# A Shift Sequence for Nurse Scheduling Using Linear Programming Problem

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1 IOSR Journal of Nursing and Health Science (IOSR-JNHS) e-issn: p- ISSN: Volume 3, Issue 6 Ver. I (Nov.-Dec. 2014), PP A Shift Sequence for Nurse Scheduling Using Linear Programming Problem Mr. B.Satheesh Kumar, Ms. G. Nagalakshmi, Dr. S. Kumaraguru Abstract: The Nurse scheduling problem (NSP) of this paper is to study and analyze the scheduling process in practice, and propose models and heuristics to improve both the process and the quality of the resulting schedule. Nurses should benefit from this study by having higher quality schedules while the employees in charge of scheduling should enjoy the positive benefits of an optimization tool to solve problem related to healthcare, which should guide their work and is certainly superior to suit and fallacy. The objective is to maximize the fairness of the schedule. This paper illustrates how the linear programming solves the nurses scheduling problem and how it has been effectively used in hospitals. A numerical illustration example of nurse scheduling for 8 hour shift is presented and the optimum solution is solved by Excel solver Keywords: Nurse scheduling (NSP), Linear Programming Problem (LPP), Constraints, Objective function. I. Introduction Nurse scheduling, or rostering, is the series of action of constructing a single whole work timetables for its staff so that an activity can satisfy the requirement for its worships. The deed of transferring the working shifts to nurse over a period for many days is a hard for utilizing the task. It involves constructing a schedule for each employee within an organization in order for a set of tasks to be fulfilled. In the domain of healthcare, this is particularly challenging because of the presence of a range of different staff requirements on different days and shifts. The objective in this paper is to conduct a practical study on the process of nurse scheduling in hospitals, then introduce a heuristic that can be easily implemented in these hospitals at no extra cost, and finally, use software to generate scheduling problem to solve is the determination of day schedules for nurses. Constraints are usually divided into two groups: hard and soft constraints, which vary significantly with respect to legal regulations and individual preferences, depending on individual institutions and countries. Hard constraints must be satisfied to obtain feasible solutions. Soft constraints are desirable but not obligatory, and thus can be violated. In real nurse rostering settings, we noticed that the problems are nearly always overconstrained. It is therefore quite common to express the quality of solutions in terms of soft constraint violations. All the feasible weekly shift patterns were pre-defined and associated with costs concerning preferences, requests, the number of successive days, etc. These shift patterns were then used to construct nurse rosters by employing different heuristic decoders within a genetic algorithm to schedule both shifts and patterns for the best permutations of nurses. The idea of permuting the nurses to be scheduled is similar to the method presented in this paper. The Mason and Smith introduced work stretch cost and work stretch transition in an Integer Programming model to define the cost of the day-on within and between the work stretches. Column generation was employed to decide on the content of the work stretch and to link them in constructing the schedules concerning other costs related to shifts. Where a concept called stint is introduced to define a feasible sequence of shifts on consecutive days. Schedules for nurses can then be constructed by using a series of stints. Millar and Kiragu, used the term stint to denote patterns, which were defined by a start date, a length, a cost and the shifts. Network programming was used where each node is a stint to construct either cyclic or non-cyclic rosters. In this work, the purpose of this article is suitable method for linear programming to solve an instance of the nurse scheduling problem met in real modern hospital, while seeking for the schedule that guarantees a high level of fairness between the nurses In Section 2, we present the problem formulation. The application area is described in Section 3 and literature review in Section 4. Section 5 shows the structures of LPP. Finally, we present nurse scheduling model and the conclusion in Section 6 &7 respectively. II. Problem formulation Nurse scheduling is a complex exercise with multiple and contradictory objectives: minimizing total costs while maximizing the nurses preferences and requests, and equally distributing workload between nurses. Work constraints imposed by collective agreements and unions as well as contracts have to be respected. Constraints in nurse scheduling relate to; requirement for each shift, that can be assigned to each particular 24 Page

3 assessed via simulation modeling, is considered.an attempt to develop a knowledge based system for generating weekly nurse rosters and then adjusting the rosters so as to react to daily changes in demand and staff availability is discussed.more recently, a mix of tabu search (TS) and integer program sub problems is used to generate weekly ward rosters while satisfying a complex set of shift rules, cost restriction, nurse grade, and employee preference constraints. A hybrid TS algorithm is used to obtain solutions within a reasonable timeframe for a commercial nurse rostering system. The algorithms incorporate various tabu and hybrid TS procedures within a genetic algorithm (GA). These algorithms are designed to overcome one of the basic problems associated with using heuristics for complex nurse rostering problems, namely that, as indicated by the authors, the quality of a solution is not necessarily a sum or combination of the partial solutions. A number of other aspects of health system rostering systems have been studied by different researchers. Models for developing rosters for nurses serving home care and regional clinics, in which travel between different locations is an important factor, have been developed. A queuing model is used to determine the staffing levels needed to handle call arrivals for inpatient, outpatient and other hospital generated appointments. Simulation modeling is used to consider operational management policies for providing maintenance staff in a large hospital. The use of a simple relational database system to manage work schedules for radiologists is discussed. IV. Literature review Literature on nurse rostering and scheduling is extensive. Several studies have employed optimization methods to solve the NSP, like linear, integer or mixed integer programming, goal programming or constraint programming. Many of more recent paper tackle the NSP with met heuristic methods such as genetic algorithms, tab search or simulation. We believe the resolution techniques involving the use of solvers are more easily transferable to hospital-services. Other approaches, like heuristics or meta- heuristics are less accessible, and could be time-consuming. Hence our contribution, related to existing approaches, is focused on the linear programming problem, which seeks to satisfy the demand coverage while minimizing the salary cost and maximizing the nurses preferences as well as team balance. Different objectives are studied in this literature are to decrease manual scheduling, to increase demand covering in terms of workforce size but also according to required skills, to obtain equity between the schedules. V. Structure of Linear Programming model. The general structure of the Linear Programming model essentially consists of three components. The activities (variables) and their relationships ii) The objective function and iii) The constraints The activities are represented by x 1, x 2, x 3.. x n. These are known as Decision variables. The objective function of an LPP (Linear Programming Problem) is a mathematical representation of the objective in terms a measurable quantity such as profit, cost, revenue, etc. Optimize (Maximize or Minimize) Z = c 1 x 1 + c 2 x 2 + c 3 x 3 + c n x n. Where Z is the measure of performance variable? c 1 x 1 + c 2 x 2 + c 3 x 3 + c n x n are the decision variables. And c 1, c 2, c 3,, c n are the parameters that give contribution to decision variables. The constraints these are the set of linear inequalities and/or equalities which impose restriction of the limited resources 5.1 Assumptions of Linear Programming Certainty. In all LP models it is assumed that, all the model parameters such as availability of resources, profit (or cost) contribution of a unit of decision variable and consumption of resources by a unit of decision variable must be known and constant. Divisibility (Continuity) The solution values of decision variables and resources are assumed to have either whole numbers (integers) or mixed numbers (integer or fractional). However, if only integer variables are desired, then Integer programming method may be employed. 26 Page

4 Additivity The value of the objective function for the given value of decision variables and the total sum of resources used, must be equal to the sum of the contributions (Profit or Cost) earned from each decision variable and sum of the resources used by each decision variable respectively. /The objective function is the direct sum of the individual contributions of the different variables Linearity All relationships in the LP model (i.e. in both objective function and constraints) must be linear. 5.2 General Mathematical Model of an LPP Optimize (Maximize or Minimize) Z = C 1 X 1 + C 2 X C n X n Subject to constraints, a 11 x 1 + a 12 x a 1n x n (<, =, >) b 1 a 21 x 1 + a 22 x a 2n x n (<, =, >) b 2 a 31 x 1 + a 32 x a 3n x n (<, =, >) b 3. a m1 x 1 + a m2 x a mn x n (<, =, >) b m and x 1, x 2,.. x n > Guidelines for formulating Linear Programming model Identify and define the decision variable of the problem Define the objective function State the constraints to which the objective function should be optimized (i.e. Maximization or Minimization) Add the non-negative constraints from the consideration that the negative values of the decision variables do not have any valid physical interpretation VI. Nurse scheduling problems Modeling Nurse scheduling is a known problem, Improving self-scheduling scheduling are of the familiar. Some of them will be describe in the technical way. The practical examples will be studied, modeled, and different ways of enlighten the present process will be published. In studied hospitals, the clerk goes through the pursuing steps to create a schedule: Gather preferences Block out the schedule Revise the schedule Display the schedule Accommodate the schedule Nurse Scheduling aim is to minimize changes to be original schedule while minimizing costs, rebuilding the schedule with current staff is usually be simple way,by changing the schedule will alter other nurse schedules as well. One of the Coimbatore city hospitals has the following minimal daily requirements for nurses. Shift(Period) Clock time (24hours day) Minimum number of nurses required a.m a.m a.m p.m p.m p.m p.m p.m p.m a.m a.m a.m. 40 Nurses report at the hospital at the beginning of each period and work for 8 consecutive hours. The hospital wants to determine the minimal number of nurses to be employed so that there will be a sufficient number of nurses available for each period. Formulate this as a linear programming problem by setting up appropriate constraints and objective function. i) Identify and define the decision variable of the problem Let x 1, x 2, x 3, x 4, x 5 and x 6 be the number of nurses joining duty at the beginning of periods 1, 2, 3, 4, 5 and 6 respectively. ii) Define the objective function 27 Page

5 Minimize Z = x 1 + x 2 + x 3 + x 4 + x 5 + x 6 iii) State the constraints to which the objective function should be optimized. The above objective function is subjected to following constraints. x 1 + x 2 70 x 2 + x x 3 + x x 4 + x 5 85 x 5 + x 6 25 x 6 + x 1 40 x 1, x 2, x 3, x 4, x 5, x 6 0 Since the model has 6 variables by using the solver to solve LPP, the feasible solution is Minimize Z=295 The solution found by the linear programming algorithm (Excel-shown below) uses the minimum number of 295 nurses to meet the schedule. VII. Conclusion In this paper shows an overview of the planning and nurse scheduling problem, this seeks the minimum number of nurses can handle the hospital needs. Although we have describe the constraints satisfaction system in terms of shift and piece of works. The aim of this problem is to maximizing the fairness of the schedule, while respectively all the constraints.nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over. In general it is efficiently utilize the time and effort, to balance the workload to lead more contented and effective. Reference [1]. Aickelin, U., K. Dowsland An indirect genetic algorithm for a nurse-scheduling problem. Comput. Oper. Res. 31(5) [2]. Azaiez, M. N., S. S. Al Sharif A 0-1 goal programming model for nurse scheduling. Comput. Oper. Res. 32(3) [3]. Bailey, R., K. Garner, M. Hobbs Using simulated annealing and genetic algorithms to solve staff scheduling problems. Asia-Pacific J. Oper. Res. 14(2) [4]. Berrada, I., J. Ferland, P. Michelon A multi-objective approach to nurse scheduling with both hard and soft constraints. Socio- Econom. Planning Sci. 30(3) [5]. De Grano, M.L, Medeiros, D.G and Eitel, D.(2009). Accommodating individual preferencesin nurse scheduling via auctions and optimization, Health Care Management Science,12: [6]. Ernst, A.T., Jiang, H., Krishnamoorthy, M. and Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, 153: 3 27 [7]. Jaumard, B., F. Semet, T. Vovor A generalized linear programming model for nurse scheduling. European Journal of Operational Research 107(1) [8]. Moz, M. and Pato, M. V. (2007). A genetic algorithm approach to a nurse rerostering problem, Computers and Operations Research 34(3): [9]. Okada, M An approach to the generalised nurse scheduling problem generation of a declarative program to represent institution-specific knowledge. Computers and Biomedical Research Page

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