Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems

Nilakantan, J. Mukund; Li, Zixiang; Tang, Qiuhua; Nielsen, Peter

doi:10.1016/j.jclepro.2017.04.032

Cited by 73 publications

(41 citation statements)

References 26 publications

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“…They propose a mixed-integer programming model to minimize makespan and utilize a CPLEX solver for solving small-sized problems, making use of two metaheuristics: a restarted simulated annealing algorithm and a co-evolutionary algorithm, to address this NP-hard problem. Nilakantan et al [17] optimize carbon footprint and line efficiency simultaneously and utilize a multi-objective co-operative co-evolutionary algorithm. These types of problems are naturally suited to being solved by implementing metaheuristics, as can be seen from the solution strategies utilized in the literature listed above.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Janardhanan

Bocewicz

et al. 2019

Applied Mathematical Modelling

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Industries are incorporating robots into assembly lines due to their greater flexibility and reduced costs. Most of the reported studies did not consider scheduling of tasks or the sequencedependent setup times in an assembly line, which cannot be neglected in a real-world scenario. This paper presents a study on robotic assembly line balancing, with the aim of minimizing cycle time by considering sequence-dependent setup times. A mathematical model for the problem is formulated and CPLEX solver is utilized to solve small-sized problems. A recently developed metaheuristic Migrating Birds Optimization (MBO) algorithm and set of metaheuristics have been implemented to solve the problem. Three different scenarios are tested (with no setup time, and low and high setup times). The comparative experimental study demonstrates that the performance of the MBO algorithm is superior for the tested datasets. The outcomes of this study can help production managers improve their production system in order to perform the assembly tasks with high levels of efficiency and quality.

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Section: Introductionmentioning

confidence: 99%

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Janardhanan

Bocewicz

et al. 2019

Applied Mathematical Modelling

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“…The parameters of TLBO and AC are the same as in the literatures [17,43], and the parameters of DDABCA are shown in Table 1. The range means the different parameters have been tested for DDABCA, and the value is the best parameters after the test [44]. All of the algorithms are coded in Matlab programming language and tested on an Intel(R) Core(TM) i5-4430S CPU @ 2.70GHz PC with 4GB RAM.…”

Section: Validationmentioning

confidence: 99%

A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

et al. 2019

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Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

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“…In order to help enterprises to quantify the carbon footprint indicator, Liu et al [22] proposed a method for calculating the carbon footprint of workshop products and an improved fruit fly optimization algorithm to minimize the carbon footprint and production cycle of all products. Nilakantan et al [23] developed a study on the robot assembly line system, using a multi-objective co-evolution algorithm to solve the carbon footprint minimization problem. Piroozfar et al [24] proposed an improved multi-objective genetic algorithm to solve the multi-objective flexible job shop scheduling problem while minimizing the total carbon footprint and total number of delayed jobs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, for the sixth operation of job 4, it can select two machines at stage 3 for processing. The processing time of O 46 is P 23 46 = 1, the completion time of the previous operation is C 45 = 9. The earliest time of the first machine at stage 3, which meets the processing time constraint between operations allowed us to start processing is S 46 T 31 = 10.…”

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confidence: 99%

Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

Dong

2019

Applied Sciences

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Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories’ and three factories’ test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II.

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Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems

Cited by 73 publications

References 26 publications

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

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