Abstract

Building a more sustainable society is an urgent requirement for today's 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.

Highlights

  • To improve the sustainability of enterprises, it is necessary for managers to improve operational strategies to make progress in achieving their economic, environmental and social, sometimes called the triple-bottom-line (TBL) objectives from the perspective of diverse stakeholders

  • Since studies on painting process scheduling problems can only be found in the limited literature, this section extends the related works to the job-shop scheduling problem

  • Given the above research background combined with the triple-bottom-line framework, the authors of this paper addressed all three TBL dimensions and expected to develop an operational strategy to achieve sustainable painting process scheduling in automobile maintenance services based on a multi-stakeholder perspective

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Summary

Introduction

To improve the sustainability of enterprises, it is necessary for managers to improve operational strategies to make progress in achieving their economic, environmental and social, sometimes called the triple-bottom-line (TBL) objectives from the perspective of diverse stakeholders. In addition to the fact that the painting process scheduling only focuses upon optimisation of equipment utilisation efficiency and emission re­ ductions, the focus upon the complex psychological behaviour of cus­ tomers and the improvement of customers’ perceived satisfaction must be added as an essential objective in service-oriented vehicle painting. Thereby, this helps enterprises to make progress in seeking to fulfil their social dimension of the TBL.

Literature review
Sustainable operations
Job-shop scheduling problem with different features
Multi-objective optimisation model formulation and its solution strategies
Summary
Problem description
Resource environment
Optimisation objectives
Model formulation for the painting process scheduling
Nonlinear 0–1 integer programming model formulation
Case study
Solution strategy
Computational results obtained by genetic algorithm
Pareto-optimal scheduling
Determining trade-offs among the three objectives
Pareto frontier analysis
Sensitivity analysis
Managerial insights
Conclusions and future work
Customers’ perception function
Determination of reference point
Findings
Formulation of the customers’ perception function
Full Text
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