Intelligent production control decision-making for apparel manufacturing process

Abstract

As a traditional labor-intensive industry with low-level automation, the production control decision-making process of today's apparel industry mainly rests on the experience and subjective assessment of shop floor management or simple computation. Facing the increasingly fierce competition and fast changing customer demand, apparel enterprises have stringent demands for lowering production costs and shortening the production lead time by using systematic and effective methods of production control and decision-making. The purpose of this research is to develop intelligent algorithm-based methodologies for the production control decision-making process of apparel manufacture. An effective framework for production control decision-making in an apparel manufacturing company was developed through integrating three types of apparel production control problems, namely order scheduling at the factory level, apparel assembly line (AAL) scheduling at the shop floor level, and AAL balancing at the assembly line level. On the basis of genetic algorithms (GA), these three types of problems were formulated mathematically and solved by effective methodologies. The order scheduling problem at the factory level considered multiple uncertainties in real apparel production, including uncertain processing time, uncertain arrival time and uncertain production orders. The uncertain time was described as continuous or discrete random variable. Based on the uncertain processing time of production processes, uncertain beginning time and completion time were determined by using the probability theory. A genetic optimization model with the variable length of sub-chromosomes was developed to generate the order scheduling solution. A bi-level genetic optimization model was proposed to solve the AAL scheduling problem with two orders. It comprised two genetic optimization processes on different levels, where the second-level GA (GA-2) was nested in the first-level GA (GA-1). GA-1 generated the optimal operation assignment of each order while GA-2 determined the optimal beginning time of each order based on the operation assignment from GA-1. In GA-1, a novel chromosome representation was proposed to deal with the flexible operation assignment in PBS. For the AAL balancing problem at the assembly line level, work-sharing, workstation revisiting and variable operative efficiencies were considered. A GA-based optimization model was developed to solve this problem. In this proposed model, a bi-level multi-parent GA (BiMGA) was developed to generate the optimal operation assignment to sewing workstations and the task proportions of the shared operation being processed in different workstations, and a heuristic operation routing rule was presented to route the shared operation of each garment to an appropriate workstation based on the results of BiMGA. The learning curve theory was used to describe the change of operative efficiency. Based on the production data from the real-life PBS, experiments were conducted to evaluate the performance of the proposed methodologies. The experimental results demonstrate the effectiveness of the proposed methodologies for the production control decision-making process of apparel manufacture.