Scheduling analysis with resources share using the transitive matrix based on P-invariant

A Petri net with controller is used to model discrete events in flexible job shop scheduling, the objective of scheduling problems is to minimize make-span, the scheduling results is obtained based on genetic and Tabu Search (TS) algorithm. It is proved capable of providing optimized schedule to the job-shop where the machine tool and manpower resources are both constrained. After crossover and mutation operations, an optimal or suboptimal scheduling plan can be found. The result of the test shows that this method is feasible and efficient. Job shop scheduling is the primary content and critical technology of the production management of CIMS, in the past during 30 years, a lot of researchers and results focus on it, but most of them deal with single objective and resource scheduling problems which are fixed processing route and constrained by machine, and production period is its optimal objective. Nevertheless, the production styles are variable and processing route is determined under the special condition. Moreover, job shop scheduling problem is the most difficult combined constraints optimal problem and typical NP hard problem which can't be solved in polynomial time by an effective algorithm. Up to date, there are some traditional algorithms to solve JSP problem (1-2). About paper (3), Petri net is used to construct JSP model and L1 which combines branch and bound algorithm with dynamic program is used to search its optimal solution. And paper (4), the job sequence is assigned by genetic code firstly, and then filtered beam search which based on breadth-first algorithm is adopted to search. In recent years, the Petri net as a discrete event dynamic system modeling and analysis tool(5), has been successfully applied to the flexible manufacturing system modeling, analysis and control. Some researchers put forward to be based on Petri network scheduling method, but only on the production line itself can not reflect the modeling, as in the production line of external control scheduling strategy influence. A hybrid algorithm is proposed to solve dual-resource scheduling problem in flexible production environment, and Petri net model with a controller, which can effectively model control. It combines the advantage of global search ability of GA with the self-adaptive merit of Tabu Search (TS) and improves its convergence. It is proved capable of global and part searching ability and efficient.

Scheduling a cluster tool with wafer residency time constraints is challenging and important in wafer manufacturing. With a backward strategy, the scheduling problem of such singlerobot-arm cluster tools is well-studied in the literature. It is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the backward one. This work uses a timed Petri net (PN) to model the dynamic behavior of the system and presents a method to determine the optimal scheduling strategy for the system. Based on its PN model and the obtained strategy, it reveals that the key issue to schedule such a tool is to determine when and how long the robot should wait for. Based on this finding, this work establishes for the first time the necessary and sufficient conditions regarding the existence of an optimal and feasible one-wafer cyclic schedule for singlerobot-arm cluster tools. It then formulates a computationally efficient linear program to find it if existing, and finally gives industrial examples to show the application and power of the proposed method.

In this chapter, we deal with the cyclic scheduling problem usually observed in the FMS producing multi-type parts where the AGVS plays a role of a material handling system. Finding the conditions guaranteeing the AGVs deadlock-free and collision-free movement policy is the aim of this work. The AGVs co-sharing the common parts of the transportation route while executing repetitive processes, can be modeled in terms of Cyclic Concurrent Process Systems (CCPSs). The chapter suggests a novel approach for schedulability analysis employing the declarative modeling. In turn, the schedulability analysis for a given CCPS answers the question whether a cyclic schedule exists or not. A reference model of constraint satisfaction cyclic scheduling problem shows that unschedulability can be caused by a relation among an initial state and dispatching rules selected. The sufficient conditions guaranteeing CCPS schedulability are discussed and the recursive approach to their designing is proposed. Possible implementations are illustrated on example of the flexible manufacturing system operation.

We focus on the analysis of Petri net models using a subnet. We propose a divide and conquer method of Petri nets under the condition of one-boundedness for all the Petri nets. The usefulness of the approach is shown by applying the proposed techniques to an illustration model.

The use of shared resources is a major problem occurring in discrete event (DE) systems, including manufacturing systems, computer systems, communication systems, highway/vehicle systems, and others. The issues involve problems of assignment of resources when the same resources are simultaneously requested by more than one job. Failure to suitably assign, or dispatch, resources in such situations can have serious deleterious effects on system performance, resulting in extreme cases in system deadlock [7]. Computer operating systems engineers and industrial engineers have used the concepts of circular wait (CW) and circular blocking in analysis of such phenomena [41, 24]. Various concepts in Petri nets (PN) are extremely useful for dealing with deadlock and a great deal of research has been done from the PN point of view (see references, especially [11, 14, 19]). The PN approaches are often based on the notion of the siphon, which indeed is called a “deadlock” in some works [26]. Unfortunately, it is known that many problems in scheduling, dispatching, and deadlock analysis are NP-hard [12], so that significant increases in computing power do not significantly improve computational ability. Neither PN nor CW approaches have addressed this issue.

Approximate optimal periodic scheduling of multiple sensors with constraints

Converging to periodic schedules for cyclic scheduling problems with resources and deadlines

Some complexity results in cyclic scheduling

A modeling method for controller in FMS is presented. This method is based upon the timed automata, and model checker tool UPPAAL is used to model, simulate and verify the FMS model. In the previous work about FMS modeling, the behavior of FMS is more emphasized than the performance, or vice versa. Because the time is included into the modeling method, the scheduling and controlling problems in FMS can be integrated into this model. The performance of the system can be guaranteed while the system behavior is controlled. Upon this model, the optimal scheduling and optimal controlling can also be checked and verified.

This paper follows the discounted cash flow (DCF) approach to investigate inventory replenishment problem for EOQ model with defective items taking account of time value of money. First, this paper shows that the present value of the total variable cost for the infinite planning horizon is a convex function. Second, this paper derives bounds to determine the optimal cycle time under minimum value of the present value of the total variable cost for the infinite planning horizon. Third, with these bounds, a simple algorithm is derived to help the determination of the exact optimal cycle time and optimal order quantity. Finally, a numerical example is given to illustrate the algorithm discussed in this paper.

The problem of lot sizing and scheduling of production runs for multiple products to be processed on a machine was studied. The problem is formulated as a dynamic system, for which we show that cyclical schedules with the clearing lot sizing rule leads the system to a steady state. In the steady state we obtained a closed-form formula for the cycle time, the lot size and the maximum inventory level for each product. Finally, we showed that the clearing lot sizing rule is the optimal linear lot sizing rule that minimizes the system's inventory levels in the steady state.

Timed Petri nets (TPNs) have been widely used for modeling discrete-event systems of diverse manufacturing and service industries. In this article, we introduce a reachability tree-based optimization algorithm to optimize cyclic schedules of TPNs. In particular, we focus on a special class of cyclic schedules that are referred to as one-cyclic schedules, i.e., the algorithm efficiently finds the optimal one-cyclic transition firing schedule of a TPN. The proposed scheduling method can be robustly applied and extended to a number of different scheduling models since the methodology is not bounded to a specific domain. To enhance the computational performance, we establish a set of transition ordering constraints that can reduce the tree size during the search procedure. We evaluate the computational efficiency of the suggested algorithm by examining robotized manufacturing systems where one-cyclic schedules are popularly being used. It is numerically shown that the proposed algorithm is computationally more efficient than the previously studied Petri net-based optimization methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Resource scheduling is one of the most important managerial issues in diverse industrial systems. An optimal scheduling method for a certain industrial system is often locally developed by utilizing domain-specific operational properties. Although such domain-dependent knowledge can contribute to enhancing the computational efficiency of an optimization method, such an approach has a weak point that the method might not be applicable to scheduling problems of different industrial fields. Our motivation is to develop an algorithm for optimizing steady-state schedules that can be robustly applied for various types of discrete-event systems. The algorithm is developed on the basis of the Petri net modeling framework as it is widely being used for describing cyclic behaviors of diverse manufacturing systems, service systems, and social systems. It is experimentally shown that the proposed algorithm is computationally efficient compared with the existing cyclic scheduling methods.

Two efficient cyclic scheduling heuristics for re-entrant job shop environments were developed. Each heuristic generated an efficient and feasible cyclic production schedule for a job shop in which a single product was produced repetitively on a set of machines was to determine an efficient and feasible cyclic schedule which simultaneously minimized flow time and cycle time. The first heuristic considered a repetitive production re-entrant job shop with a predetermined sequence of operations on a single product with known processing times, set-up and material handling times. The second heuristic was a specialization of the first heuristic where the set-up for an operation could commence even while the preceding operation was in progress. These heuristics have been extensively tested and computational results are provided. Also, extensive analysis of worst-case and trade-offs between cycle time and flow time are provided. The results indicate that the proposed heuristics are robust and yield efficient and superior cyclic schedules with modest computational effort.

Optimal joint replenishment decisions for a central factory with multiple satellite factories

We deal with the optimal control problem for piecewise linear and hybrid systems by using a computational approach based on performance-driven reachability analysis. The idea consists of coupling a reach-set exploration algorithm, essentially based on a repetitive use of linear programming, to a quadratic programming solver which selectively drives the exploration. In particular, an upper bound on the optimal cost is continually updated during the procedure, and used as a criterion to discern non-optimal evolutions and to prevent their exploration. The result is an efficient strategy of branch-and-bound nature, which is especially attractive for solving long-horizon hybrid optimal control and scheduling problems.