With wafer residency time constraints for some wafer fabrication processes, such as low pressure chemical-vapor deposition, the schedulability and scheduling problems are still open. This paper aims to solve both problems. A Petri net (PN) model is developed for the system. This model describes when the robot should wait and a robot wait is modeled as an event in an explicit way. Thus, to schedule a single-arm cluster tool with wafer residency time constraint is to decide how long a robot wait should be. Based on this model, for the first time, we present the necessary and sufficient conditions under which a single-arm cluster tool with residency time constraints is schedulable, which can be checked analytically. Meanwhile, a closed form scheduling algorithm is developed to find an optimal periodic schedule if it is schedulable. Also, a simple method is presented for the implementation of the periodic schedule for steady state, which is not seen in any previous work.

There are wafer fabrication processes in cluster tools that require wafer revisiting. If a swap strategy is applied to dual-arm cluster tools handling wafer revisiting, a three-wafer periodical process is formed with three wafers completed in each period. Such a period contains three cycles in a revisiting process and three cycles in a nonrevisiting one. Hence, analysis and scheduling of such tools become very complicated. In this paper, a Petri net (PN) model is developed to describe their operations. Based on it, it is found that, if a swap strategy is applied, such tools are always in a transient state. A systematic method is then presented to analyze their performance. With the help of the proposed PN model, this work, for the first time, derives the optimality conditions of three-wafer period scheduling. Industrial application examples are given to show the results.

For some wafer fabrication processes in cluster tools, e.g., atomic layer deposition (ALD), wafer revisiting is required. Typically, in such processes, wafers need to visit two consecutive processing steps several times. Such a revisiting process can be denoted as (mi, mi + 1)^h, where i means the ith-step and m_i and m_{i + 1} mean the corresponding quantity of the processing modules in i and (i+1)th steps, and h the number of visiting times. This paper conducts a study for scheduling single-arm cluster tools with such a wafer revisiting process. The system is modeled by Petri nets (PNs) to guarantee the feasibility of robot activities. Based on the model, a deadlock avoidance policy is presented. With the control policy, cycle time analysis for the revisiting process is made. With the fact that wafer processing times are much longer than robot movement times in cluster tools, it is shown that, when m_i = m_{i + 1} = 1, i.e., each step has only one processing module, the optimal one-wafer cyclic schedule is deterministic and unique, and the minimal cycle time can be calculated by an analytical expression. It is also shown that, when m_i = 1 and m_i + 1 = 2 or m_i = 2 and m_i + 1 = 1, the optimal one-wafer cyclic schedule can be obtained by finding h deterministic schedules and the one with the least cycle time. A novel analytical method is finally presented to schedule the overall system containing such reentrant wafer flow. This represents a significant advance in single-arm cluster equipment automation.

An intelligent logistics system is an important branch of intelligent transportation systems. It is a great challenge to develop efficient technologies and methodologies to improve its performance in meeting customer requirements while this is highly related to people's life quality. Its high efficiency can reduce food waste, improve food quality and safety, and enhance the competitiveness of food companies. In this paper, we investigate a new integrated planning problem for intelligent food logistics systems. Two objectives are considered: minimizing total production, inventory, and transportation cost and maximizing average food quality. For the problem, a bi-objective mixed integer linear programming model is formulated first. Then, a new method that combines an ε-constraint-based two-phase iterative heuristic and a fuzzy logic method is developed to solve it. Computational results on a case study and on 185 randomly generated instances with up to 100 retailers and 12 periods show the effectiveness and efficiency of the proposed method.