in machine scheduling, machines are unary resources that can process one job at a time, while in the more general project scheduling problems each resource can process multiple activities at the same time.…”

Section: Resource Levelingmentioning

confidence: 99%

“…Beside the above applications, resource leveling problems occur in scheduling problems where a balanced use of energy is one of the main objectives [20], in construction engineering [11], and in production planning [2], [13].…”

Section: Resource Levelingmentioning

confidence: 99%

Hard Planning and Scheduling Problems in the Digital Factory

Kis

Drótos

2017

Math for the Digital Factory

View full text Add to dashboard Cite

Production planning and scheduling with the aid of software tools in today's manufacturing industries have become a common practice which is indispensable for providing high level customer service, and at the same time to utilize the production resources, like workforce, machine tools, raw materials, energy, etc., efficiently. To meet the new requirements, problem modeling tools, optimization techniques, and visualization of data and results have become part of the software packages. In this chapter some recent developments in problem modeling and optimization techniques applied to important and challenging industrial planning and scheduling problems are presented. We will focus on new problem areas which are still at the edge of current theoretical research, but they are motivated by practical needs. On the one hand, we will discuss project based production planning, and on the other hand, we will tackle a resource leveling problems in a machine environment. We will present the problems, some modeling and solution approaches, and various extensions and applications.

show abstract

“…Thus, study of the multi-objective scheduling problem is very important in terms of theoretical significance and applying value. In recent years, many manufacturing companies and researchers have started to focus on the minimum energy consumption [2]. Just-in-time (JIT) production is another key issue because most companies are concerned not only with reducing inventory cost but also with delivering on time.…”

Section: Introductionmentioning

confidence: 99%

“…Mouzon and Yildirim [16] applied a greedy randomized adaptive search algorithm to a single-machine multi-objective optimization scheduling problem with the objective of minimizing the total energy consumption and total tardiness. Rager et al [2] proposed an evolutionary algorithm for energy-oriented parallel machine scheduling. For the flow-shop environment, Fang and Uhan et al [17,18] presented some new mathematical programming models of its scheduling problem that consider "peak power load, energy consumption, and associated carbon footprint in addition to cycle time".…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Yin

et al. 2016

Sustainability

View full text Add to dashboard Cite

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

show abstract

Energy-oriented scheduling based on Evolutionary Algorithms

Cited by 70 publications

References 28 publications

Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling

Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling

Hard Planning and Scheduling Problems in the Digital Factory

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Contact Info

Product

Resources

About