| 1. |
- Cao, Cejun, et al.
(författare)
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A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains
- 2021
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Ingår i: International Journal of Production Economics. - : Elsevier. - 0925-5273 .- 1873-7579. ; 235
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Tidskriftsartikel (refereegranskat)abstract
- In the aftermath of large-scale natural disasters, supply shortage and inequitable distribution cause various losses, hindering humanitarian supply chains? performance. The optimal decisions are difficult due to the complexity arising from the multi-period post-disaster consideration, uncertainty of supplies, hierarchal decision levels and conflicting objectives in sustainable humanitarian supply chains (SHSCs). This paper formulates the problem as a fuzzy tri-objective bi-level integer programming model to minimize the unmet demand rate, potential environmental risks, emergency costs on the upper level of decision hierarchy and maximize survivors? perceived satisfaction on the lower level of decision hierarchy. A hybrid global criterion method is devised to incorporate a primal-dual algorithm, expected value and branch-and-bound approach in solving the model. A case study using data from the Wenchuan earthquake is presented to evaluate the proposed model. Study results indicate that the hybrid global criterion method guides an optimal strategy for such a complex problem within a reasonable computational time. More attention should be attached to the environmental and economic sustainability aspects in SHSCs after golden rescue stage. The proposed bi-level optimization model has the advantages of reducing the total unmet demand rate, total potential environmental risks and total emergency costs. If the decision-agents with higher authorities act as the leaders with dominant power in SHSCs, the optimal decisions, respectively taking hierarchical and horizontal relationships into account would result in equal performance.
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| 2. |
- Yang, Qin, et al.
(författare)
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Sustainable operations-oriented painting process optimisation in automobile maintenance service
- 2021
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Ingår i: Journal of Cleaner Production. - : Elsevier Science Ltd. - 0959-6526 .- 1879-1786. ; 324
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Tidskriftsartikel (refereegranskat)abstract
- Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises.
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