Resilience markers for safer systems and organisations

If computer systems are to be designed to foster resilient performance it is important to be able to identify contributors to resilience. The emerging practice of Resilience Engineering has identified that people are still a primary source of resilience, and that the design of distributed systems should provide ways of helping people and organisations to cope with complexity. Although resilience has been identified as a desired property, researchers and practitioners do not have a clear understanding of what manifestations of resilience look like. This paper discusses some examples of strategies that people can adopt that improve the resilience of a system. Critically, analysis reveals that the generation of these strategies is only possible if the system facilitates them. As an example, this paper discusses practices, such as reflection, that are known to encourage resilient behavior in people. Reflection allows systems to better prepare for oncoming demands. We show that contributors to the practice of reflection manifest themselves at different levels of abstraction: from individual strategies to practices in, for example, control room environments. The analysis of interaction at these levels enables resilient properties of a system to be ‘seen’, so that systems can be designed to explicitly support them. We then present an analysis of resilience at an organisational level within the nuclear domain. This highlights some of the challenges facing the Resilience Engineering approach and the need for using a collective language to articulate knowledge of resilient practices across domains

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level

Urban Air Mobility Fleet Manager Gap Analysis and System Design

NASA's Urban Air Mobility (UAM) Sub-Project is engaged in research to support the introduction of air taxis into the National Airspace System. Such operations will require a range of communication, navigation, and surveillance systems. Air vehicles for UAM are under development and will initially have human pilots. Separation from other aircraft, obstacles, and weather may be a pilot responsibility or provided by an operator's ground-based systems. Eventually, air taxis may be flown from the ground or fly autonomously. There will be a need for dispatch services for UAM. This report presents a gap analysis, data and capability requirements, and workstation design concepts for the UAM dispatcher or Fleet Manager (FM) position

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Design project 1968/9: management report

1. INTRODUCTION The design of an automatic assembly machine with versatility in application was undertaken as a group project by post-graduate students attending a course in production technology. This report summarises the work clone and conclusions reached during the project. In addition there are available five other reports which describe the designing of different areas of the machine in full detail (refs. 1 to 6). There is also the report of a technical survey which was carried out to investigate industrial requirements for automatic assembly. In order that this report may serve as a guide, a summary of the content of each of the other reports is included

Computational tasks in robotics and factory automation

The design of Manufacturing Planning and Control Systems (MPCSs) — systems that negotiate with Customers and Suppliers to exchange products in return for money in order to generate profit, is discussed.\ud \ud The computational task of MPCS components are systematically specified as a starting point for the development of computational engines, as computer systems and programs, that execute the specified computation. Key issues are the overwhelming complexity and frequently changing application of MPCSs

Adaptive signal control using approximate dynamic programming

This paper presents a concise summary of a study on adaptive traffic signal controller for real time operation. The adaptive controller is designed to achieve three operational objectives: first, the controller adopts a dual control principle to achieve a balanced influence between immediate cost and long-term cost in operation; second, controller switches signals without referring to a preset plan and is acyclic; third, controller adjusts its parameters online to adapt new environment. Not all of these features are available in existing operational controllers. Although dynamic programming (DP) is the only exact solution for achieving the operational objectives, it is usually impractical for real time operation because of demand in computation and information. To circumvent the difficulties, we use approximate dynamic programming (ADP) in conjunction with online learning techniques. This approach can substantially reduce computational burden by replacing the exact value function of DP with a continuous linear approximation function, which is then updated progressively by online learning techniques. Two online learning techniques, which are reinforcement learning and monotonicity approximation respectively, are investigated. We find in computer simulation that the ADP controller leads to substantial savings in vehicle delays in comparison with optimised fixed-time plans. The implications of this study to traffic control are: the ADP controller meet all of the three operational objectives with competitive results, and can be readily implemented for operations at both isolated intersection and traffic networks; the ADP algorithm is computationally efficient, and the ADP controller is an evolving system that requires minimum human intervention; the ADP technique offers a flexible theoretical framework in which a range of functional forms and learning techniques can be further studied

Scheduling vehicles in automated transportation systems : algorithms and case study

One of the major planning issues in large scale automated transportation systems is so-called empty vehicle management, the timely supply of vehicles to terminals in order to reduce cargo waiting times. Motivated by a Dutch pilot project on an underground cargo transportation system using Automated Guided Vehicles CAGV s), we developed several rules and algorithms for empty vehicle management, varying from trivial First-Come, First-Served (FCFS) via look-ahead rules to integral planning. For our application, we focus on attaining customer service levels in the presence of varying order priorities, taking into account resource capacities and the relation to other planning decisions, such as terminal management We show how the various rules are embedded in a framework for logistics control of automated transportation networks. Using simulation, the planning options are evaluated on their performance in terms of customer service levels, AGV requirements and empty travel distances. Based on our experiments, we conclude that look-ahead rules have significant advantages above FCFS. A more advanced so-called serial scheduling method outperforms the look-ahead rules if the peak demand quickly moves amongst routes in the system

Store capacity optimisation

The problem is one of increasing the efficiency of distributing paper rolls from the manufacturing plants to the customers. A related problem is one of utilising the available capacity at the customer stores in an effective manner. During the MISG, several approaches to the above problems were proposed. In this report we describe the problem and several methods for solving it. Preliminary results are provided for some of these

An Agent-Based Approach to Self-Organized Production

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively ("socially"), the blockings in the system are reduced. A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which gives conclusions about the consequencies for the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c