An approximation approach to simultaneous scheduling and routing in smart factories - Lim, Che Han (Nanyang Technological University, 2023)
In contrast to earlier manufacturing environments where items are either transported manually by human labor or human driven vehicles, automation requirements of Industry 4.0 mandate the use of fully automated transporters such as automated guided vehicles (AGVs) for transporting items between various job shops for processing in Smart Manufacturing shop floors efficiently and effectively. To satisfy operational demands and bridge the gap between practice and theory in smart manufacturing scheduling, this research uses flexible manufacturing systems (FMS) as a testbed. A typical FMS comprises a network of flexible job shops connected by guideways plied by transporters to pick up and deliver jobs between job shops. Due to these interdependent precedencies between transportation and production processes, it is necessary to synchronize both processes to maximize overall operational efficiency, while minimizing time and distance wastages. The FMS is suited for this end due to its inherent complexities arising from the said interdependent precedencies. Algorithms are needed to address both machine scheduling and vehicle routing concurrently to meet the aforementioned operational requirements. Hence, this research aims to develop a concurrent scheduling and routing methodology using MILP (Mixed Integer Linear Programming) under static settings, which can be extended to solve robust dynamic scheduling problems which consider random interruptions such as breakdowns, sudden insertion and withdrawals of jobs in catering to the real-time, stochastic operational nature of smart factories. Prior literature has proven that each of the individual machine scheduling and vehicle routing subproblems encapsulated within this problem is NP-hard. Hence, its complexity is increased tremendously when both subproblems are considered concurrently. To mitigate this complexity, a two-phase iterative heuristic employing a machine-operation assignment centric decomposition scheme is proposed here. The first phase involves approximating the flexible job shop scheduling problem with transportation (FJSPT) by considering a classical flexible job shop scheduling problem (FJSP) model augmented with intermachine transportation constraints, under the assumption of unlimited transporters. The augmented FJSP reduces the original FJSP solution space, while serving as a heuristic in guiding the search towards good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these machine-operation assignments and solved for makespan. To the best of knowledge, the constructed JSPT considers job pre-emption, which was not considered by prior research. Experiments indicate that job pre-emption is instrumental in enabling this approach to outperform certain benchmarks. In general, results show that this approach is effective, robust and competitive against existing benchmarks.