Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration

Zhou, Binghai; Shen, Chengyi

doi:10.1016/j.jclepro.2018.04.251

Cited by 69 publications

(35 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization objective considered in this study was to minimize the line-side inventory while relaxing some critical assumptions, such as stock-out occurrence and line-side storage limitation. Zhou and Shen [9] addressed the material supply problem at the assembly line by proposing a multi-objective hybrid Tabu search and particle swarm optimization (TSPSO) algorithms. The optimization objectives considered were to minimize the total weighted tardiness and energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Emde et al [24] PSSP a Golz et al [15] OH b Emde and Boysen [25] DP Rao et al [26] GASA Faccio et al [27] Simulation Fathi et al [28] ACO Fathi et al [29] SA Muguerza et al [30] Fathi et al [31] PSO Emde and Schneider [32] NSA Peng and Zhou [33] HACO Zhou and Shen [9] TS PSO Zhou and Peng [34] OH & PSSP a Problem-specific solution procedure; b Other heuristics.…”

Section: Model Scheduling Loading Routingmentioning

confidence: 99%

“…As a common strategy for staying economically sustainable in the current competitive market, manufacturers aiming for mass customization strategies are interested in producing different models of their products while undergoing the smallest change in costs in terms of tools and facilities [8]. In such circumstances, among many manufacturing configurations, mixed-model assembly lines are widely used, as they enable manufacturers to produce different models of a basic product model in the same line without incurring a high cost [9]. In general, the use of a mixed-model assembly line enables the production of various versions of a base product with some differences from the base product [10,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Fathi

Ghobakhloo

2020

Sustainability

View full text Add to dashboard Cite

The fourth industrial revolution and the digital transformation of consumer markets require contemporary manufacturers to rethink and reshape their business models to deal with the ever-changing customer demands and market turbulence. Manufacturers nowadays are inclined toward product differentiation strategies and more customer-focused approaches to stay competitive in the Industry 4.0 environment, and mass customization and product diversification are among the most commonly implemented business models. Under such circumstances, an economical material supply to assembly lines has become a significant concern for manufacturers. Consequently, the present study deals with optimizing the material supply to mixed-model assembly lines that contribute to the overall production cost efficiency, mainly via the reduction of both the material transportation and material holding costs across production lines, while satisfying certain constraints. Given the complexity of the problem, a novel two-stage heuristic algorithm is developed in this study to enable a cost-efficient delivery. To assess the efficiency and effectiveness of the proposed heuristic algorithm, a set of test problems are solved and compared against the best solution found by a commercial solver. The results of the comparison reveal that the suggested heuristic provides reasonable solutions, thus offering immense opportunities for production cost efficiency and manufacturing sustainability under the mass customization philosophy.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Model Scheduling Loading Routingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Fathi

Ghobakhloo

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Recently, more and more attention from industries and academia has been shifted to the area of energy-efficient production. 3,4…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

Zhou

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

show abstract

“…Generally, the operators generally choose the route according to personal experience or even personal preference when distributing the materials since there are no standard routes or rules to follow, and the routes are not optimized by enterprise. As a result, the cost of production will increase, while the carbon emissions will increase due to waste of distribution cost and occupation of transportation resources [7,8]. Most importantly, choosing the route by operators' experience and preference could create some problems such as untimely distribution of material, lacking materials in some workstations, even stopping production, and increasing production costs [9,10].…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019

View full text Add to dashboard Cite

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

show abstract

Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration

Cited by 69 publications

References 30 publications

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

Contact Info

Product

Resources

About