COMPARISON OF DEADLOCK HANDLING STRATEGIES FOR DIFFERENT WAREHOUSE LAYOUTS WITH AN AGVS

Automated guided vehicles (AGVs) form a large and important part of logistic systems to improve productivity and reduce costs. When multiple AGVs are running in limited and uncertain environments, lots of issues can occur, such as collisions and deadlocks, which need to be addressed. This paper presents a flexible simulation model for a warehouse with various AGVs. We implemented all three typical strategies to handle deadlocks: prevention, avoidance and detection and resolution. The results show that there is no dominant strategy and that the results strongly depend on the individual case and the input parameters. 1 PROBLEM AND MOTIVATION The constant internationalization of the markets, stronger customer orientation, and ever shorter product life cycles put companies in almost all industries under increasing pressure (Obermaier 2019). The degree of automation and digitization is gradually growing in many areas to cope with the resulting needs and social developments such as geographical change (Schenk and Schumann 2015). Logistics, as a crosssectional function, is especially affected by this trend and is undergoing the process of automation. According to a current survey, more than 90 % of the companies surveyed from the logistics sector in Germany rate the use of robots and automation as "significant" or "very significant" (Statista 2019). An important part of this is the use of automated guided vehicle systems (AGVS), which automatically carry out transport orders with specified start and destination locations. About 55 % of the logistics experts surveyed in Germany rate the relevance of AGVS as "large" or "very large" (Statista 2018). When planning an AGVS, a wide variety of decisions have to be made: For instance, the layout, control logic, and number of vehicles required have to be determined. Many of these parameters cannot be reliably determined in a complex, stochastic environment by static calculations. Simulation models are a typical way to help out with these issues and are, therefore, created to take into account the complexity and dynamics of the system (Ullrich and Albrecht 2019). 1300 978-1-7281-9499-8/20/$31.00 ©2020 IEEE Müller, Ulrich, Reyes-Rubiano, Reggelin, and Lang Avoiding collisions and the correct handling of deadlocks is an elementary part of the smooth running of AGVS. A simulation model must also map the correct handling of deadlocks and collisions for a proper representation of the planned system. There are various strategies to handle deadlocks. The literature gives for logistical problems little knowledge-based advice about what might be the best strategy with respect to important figures in logistics. The aim of this publication is, therefore, to give a simulation-based comparison in which different logistical scenarios in a warehouse with an AGVS which deadlock strategy performs best regarding the key figures. The findings build on our paper from last year, where a rail-based storage and retrieval system handled the deadlocks with an approach of detection and resolution (Müller et al. 2019). In contrast to the 2019 publication, this publication now considers an AGVS instead of an ASRS, and we do not decide in advance for a strategy approach to deadlock handling, as is often the case in the literature, but consider all three strategy approaches under influence of various parameter changes. Furthermore, the resource types are limited to floor-bound sections of the path (no more lifts as different resource type) and the stacking restriction specified by the industrial laundry scenario does not occur with the general problem considered in this paper. Due to the different structure of the logistic system, the strategy approach of detect and resolve is also solved differently in this paper than in the publication of 2019. 2 LITERATURE There are different, problem-specific approaches to handle deadlocks. Most of these approaches do not come from logistics at all, but from computer science, where this problem has been recognized and discussed since the late 1960s and early 1970s (Coffman et al. 1971; Havender 1968; Holt 1972). Deadlocks occurred primarily in parallel programming or multi-programming, and general strategy approaches were developed to break the conditions for creating deadlocks. Since then, the four conditions by Coffman et al. have been used as starting points for dealing with deadlocks and are also used for current problems (Bashir et al. 2018; Coffman et al. 1971; Palmer et al. 2018; Zheng et al. 2020). In addition to the focus on breaking one of Coffman’s conditions for a deadlock, a distinction was made between three basic strategic approaches to handle deadlocks (Coffman et al. 1971):  prevention  avoidance  detection & resolution Both early observations by Coffman et al. and later views by, for example, Tanenbaum (2015) saw, from the perspective of computer science, in the strategic approach of deadlock prevention, above all how resources are allocated. They referred to suggestions by Havender (1968). This perspective was not always followed in production and logistics. Either prevention was not clearly delimited to avoidance (Mayer 2009; Mayer and Furmans 2010), or like Lehmann (2006) deadlock prevention meant the design of a logistical system, process, and infrastructure in such a way that no more deadlocks can occur. Ultimately, when designing the system, reference was often made only to the rules on resource use (Kim and Kim 1997; Lienert and Fottner 2017). The authors described this design process as forward-looking system planning, but what is ultimately meant is a corresponding change in the processes involved in order planning. Precisely, the condition of mutual exclusion according to Coffman et al. could also be attacked by skillful infrastructure planning. How an adapted infrastructure planning could prevent deadlocks has not yet been discussed in detail in the literature. In this context, this paper examines the interaction of infrastructure parameters and deadlocks. Another aspect that occurs frequently in the literature is the quick decision for a certain deadlock handling strategy approach. There are many solutions and algorithms provided in detail for a certain approach (Fanti et al. 2018; Qi et al. 2018; Zhao et al. 2020), but most of these publications only provide