Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

Abstract

Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories’ and three factories’ test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II.

Research on Two-Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer

“…In this paper SP, GD, and IGD are selected as evaluation indicators [44]. For the investigated problem, the Pareto optimal solutions of all test algorithms are regarded as the final Pareto optimal solutions.…”

“…where w is a weighted learning factor, Levy is Levy flight step, and β is a parameter between [1,2], and we set it to 1.5. u and v obey normal distribution. For subpopulation pop y , NSGA-II algorithm is used to generate new subpopulations new1 and new2 by LOX crossover [44] and swap mutation. Combine them to calculate the crowding distance and perform fast nondominated ranking; the first N/2 individuals are selected to obtain a new offspring population C2.…”

Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs

“…ree performance measures [40] are used for performance comparison, including convergence measure IGD, dominance measure Ω, and diversity measure Δ. In addition, because the real optimal Pareto fronts of the tested problem are unknown, this paper approximates the union set of nondominant solutions of the four algorithms as the optimal Pareto solutions.…”

“…Although the new machine has a high processing capacity, it also consumes a large amount of energy per unit time. Taking the scheduling of 10 jobs, 2 reentrances, 2 stages, 2 parallel machines at each stage, and processing time ranges of [10,40] as an example, the effects of different parameter combinations on the scheduling results are studied. In this case, the speed and power of machines are divided into three levels.…”

Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

“…The business mode of one headquarters and multi factories emerged and matured based on the network manufacturing technology. To solve a semiconductor production scheduling problem, Dong and Ye [20] presented a gray wolf algorithm to allocate production tasks among multiple heterogeneous factories reasonably to realize a collaborative optimization. Chung et al [21] studied a collaborative strategy for distributed factories and proposed a hybrid genetic algorithm to determine the production plan of each factory.…”