A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure

Rostering from staffing levels: a branch-and-price approach

Many rostering methods first create shifts from some given staffing levels, and after that create rosters from the set of created shifts. Although such a method has some nice properties, it also has some bad ones. In this paper we outline a method that creates rosters directly from staffing levels. We use a Branch-and-Price (B\&P) method to solve this rostering problem and compare it to an ILP formulation of the subclass of rostering problems studied in this paper. The two methods perform almost equally well. Branch-and-Price, though, turns out to be a far more flexible approach to solve rostering problems. It is not too hard to extend the Branch-and-Price model with extra rostering constraints. However, for ILP this is much harder, if not impossible. Next to this, the Branch-and-Price method is more open to improvements and hence, combined with the larger flexibility, we consider it better suited to create rosters directly from staffing levels in practice

Project Controls and Management Systems : current practice and how it has changed over the past decade

Project Controls and Management System (PCMS) refers to an ecosystem of processes, tools and personnel required for the proper planning and execution of capital projects throughout the different phases of design, procurement, construction and startup. This can be divided into different focus areas (functions) that would include Estimating, Planning, Scheduling, Cost Control, Change Management, Progressing, and Forecasting. Various trends such as globalization, contractor specialization and information technology developments have impacted the way PCMS are implemented and made it the subject of extensive research over the past years to investigate how to best utilize those trends. Replicating the research methodology used in a 2011 report published by the Construction Research Institute (CII), this work aims to investigate the current status of PCMS implementation and how it has changed over the past decade. It was concluded that while the original PCMS principles are still valid, adoption has drastically changed in terms of efficiency for the majority of the functions. The research also identifies areas of potential concerns and provides recommendations for further improvement.Civil, Architectural, and Environmental Engineerin

Crew Scheduling for Netherlands Railways: "destination: customer"

: In this paper we describe the use of a set covering model with additional constraints for scheduling train drivers and conductors for the Dutch railway operator NS Reizigers. The schedules were generated according to new rules originating from the project "Destination: Customer" ("Bestemming: Klant" in Dutch). This project is carried out by NS Reizigers in order to increase the quality and the punctuality of its train services. With respect to the scheduling of drivers and conductors, this project involves the generation of efficient and acceptable duties with a high robustness against the transfer of delays of trains. A key issue for the acceptability of the duties is the included amount of variation per duty. The applied set covering model is solved by dynamic column generation techniques, Lagrangean relaxation and powerful heuristics. The model and the solution techniques are part of the TURNI system, which is currently used by NS Reizigers for carrying out several analyses concerning the required capacities of the depots. The latter are strongly influenced by the new rules.crew scheduling;dynamic column generation;lagrange relaxation;railways;set covering model

Public Policy Platform on Flexible Work Arrangements

On May 13, 2009, Workplace Flexibility 2010 released a comprehensive set of policy solutions to expand Americans’ access to flexible work arrangements such as compressed workweeks, predictable schedules, and telecommuting. Flexible Work Arrangements (FWAs) alter the time and/or place that work is conducted on a regular basis - in a manner that is as manageable and predictable as possible for both employees and employers. FWAs provide: Flexibility in the scheduling of hours worked, such as alternative work schedules (e.g., non-traditional start and end times, flex time, or compressed workweeks) and arrangements regarding overtime, predictable scheduling, and shift and break schedules; Flexibility in the amount of hours worked, such as part time work, job shares, phased retirement or part year work; and Flexibility in the place of work, such as working at home, at a satellite location or at different locations

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers