Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

Lu, Chao; Gao, Liang; Li, Xinyu; Chen, Peng

doi:10.1016/j.jclepro.2016.07.029

Cited by 69 publications

(56 citation statements)

References 43 publications

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“…The suggested multiobjective optimiser was tested on the standard IEEE 30 bus system. The optimiser was capable of minimising the OPF problems in large-scale power systems with thousands number of buses Finally, another MOBSA was proposed for the energy-efficient multi-pass turning operation problem [135].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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“…With carbon emission and global warming becoming increasingly severe problems, energy-efficient scheduling is attracting much more attention than before. Massive consumption of coal-fired electricity in manufacturing sectors causes more greenhouse gas and lots of carbon dioxide (CO 2 ) will be released into the atmosphere directly; finally, the greenhouse effect has arisen [32]. Therefore, carbon emission reduction appears especially urgent [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Realizing Energy Savings in Integrated Process Planning and Scheduling

2019

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The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

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“…For a parameter optimization problem, Lu et al [28] developed a model for minimizing unit production energy consumption and machining precision value. He et al [29] proposed a model to reduce energy consumption and the makespan, then a heuristic algorithm is developed to obtain the optimal or approximate optimal solutions based on tabu search strategy.…”

Section: Energy Consumption In the Scheduling Problemmentioning

confidence: 99%

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

Yin

et al. 2016

Sustainability

Self Cite

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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.

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Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

Cited by 69 publications

References 43 publications

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Realizing Energy Savings in Integrated Process Planning and Scheduling

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

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