Evaluating Smartphones for Infrastructure Work Order Management

Infrastructure managers require timely and accurate state information to diagnose, prioritize, and repair the substantial infrastructure assets supporting modern society. Challenges in obtaining sufficient information can often be attributed to inadequate data collection procedures (phone calls, paper reports, etc.) or a general lack of knowledge or ability on the part of the reporting individual to accurately convey what is actually wrong with the facility. Fortunately, modern smart-phone technology offers the potential to improve maintenance work requests by providing better geolocation and problem description accuracy. An experiment simulating real-world maintenance requests was conducted comparing smart-phones with traditional verbal work order request systems. Usefulness and description accuracy ratios revealed smartphone systems generated more useful information regardless of submitter background or experience. However, interestingly the smart-phone applications did not improve asset geolocation and actually negatively impacted the ability of maintenance personnel to accurately relocate the asset needing service. Given the ubiquitous nature of smartphone technology, the potential exists to turn any citizen into an infrastructure sensor. This study takes a step toward understanding the benefits, as well as the limitations, of the smart-phone based work order submission systems

Real-World Applications Infrastructure Work Order Planning Using Genetic Algorithms

Infrastructure management offices plan and complete several thousand small construction projects annually. Effective planning is vital if the public and private sectors are to maintain valuable infrastructure investments at the least cost to the taxpayer or shareholder. This paper presents the results an application of Genetic Algorithms (GA) in multi-project resource allocation to minimize the total cost of work order execution on realistically sized infrastructure management problems. In addition to direct crew costs indirect costs for set-up, idle time, and travel are included in this model. Results of test cases demonstrate the effectiveness of the approach when compared to several standard heuristics. 1