An iterated greedy algorithm for the planning of yarn-dyeing boilers

Abstract

Yarn dyeing is a critical link in the textile production chain that consumes the most time and energy. Today's dyeing shops receive hundreds of demands with thousands of different colors, different due dates, and different production requirements. This situation has made it very difficult for the human brain to create a minimum-cost production plan by complying with the due dates. In this study, a real-life problem of a company operating in the textile industry is discussed and a solution has been developed for the planning of yarn-dyeing boilers. The application was held in Bursali Textile, which is the major towel manufacturer operating in Turkey. The problem dealt with is basically in the nature of the variable-size bin-packing problem (VSBPP). The limited availability of bins (boilers) of different sizes and the packing of the items (yarn work orders) with due date constraints are the original aspects of this study. Multi-objective mixed integer programming model is developed to minimize two objectives. For the solution, the preemptive method called the lexicographic approach, in which the objectives are solved in order, is preferred. As the first objective, the overcapacity usage is minimized and then the second objective, which is the boiler usage cost, is minimized. Given that the VSBPP is strongly NP-hard, an iterated greedy algorithm with two different decoding approaches is proposed. Computational experiments were conducted on 20 randomly generated benchmark instances and a real-world industrial dataset. The numerical results show that good solutions can be obtained in seconds using the proposed approaches.