Deadlock avoidance policies for automated manufacturing cells

Abstract

This chapter considers the problem of deadlock avoidance in flexibly automated manufacturing systems, one of the most prevalent supervisory control problems that challenges the effective deployment of these environments. The problem is addressed through the modeling abstraction of the (sequential) resource allocation system (RAS), and the pursued analysis uses concepts and results from the formal modeling framework of finite state automata (FSA). A notion of optimality is defined through the notion of maximal permissiveness, but the computation of the optimal DAP is shown to be NP-Hard. Hence, the last part of the chapter discusses some approaches that have been developed by the relevant research community in its effort to deal with this negative complexity result. IntroductionContemporary manufacturing is characterized by (i) an ever increasing emphasis on automation and by (ii) a quest for broader product lines and more customized product offerings [22].Indeed, since the beginning of the industrial revolution, automation has been at the core of the mass-production concept and practices, enabling activities and tasks that frequently transcend the human element in terms of capability, consistency, durability and speed [21]. More recently, automation has also been advocated as a key enabler for controlling the overall production cost, especially in economies where labor cost is particularly high. Hence, there is an increasing tendency to confine the presence of the human element on the production shop-floor to supervisory and maintenance tasks only, while the actual production activity is taken over by "intelligent" hardware, materialized in the form of numerically controlled (NC) machines, CHAPTER 6. 2 robots, and other material handling equipment. Clearly, enabling the reliable deployment of such an extensive mode of automation is a pretty challenging task. Yet, in the recent years, things have been further complicated, and the aforementioned challenges have been further exacerbated, by the second manufacturing trend mentioned above, i.e., the emphasis that is placed by the global markets upon choice and customization. This trend implies a need to support the production of a broader set of items, in smaller batch sizes, and with shorter life spans. Such demand and production patterns negate the economies of scale and the efficiencies of the dedicated production lines that have been sought by traditional manufacturing, and (re-)places the emphasis on concepts like resource sharing, flexibility, reconfigurability and concurrency [18]. But the current reality has also shown that the effective support of these concepts on the manufacturing shop-floor is highly non-trivial, and it adds an entirely new layer of complexity and challenges to the aforementioned endeavors towards the automated operation of production systems. The manufacturing world has become increasingly conscious of the fact that the successful design and deployment of flexibly automated production systems necessitates the development of a (ma...

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“…An additional issue to be addressed in our future research concerns the management of the computational complexity of the considered problems and their optimal solution. This complexity arises from the fact that the presented Mathematical Programming (MP) formulations involve the complete enumeration of the underlying system state space, which is known to explode extremely fast for the considered class of systems (c.f., [6]). Hence, in order for the results presented in this work, as well as their potential extensions to the broader scheduling problem, to be fully exploited, we need to develop alternative more concise problem characterizations and/or computationally efficient solution techniques that can lead fast to (near-)optimal solutions for the considered performance optimization problems.…”

“…The complement of S rs with respect to S r is denoted by S ru , and it constitutes the system reachable unsafe region; formally, S ru = S r \S rs . In the context of the considered RAS's, the optimal DAP, P * , is well-defined, and furthermore, it is effectively computable, even though its implementation constitutes an NP-Hard problem, in general [6]. Yet, to facilitate the subsequent discussion, in the following we shall assume that the deadlock avoidance strategy implements the optimal DAP, P * ; we notice, however, that the presented methodology can be applied to the comparison of any other sub-optimal DAP, P, to the alternative strategies of detection and recovery, and randomized deadlock avoidance, by substituting in the subsequent analysis the reachable safe subspace, S rs , by the policy admissible space, S r (P).…”

“…JT1: R1 -> R2 JT2: R2 -> R1 6 The STD modeling the system state space under the control of the three deadlock resolution strategies considered in this work is depicted in Figure 2. Specifically, the uncontrolled system behavior is modeled by the subgraph induced by the state subset S r = {s 0 , s 1 , s 2 , s 3 , s 4 , s 5 , s 6 , s 7 }.…”