An optimization problem of manufacturing systems with stochastic machine breakdown and rework process

Abstract

SUMMARYThis paper is concerned with optimization of production run time that takes stochastic breakdown and the reworking of defective items into consideration. In a real-life manufacturing process, production of imperfect quality items as well as random breakdowns of production equipment is inevitable. All defective items produced are assumed to be repairable through a rework process right after the regular production stops in each cycle. This research starts with derivations of the cost functions for production systems with breakdown (no-resumption policy is considered) and without breakdown taking place, respectively. Then cost functions of both cases are integrated. Theorems on conditional convexity of the overall cost function and bounds for optimal production run time are proposed and proved. This study concludes that although the optimal run time cannot be expressed in a closed form, it falls within the range of bounds. Hence, it can be pinpointed by the use of the bisection method based on the intermediate value theorem. A numerical example is provided to demonstrate its practical usages.

Intelligent assembly system for mechanical products and key technology based on internet of things

“…The resource optimization deployment is modeled as a multi-objective optimization problem (Changfeng et al 2006). Chiu (2008) was concerned with the optimization of production running time and considered the stochastic breakdown and reworking of defective items. The dynamic optimization driven by real-time perception data is considered a core characteristic of next-generation manufacturing systems ).…”

Optimal Uncertainty Decision of Innovation Products Output Based on Return Collection

“…Based on whether product production is a multistage manufacturing system or a different production mode [9,[14][15][16][17][18][19][20][21], it is necessary to consider remanufacturing costs, inventory costs, and shortage costs [22,23] and to make decisions in production planning and in production output when studying remanufacturing lot-sizing problems under an uncertain environment [24][25][26][27]. Worker errors [28,29], machine breakdowns, and product rejects [30] can all cause defective products. The defective rate can be predicted using a support vector machine model based on statistical learning theory [31].…”

A production policy considering reworking of imperfect items and trade credit

“…Konstantaras et al (2007) optimized the ordering lot size and obtained the optimal number of batches in two cases: (1) selling imperfect items to a secondary market, and (2) reworking imperfect items at some cost, and then using them as new items to satisfy demand. Chiu (2008) considered the production of imperfect items and the random breakdown of production equipment as inevitable. He therefore integrated the stochastic breakdown of production equipment and the reworking of imperfect items into the EPQ model to optimize production run time.…”