Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Zhang, Hongliang; Ge, Haijiang; Pan, Ruilin; Wu, Yujuan

doi:10.3390/a11120210

Cited by 11 publications

(3 citation statements)

References 45 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scholars have mostly used heuristic algorithms to solve the bi-level programming model, due to its complexity. Zhang et al [29] used a multi-objective hierarchical genetic algorithm to solve a bi-level programming model to solve the job shop's scheduling and layout problems. Ma et al [30] proposed an algorithm combining a hybrid particle swarm algorithm and a differential evolutionary algorithm to deal with bi-level programming problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Temporary Reversible Lane Design Based on Bi-Level Programming Model during the Winter Olympic Games

et al. 2022

View full text Add to dashboard Cite

When the Winter Olympic Games were held, several roads were divided into exclusive lanes for the Winter Olympics to ensure the smooth passage of Winter Olympic vehicles. This reduced the number of lanes available for private vehicles, which caused a temporary tidal traffic phenomenon that led to traffic congestion and increased exhaust emissions. Temporary reversible lanes were added to the object lane to alleviate the temporary tide traffic phenomenon. A bi-level programming model was developed based on the principle of the minimum construction cost and the minimum total travel time of the road network. Meanwhile, three heuristics algorithms were used to solve the problem. The results show that the reasonable addition of temporary reversible lanes during the Olympic Games can reduce the total system travel cost, solve the temporary tidal traffic phenomenon, and alleviate traffic congestion.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Temporary Reversible Lane Design Based on Bi-Level Programming Model during the Winter Olympic Games

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The main aspects of energy consumption, e.g., processing energy, idle, and setup energy, in a production process are modeled and evaluated. Some researchers have started to consider other energy consumption in a production process, for instance, energy for component transportation [80,88].…”

Section: Extending Of Energy Consumption Aspectsmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

172

View full text Add to dashboard Cite

Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

show abstract

“…Content may change prior to final publication. [32] Finite string-based genetic algorithm × nested randomly [33] LMO PSO: Linear MultiObjective Particle Swarm Optimization × nested randomly [35] Bi-objective adaptive -constraint algorithm using × single-level N/A Branch-and-Bound transformation [36] HESA: Hybrid Evolutionary Simulated Annealing × nested randomly HGA: Hybrid Genetic Algorithm × nested randomly HABC: Hybrid Artificial Bee Colony algorithm × nested randomly [37] IMHGA: Improved Multi-objective Hierarchical Genetic × nested randomly Algorithm [38] Bi-objective Bilevel algorithm based on explicit enumeration × single-level N/A transformation reason, the solution selection is Not Applied (N/A). In fact, the difference between our proposed approach and existing ones is explained as follows.…”

Section: ) Shortcomings Of Existing Approachesmentioning

confidence: 99%

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

View full text Add to dashboard Cite

Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lowerlevel PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level productiondistribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multiobjective feature construction problem.

show abstract

Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Cited by 11 publications

References 45 publications

Temporary Reversible Lane Design Based on Bi-Level Programming Model during the Winter Olympic Games

Temporary Reversible Lane Design Based on Bi-Level Programming Model during the Winter Olympic Games

A review of energy-efficient scheduling in intelligent production systems

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Contact Info

Product

Resources

About