An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior

Industrial safety has been deeply improved in the past years, thanks to increasingly sophisticated technologies. Nevertheless, 2.3 million people yearly die worldwide due to occupational illnesses and accidents at work. Human factors can be profitably used for safety improvement because of their influence on the workers’ behavior. This paper presents an integrated optimization system to help companies assign each task to the most suitable worker, minimizing cost, while maximizing expertise and safety. The system is made of three modules. A neural module computes each worker’s caution for every task on the basis of some human factors and the worker’s behavior. To solve the multiobjective job assignment problem, an evolutionary module approximates the Pareto front through the nondominated sorting genetic algorithm II. Pareto-optimal solutions then form the alternatives of a multicriteria decision-making problem, and the best is selected by a decision module jointly based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution. Validation was carried out involving two footwear companies, where personnel was recruited and reassigned to tasks, respectively. Comparing the worker-task assignment proposed by the system to the one suggested/used by the management, noteworthy low-cost improvement in safety is shown in both scenarios, with low or no decrease in expertise. The proposed system can, thus, contribute to get safer workplaces where risks are less likely and/or less harmful

2015 Annual Report Transportation Research Center for Livable Communities

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Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Specification of vertical semantic consistency rules of UML class diagram refinement using logical approach

Unified Modelling Language (UML) is the most popular modelling language use for software design in software development industries with a class diagram being the most frequently use diagram. Despite the popularity of UML, it is being affected by inconsistency problems of its diagrams at the same or different abstraction levels. Inconsistency in UML is mostly caused by existence of various views on the same system and sometimes leads to potentially conflicting system specifications. In general, syntactic consistency can be automatically checked and therefore is supported by current UML Computer-aided Software Engineering (CASE) tools. Semantic consistency problems, unlike syntactic consistency problems, there exists no specific method for specifying semantic consistency rules and constraints. Therefore, this research has specified twenty-four abstraction rules of class‟s relation semantic among any three related classes of a refined class diagram to semantically equivalent relations of two of the classes using a logical approach. This research has also formalized three vertical semantic consistency rules of a class diagram refinement identified by previous researchers using a logical approach and a set of formalized abstraction rules. The results were successfully evaluated using hotel management system and passenger list system case studies and were found to be reliable and efficient

Artificial bee colony optimization to reallocate personnel to tasks improving workplace safety

Worldwide, just under 5,800 people go to work every day and do not return because they die on the job. The groundbreaking Industry 4.0 paradigm includes innovative approaches to improve the safety in the workplace, but Small and Medium Enterprises (SMEs)—which represent 99% of the companies in the EU—are often unprepared to the high costs for safety. A cost-effective way to improve the level of safety in SMEs may be to just reassign employees to tasks, and assign hazardous tasks to the more cautious employees. This paper presents a multi-objective approach to reallocate the personnel of a company to the tasks in order to maximize the workplace safety, while minimizing the cost, and the time to learn the new tasks assigned. Pareto-optimal reallocations are first generated using the Non-dominated Sorting artificial Bee Colony (NSBC) algorithm, and the best one is then selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach was tested in two SMEs with 11 and 25 employees, respectively

A Planning Pipeline for Large Multi-Agent Missions

In complex multi-agent applications, human operators are often tasked with planning and managing large heterogeneous teams of humans and autonomous vehicles. Although the use of these autonomous vehicles broadens the scope of meaningful applications, many of their systems remain unintuitive and difficult to master for human operators whose expertise lies in the application domain and not at the platform level. Current research focuses on the development of individual capabilities necessary to plan multi-agent missions of this scope, placing little emphasis on the integration of these components in to a full pipeline. The work presented in this paper presents a complete and user-agnostic planning pipeline for large multiagent missions known as the HOLII GRAILLE. The system takes a holistic approach to mission planning by integrating capabilities in human machine interaction, flight path generation, and validation and verification. Components modules of the pipeline are explored on an individual level, as well as their integration into a whole system. Lastly, implications for future mission planning are discussed