Global strategy for optimizing textile dyeing manufacturing process via GA-based grey nonlinear integer programming

This paper describes the method and procedure for optimizing a textile dyeing manufacturing process in response to the designated waste minimization alternatives, the new environmental regulations, and the limitations of production resources. The optimization steps concerning numerous screening and sequencing combinations of those waste minimization alternatives along the timeline are, therefore, treated as an integral part of the optimal production-planning program under uncertainty. It utilizes a nonlinear integer optimization framework formulated in terms of multiple products to account for the trade-offs between costs and benefits relevant to each alternative in decision-making. Such an analysis profile covers not only the production costs and the incomes from product sale but also the emission/effluent charge and water resources fees required by new environmental relations. The inclusion of an interval analysis skill in model formulation notably reflects the subjectivity, impreciseness, and variations in the measurement of parameter values. The embedded systematic uncertainties can then be grossly addressed by a series of interval expressions as the same as they have been using in the grey systems theory for a decade. Facing the challenge of dealing with the numerous nonlinear constraints and integer variables in the optimization steps, genetic algorithm is applied as a means in the solution procedure to aid in finding the optimal decision. The case study, illustrating the applicability and suitability of this methodology in a textile dyeing firm, finally demonstrates the application potential and presents the germane insight in the nexus of environmental management and production planning. Such long-term strategic planning issues internal to the organization and its stakeholders in the context of emerging regulatory impact on corporate management exactly reflect the complexity and uncertainty of application challenges in the optimal production planning program in industry.