Robust optimisation for ripple effect on reverse supply chain: an industrial case study

Author(s):  
Gökhan Özçelik ◽  
Ömer Faruk Yılmaz ◽  
Fatma Betül Yeni
2018 ◽  
Vol 8 (3) ◽  
pp. 115-129 ◽  
Author(s):  
Saurabh Agrawal ◽  
Rajesh K. Singh ◽  
Qasim Murtaza

Author(s):  
A. T. Ubando ◽  
K. B. Aviso ◽  
A. B. Culaba ◽  
D. K. S. Ng ◽  
R. R. Tan

Polygeneration systems produce multiple energy products (i.e. electricity, heat, cooling), and other biochemical products (biofuels and syngas). Such systems offer a sustainable approach in meeting the ever-growing demand of energy, while reducing its environmental impact. The optimal design of such systems should consider the design of the supply-chain in producing the targeted energy products to reduce the resource consumption and waste generation and to maximize its economic potential. One of the important considerations in designing such a system is whether to out-source its raw materials or to produce them in-house. The criteria for such decision strategies are assessed through economics, product demand, and environmental impact. One holistic way to measure the environmental impact of such system is to consider the triple footprint: carbon, water, and land. The objective of this work is to maximize the economic potential while maintaining the footprints at acceptable levels and simultaneously meeting product demands. In this study, an adoption of fuzzy multi-objective approach is presented wherein the economic potential is introduced as a constraint. Moreover, predefined fuzzy trapezoidal-shaped limits for the product demand constraints are used which mimics the probabilistic demand scenario for each of the product streams. Lastly, the triple footprint constrains is utilized to assess the environmental impact of the polygeneration. The technique is demonstrated using a modified industrial case study of a polygeneration system.


2019 ◽  
Vol 76 ◽  
pp. 87-108 ◽  
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Luu Quoc Dat

2018 ◽  
Vol 10 (9) ◽  
pp. 3013 ◽  
Author(s):  
Manoj Paras ◽  
Lichuan Wang ◽  
Yan Chen ◽  
Antonela Curteza ◽  
Rudrajeet Pal ◽  
...  

The scarcity of natural resources and the problem of pollution have initiated the need for extending the life and use of existing products. The concept of the reverse supply chain provides an opportunity to recover value from discarded products. The potential for recovery and the improvement of value in the reverse supply chain of apparel has been barely studied. In this research, a novel modularized redesign model is developed and applied to the garment redesign process. The concept of modularization is used to extract parts from the end-of-use or end-of-life of products. The extracted parts are reassembled or reconstructed with the help of a proposed group genetic algorithm by using domain and industry-specific knowledge. Design fitness is calculated to achieve the optimal redesign. Subsequently, the practical relevance of the model is investigated with the help of an industrial case in Sweden. The case study finding reveals that the proposed method and model to calculate the design fitness could simplify the redesign process. The design fitness calculation is illustrated with the example of a polo t-shirt. The redesigned system-based modularization is in accordance with the practical situations because of its flexibility and viability to formulate redesign decisions. The grouping genetic algorithm could enable fast redesign decisions for designers.


2019 ◽  
Vol 53 (5) ◽  
pp. 1489-1512 ◽  
Author(s):  
Mohamadreza Fazli-Khalaf ◽  
Seyed Kamal Chaharsooghi ◽  
Mir Saman Pishvaee

Nowadays, the importance of caring about tremendous undesirable economical and technological effects of disruptions has impelled many researchers to design reliable supply chain networks. Moreover, the issue of intrinsic imprecision of input parameters should be gingerly regarded in the design of supply chain networks because it could have inverse impact on the quality of long-term planning decisions. Consequently, to handle the noted problems, in this paper, a reliable closed-loop supply chain network is formulated in which a new reliability method is introduced. The proposed formulation can effectively enable the design of a reliable network under different kinds of disruptions besides seeking for minimum overall costs of network design. On the one hand, a new effectual robust possibilistic programming (RPP) model is developed to confront with business-as-usual uncertainty in input parameters. Lastly, a real industrial case study is employed to validate the utility and practicability of the rendered model as well as presenting the efficiency and felicity of the developed RPP model.


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