In this paper, we focus on a novel troublesome practical challenge, termed Weakly Flexible Job-Shop Scheduling (WFJSP) with parallel machines, where we are required to schedule the jobs with many unconventional limitations, including maximum machine usage constrained by computing resources (single server with limited memory), machines (i.e., idle time, office hours and efficiency), mold components (i.e., idle time, counts, and uncertain processes), and processes (i.e., types and orders) on self-developed intelligent mold processing system hosted by a single resource-limited server. We first highlight the pros and cons of pure theoretical works by comparing with multiple jop-shop scheduling methods, then emphasize the necessity of considering system resource utilization from perspective of software quality. We then shed light on the definitions of different job-shop scheduling problems and clarify the novel scenario at length with six innate conflicts of WFJSP. Based on these detailed analysis, three methods are devised and designed inspired by combining greedy algorithm, ranking strategy, mature infrastructures and well-designed system hierarchy. Experiments are conducted on our self-developed intelligent mold processing system. As a baseline, we introduce a genetic algorithm specifically designed for WFJSP, named WFJSP-GeneA. To evaluate the efficacy and practicability of these four approaches, we adopt five metrics dependent on the software quality attributes. Our proposed algorithms outperform on all the metrics compared with the baseline WFJSP-GeneA. Particularly, we observe outstanding performance of our algorithms on the metrics of Parsimony, Reliability, and Performance. We thus consider that proposed algorithms overcome WFJSP and are practically applicable on production system using mature infrastructures and well-designed system hierarchy.
