# Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
## 2 Literature Review
While POLCA is designed to manage high-mix, low-volume (HMLV) production environments, its effectiveness relative to other systems, such as ATKS, GKS, and CONWIP, remains an active area of research. Some studies highlight its ability to regulate WIP effectively, while others suggest that its authorization mechanism may impose constraints on flow efficiency. A closer look at comparative analyses provides insights into where POLCA excels and where alternative systems might offer advantages. Ref. [11] compares three production control systems--ATKS, GKS, and POLCA--in a flexible flow shop environment, aiming to minimize total throughput time (TTT) and the percentage of tardy jobs. Using SIMIO simulation, the study evaluates different levels of control card availability and workcenter assignment rules. Results indicate that reducing control cards decreases shop floor throughput time (STT) by lowering WIP but increases TTT and tardy orders. Among the control systems, GKS performs best due to unrestricted order flow, while ATKS surpasses POLCA but lags slightly behind GKS. Moreover, the study finds that workcenter assignment using the least-loaded rule outperforms the minimum job number rule, better balancing workloads across workcenters. Ref. [12] investigates the impact of the authorization mechanism in POLCA by comparing it with a simplified variant, POLC, which removes job authorization and relies solely on earliest release date dispatching. Using ARENA simulation across 420 scenarios in two production structures (divergent and convergent flows), results show that POLC significantly outperforms POLCA in terms of total throughput time, percentage of tardy orders, and mean tardiness while maintaining lower WIP levels. Conversely, POLCA is more effective in reducing lateness variability. The study also reveals that high variability in processing times reduces the performance gap between the two systems, suggesting that neither is optimal in highly uncertain environments. In addition to performance metrics such as throughput time and WIP levels, researchers have also focused on job release strategies, which are critical for synchronizing production flow. Since POLCA controls WIP by limiting the number of active jobs, the way in which jobs are released into the system significantly influences overall performance. Different strategies, such as centralized versus decentralized job release, affect synchronization across workstations and impact key metrics like throughput time and tardiness. Additionally, POLCA's ability to manage both assembly and component manufacturing has been explored, further expanding its applicability. Ref. [13] compares centralized and decentralized job release strategies within POLCA systems, addressing synchronization challenges across workstations. The study employs discrete-event simulation in two configurations--pure flow-shop (PFS) and general flow-shop (GFS)--to assess throughput time, tardy orders, and lateness variability. Findings indicate that centralized job release, where jobs remain in a pre-shop pool until cards become available, outperforms decentralized release by reducing throughput time and improving tardiness metrics. Among dispatching rules, Modified Capacity Slack (MODCS) is shown to be the most effective in reducing tardy orders compared to Earliest Authorization Date (EAD) and First-Come-First-Serve (FCFS). However, the benefits of centralized release diminish in tightly synchronized PFS or GFS settings. Ref. [14] extends POLCA's application beyond assembly operations to component manufacturing. Using ARENA-based discrete-event simulation, the study compares POLCA with Immediate Release (IMR) under varying WIP caps and quantum settings. Results confirm that as WIP caps increase, throughput time rises accordingly. Nonetheless, POLCA proves more effective than IMR in controlling tardy orders and stabilizing lead times, validating its applicability in multi-stage production systems.