Why is Nurse Rostering important?
Nurse scheduling is a top priority in the effective, efficient management of any hospital or other health care facility. And, for many administrators and supervisors, it is one of the greatest challenges.
Building a schedule produces a common dilemma: lots of variables! There are time-off requests, shift swaps, special assignments, training schedules, overlapping shifts, and more. How do you keep track of it?
Nurse rostering software, ideal for 24/7 operations: hospitals, clinics, surgical facilities, nursing homes, and more. With its user-friendly design, even those inexperienced with computers will find it easy to use. Medical scheduling tasks can be performed with a simple point-and-click of your mouse.
With NRPS, medical scheduling is guaranteed to be:
Fast & Easy. A single, easy-to-learn interface makes NRPS user-friendly - even those inexperienced with computers will find it easy to use. With this nurse scheduler, staffing tasks can be performed with a simple point-and-click of your mouse. To get started, add your nurses and staff, and then define your shifts. You'll be up and running in minutes, and experiencing the timesavings this health care scheduling software can deliver.
Flexible. NRPS is packed with features giving it the flexibility to meet the medical scheduling needs of any medical facility. NRPS users range from five-person departments to hospitals that staff multiple locations. You can customize NRPS to suit any shift-based, 24/7 operation. You also have the ability to view, print, publish, or e-mail reports and schedules in about any format you can imagine. Just tell NRPS what you want to see. To get the ultimate in scheduling flexibility, ABS also offers an online scheduling system. By leveraging the Internet, it gives you access to work schedules and duty rosters anytime, anywhere, from any Internet connection.
Here we describe the nurse rostering problem in the Ophthalmology ward of the Queen’s Medical Centre University Hospital NHS Trust (QMC). The QMC ward consists of between 20 and 30 nurses and cover is required on a 24 hour basis. Typically, rosters are produced over periods of 28 days and are described by the months in which they fall.
urses in QMC are employed on a full time or part time basis. Based on the nature of the contract, full time nurses are required to work at most 75 hours per fortnight while part time nurses work regarding the agreement on their contract. Normally, it is indicated as the number of hours per week for part time nurses.
Nurses in the QMC ward belong to one of four possible qualification categories. These are, in descending order of seniority: registered (RN), enrolled (EN) auxiliary (AN) and student (SN). RNs and ENs are classified as qualified (QN) while QNs and ANs are both employed (PN). Qualified nurses, QNs, can receive additional training specific to the ward that they work in. In the ophthalmological ward, these nurses receive eye-training (ET).
In QMC, there are three types of shifts: early shift, late shift and night shift. Early shift is from 07:00 to 14:45, counts for seven and a half hours (7.5 hours). Late shift is from 13:30 to 21:15, counts for seven and a half hours (7.5 hours). Night shift is from 21:00 to 07:15, counts for ten hours (10 hours).
The cover requirements of nurses with different qualifications and specific training are as follows:
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There are also additional constraints need to be taken into account when constructing rosters. These are as follows:
· MaxHours: The maximum number of hours any nurse may work in a fortnight is 75 for full time nurses and for part time it is the agreement in their contract.
· MaxDaysOn: The maximum number of consecutive shifts for nurses of any type is 6.
· MinDaysOn: The minimum number of consecutive shifts for full time nurses is 2
· Succession: An EARLY shift must not follow a NIGHT shift.
· SingleNight: Nurses should not work only one night shift.
· WeekendBalance: Nurses should work no more than 3 weekends out of every 4.
· WeekendSplit: Nurses should work either both days of a weekend or not at all.
· HardRequest: regarding Team & Individual Preferences, Annual Leave must be satisfied.
· SoftRequest: Requests, except “
Of all 20 above constraints (11 cover constraints and 9 additional constraints), the followings are hard constraints: MaxHours, MaxDaysOn, Succession, HardRequest, MinDaysOn. The remaining constraints are soft constraints.
Occasionally, in the individual preference, nurses indicate the starting and ending time which they prefer to work rather than specific shift. In this case, this unusual shift can be considered as one of the normal shift for which most of the unusual shift belongs to. For example, an unusual shift 9-5 is considered as Early shift.
Why is Rostering important?
· Better schedules
· Reduce costs
· Decrease risks
· Improve working conditions
· Increase quality
Better scheduling with software tools
· Overcomes the limits of manual scheduling
· There are more and better schedules to chose from
· Decisions are transparent
· Scheduling is done faster
· Better analysis possible
The problem of allocating working patterns to employees is both difficult and time consuming. Personnel managers often spend a large percentage of their time con-structing rosters and in most cases do not succeed in producing rosters which satisfy both operational requirements and staff preference. Poorly constructed rosters can have a number of detrimental effects on an organisation’s performance. Overstaffing leads to high employment costs but is often necessary for organisations with unpredictable service demands. Conversely, understaffing can result in lost revenues or poor service provision. Rosters which are perceived by employees to be inflexible or unfair can significantly affect staff morale and lead to increased absenteeism and staff turnover.
Rostering problems are found in many different types of organisations and in dustries, including manufacturing, call centres, schools, emergency services, energy producers, and transportation. This thesis will focus on the health-care industry, and in particular on nurse rostering. Nurse rostering provides some of the most highly constrained and difficult personnel rostering problems. Hospitals must balance staff shortages and budget restrictions with consistent and high quality patient care. In recent years, a recognition of the importance of flexible employment practices has increased the need for automated rostering tools which can cope with ever more complex problems.
Personnel rostering is defined as the problem of placing resources into slots in a pattern, subject to given constraints, where the pattern denotes a set of legal shifts defined in terms of work to be done. However, the constraints imposed on the problem cannot often be satisfied completely, and the aim is to find the solu-tion which violates as few as possible. Real-world personnel rostering problems are highly constrained resource allocation problems which are difficult to solve manually . Conflicting legal, management, and staff requirements must be considered when making rostering decisions. For example, management requirements for the cover and skill mix needed for a particular task often conflict with the maximum working hours allowed (by law and contract) as well as with individual staff preferences. In general, it is difficult, if not impossible, to predict how an attempt to satisfy one constraint will affect the others.
Personnel rostering problems have been a subject of interest within both artificial intelligence and operational research communities for a number of decades. Traditional operational research optimisation methods, especially mathematical programming, were initially employed to solve simple problems with few constraints. However, these were rarely useful for real-world problems, particularly as they frequently incurred computational cost exponential in the number of employees considered. Many mathematical formulations of personnel rostering problems are NP-hard at the time of writing no algorithm is known which will solve such problems in polynomial time .
Consequently, many optimisation techniques from the artificial intelligence com-munity have been explored for finding good quality (but not provably optimal) solutions. Rostering problems are often represented as constraint optimisation problems, where the objective is to minimise the overall violation of constraints. Many approaches have been developed using constraint satisfaction methods or meta-heuristic techniques such as tabu search, simulated annealing, and evolutionary algorithms. In general, they measure roster quality using objective functions which represent the vi-olation
of constraints in rosters, usually weighted according to the perceived relative
importance of each constraint type.
From a mathematical modelling perspective personnel rostering problems are difficult for a number of reasons:
- Combinatorial solution space: the number of possible solutions to rostering problems usually increases exponentially with the number of staff, shift types, and with the size of the planning horizon . Consequently, brute-force methods quickly become computationally intractable for all but the smallest of problems.
- highly constrained: operational requirements, complex employment laws, and staff requests all translate into a large number of constraints. For most problems it is impossible to satisfy all constraints and so solutions must be found which meet a core set of strict requirements, and otherwise violate as few of the remaining constraints as possible.
- organisation-dependent: the definitions of staff skill, shift types, planning periods, and, of course, of the myriad constraints vary considerably both between and within organisations. Methods suitable for one problem are not easily trans-ferred to other problems and frequently the unsatisfactory approach of ‘changing the problem to suit the method’ is employed, with poor results. The problem modelling process also suffers from the knowledge acquisition bottleneck due to the difficulties present in translating operational information into mathematical models.
- subjective: also linked to problems of knowledge acquisition is the issue of subjectivity. Many of the decisions made by rostering experts (for example nurse managers) are of a personal, subjective nature, and are therefore difficult to model systematically. This is particularly true when considering the treatment of staff requests and preferences, which may need to be treated on a case by case basis.
Personnel scheduling refers to the problem of the assignment of employees (resources) to shifts and duties (objects) over a scheduling period (typically a week/fortnight/month) so that certain constraints (organisational and personal) are satisfied or at least nearly satisfied. It involves construction of a schedule for each of the employees within an organisation in order for a set of tasks to be accomplished. Individual employees are characterized by their qualifications, responsibilities and preferences.
The organization itself imposes a set of constraints that reflect organisational requirements. Personnel scheduling is a special type of constraint satisfaction problems where variables represent various tasks to be performed by the employees (What is to be done?). Variables are to be assigned a value from the corresponding domain, that is? a finite set of employees qualified for the task (Who can do what?). Individual preferences and global constraints restrict the way in which these assignments are combined (Who will do what?). We are usually interested in finding a (near-)optimal solution with respect to an objective function. In this case, personnel scheduling is viewed as an optimisation problem in which the objective is to find a solution which optimizes (that is ? minimizes or maximizes) an objective function involving certain variables of the problem while satisfying the given set of constraints. Personnel rostering problem of particular interest to is nurse rostering.
· Port space allocation with a time dimension
· A multi-mode resource-constrained scheduling problem in the context of port operations
We are capable to discuss problem of attending ships within agreed time limits at a port under the condition of the first come first served order. The objective of the research is creating the new tool to support decisions at a strategic level with the objective of improving the attendance of the ships. The tool is based on a mathematical model of a multi-mode resource-constrained scheduling problem and on an extension of a differential evolution algorithm. We can illustrate the implementation by computational tests with data generated on the basis of the characteristics of a real port environment.
· Advanced Heuristics in Transportation and Logistics
You’re required to plan the routes of your company’s 250 delivery trucks from 12 depots to the city’s 5,000 vendor locations. Traffic patterns, accidents and breakdowns, vendor-specified delivery time windows, perishable goods, returned empty packages, and product shortages at specific depots further complicate the picture. And, by the way, yesterday’s plan is no longer applicable because heavy overnight snowfall has invalidated your estimations of the trucks’speeds; you need a new plan by daybreak, in two hours. Where do you start? In today’s global transportation and logistics environment, industries require computational and simulation-based methods that will lead to faster transactions, reduced operating costs, and improved performance and customer service. They also want methods and tools that provide more control and flexibility in their operations, such as production and location planning, warehousing, distribution, and transportation. Transportation and logistics organizations often face large-scale combinatorial problems on both operational and strategic levels. In such problems, you must examine all possible combinations of decisions and variables to find a solution; consequently, no partial- enumeration-based exact algorithm can consistently solve them. This occurs because sharp lower bounds on the objective value are hard to derive, causing a slow convergence rate.
The main aim of our research is to investigate novel methods of handling various educational timetabling problems. Our research direction is to explore approaches that can operate at a higher level of generality than what the current timetabling system can support. Educational Timetabling Problems include the timetabling or scheduling of exams, courses, rooms and project presentations. Closely related problems are School Timetabling, Personnel Scheduling, and University Space Allocation.
Efficient resource planning, scheduling and allocation are essential for transportation operators who want to improve service quality and increase reactivity, particularly when cost is a primary factor. Operators need to assign the right resources to the right place at the right time, generating savings while ensuring that feasible schedules meet policies and regulations.
When the number of resources, regulations and polices increase, and costs become extremely high, it is impossible to efficiently schedule and allocate resources manually. Planning and scheduling (P&S) applications built with ILOG technology help operators to be more reactive, minimizing operational costs and guaranteeing legal, feasible schedules, from long-term strategic planning to short-term tactical operations.
The Office Space Allocation Problem
The classes of Space Allocation or Capacity Allocation problems are those in which the amount of space (area or volume) or capacity that is available has to be distributed among a set of items, satisfying specific requirements and constraints. Examples of this class of problems are: bin packing problem, knapsack problem, space planning and others.
Here, Office Space Allocation refers to the distribution of the available areas of office space among a number of entities with different sizes so as to ensure the optimal space utilisation and the satisfaction of additional requirements and/or constraints. In this generic case, an important condition exists: the areas of space that can be used and the space required by the entities are not subject to modification. Then the quality of a solution or allocation is measured in terms of the following five aspects (not necessarily in this order of importance):
· the number of entities than have been allocated.
· space utilisation, i.e the amount of space that is wasted (space not used) and the amount of space that is overused (entities with less space allocated than needed).
· satisfaction of any additional requirements.
· satisfaction or no violation of constraints.
The ideal solution in the space allocation problem is one where all the entities are allocated, no space is wasted or overused and every additional requirements and constraints have been satisfied. However, not always this ideal optimal solution is achievable. In a more realistic scenario, the optimal solution would be one where all objects are scheduled and the space utilisation is the best possible, i.e. the amount of space wasted and overused has been reduced to the minimum and the additional requirements and constraints have been all satisfied.
Some examples of constraints (specific restrictions hat should or must be fulfilled) are listed below, but different constraints may exist in different scenarios. Constraints can be classified as hard or soft. Hard constraints are those that cannot be violated while soft constraints are those that can be broken but penalised. To minimise the penalties in a solution for an Office Space Allocation problem, no hard constraints should be violated and as many as possible soft constraints should be satisfied.
· Wastage. Restricts the amount of space that can be not used in each area.
· Overuse. Refers to the amount of space that can be overused, i.e. the difference between the space available and the space needed for the allocated entities.
· Unallocated. Refers to those entities that are not allocated in the solution.
· Sharing. Restricts sharing a common area between two or more entities, i.e. refers to entities that must not be allocated in the same office space.
· Be located in. Restricts the allocation of a particular entity to the indicated preferred area.
· Be adjacent to. Refers to the situation in which some entities have to be allocated in adjacent areas.
· Be away of. Specifies that some entities have to be allocated away of a certain entities or areas.
· Be together with. Refers to the situation in which some entities have to be allocated in the same location.
· Be grouped with. Refers to the situation in which some entities have to be grouped, i.e. the entities will be in nearby areas.
· Not overused. Specifies that certain areas cannot be overused.
· Disturbance. Restrict the amount of changes when reorganising an existing solution.
Most of the real instances of the Office Space Allocation problem can be classified as one of the following types:
Reorganisation of the existing allocation. Refers to the rearrangement of the current space distribution among the entities and it is performed when either it is required to improve the existing solution under the existing conditions or it is required to modify the allocation because the conditions (requirements, constraints, number of entities to be allocated, number of areas of space) of the problem change.
Construction of a complete solution. The construction of a complete allocation or solution refers to the generation of a new solution from scratch to distribute all the available areas of space among all the entities in the problem under the given conditions.
When reorganising an existing allocation, it may be required to minimise the amount of disruption caused, i.e. relocation of entities. This constraint exists because it may be too costly to move every entity around and this often impedes finding the optimal utilisation of space. When reorganising an allocation, the amount of disruption permitted establishes a balance between the quality of the allocation and the difficulty in achieving it.
Investigate on a wide range of complex, uncertain and dynamic real-world scheduling problems. Develop fuzzy multi-criteria approaches to produce high quality solutions for scheduling and rescheduling problems in uncertain environments. Develop efficient and effective method based on advanced heuristic optimization techniques and multi-agent techniques for dynamic scheduling problems that emerge in flexible manufacturing systems, steel production and printed circuit board (PCB) assembly.Global competition and rapid changing customer requirement are making efficient production scheduling increasingly more important than ever for modern production/manufacturing organizations. Production scheduling concerns the scheduling of jobs and the control of their flow through a production process, which is described by various temporal relations, resource requirements and capacity constraints. Increasingly, traditional scheduling algorithms and models based on the operation research techniques that usually assume ideal situations are being found insufficient flexible in practice because underlying almost all real production systems are fraught with uncertainties and dynamics. In attempt to bridge the existing gap between the scheduling theory and practice, fuzzy scheduling models that utilize multi-criteria approaches have recently attracted increased interest among the scheduling research society. Imprecise scheduling parameters, such as processing times and due dates, and flexible constraints on resource capacity and relations have been represented using fuzzy sets. Multiple usually conflicting and incommensurable scheduling criteria have also been considered. Since the real-life internal and external business environment may change dynamically, there is a need to respond to the unexpected disturbances and to modify the existing schedules. To successfully deal with the uncertain and dynamic scheduling problems, a multi-disciplinary research that involves three themes, namely fuzzy reasoning, multi-criteria decision making and rescheduling in uncertain environments, will be fully investigated.
Basic features expected of job scheduler software are:
- Interfaces to define workflows and/or job dependencies
- Automatic submission of executions
- Interfaces to monitor the executions
- Priorities and/or queues to control the execution order of unrelated jobs
If software from a completely different area includes all or some of those features, this software is considered to have job scheduling capabilities.
Most operating system platforms such as Unix and Windows provide basic job scheduling capabilities, for example Cron. Many programs such as DBMS, backup, ERPs, and BPM also include relevant job scheduling capabilities. Operating system (OS) or point program supplied job scheduling will not usually provide the ability to schedule beyond a single OS instance or outside the remit of the specific program. Organizations needing to automate highly complex related and un-related IT workload will also be expecting more advanced features from a job scheduler, such as:
- Real-time scheduling based on external, un-predictable events
- Automatic restart and recovery in event of failures
- Alerting and notification to operations personnel
- Generation of incident reports
- Audit trails for regulatory compliance purposes
These advanced capabilities can be written by in-house developers but are more often provided by solutions from suppliers that specialize in systems management software.