Workforce scheduling model

Workforce Scheduling Model
Student’s Name:
Institutional Affiliation:
Chapter 4
Case Study and Model Formulation
This chapter focuses on a case study of a power plant experiencing workforce problems of operators taking excess overtime hours. The extra hours require the plant to incur extra expenditure and add to overall production costs lowering the profit margin of the plant, aside from generating problems during regular scheduling of shifts. The solution to this problem is to formulate a mathematical model to generate the optimal workforce scheduling plan to reduce the demand for the excess overtime hours. An optimal workforce scheduling plan reduces the pressure on workers and allows the plant to operate at maximum efficiency at the lowest cost without compromising on quality. Work at the power plant involves a lot of planned and unplanned maintenance operations that must be processed daily to ensure the smooth running of the facility. The maintenance management team has raised complaints that the current workforce is not sufficient to handle the daily workload including unplanned maintenance operations and hence there is the need for smooth shifts and more personnel during official working days.
The plant has five official working days from Sunday to Thursday, and any work is done outside this is billed as overtime. The demands for maintenance are extended to include weekends and holidays. The management has expressed concern about the excessive overtime hours that are being taken daily and on the weekend.

Although the workforce is not sufficient as declared by the maintenance team, the management tries to optimize the number of employees available since they believe that there is a problem in resource planning and scheduling. The chapter consists of two parts; the first part entails a study and analysis of the excessive overtime requests, to discover its causes and the effect it has on an organization. The second part proposes a model mathematical formulation to tackle this problem with an improved and optimal working schedule for implementation.
Case Study
Workforce management is a prerequisite in the running of an efficient organization that maximizes output with reduced input requirements. It entails steps and procedures taken by organizations to balance resources at hand in order to complete work engagements. This is referred to as workforce scheduling, which is a process involving drafting of work timetables for staff within organizations. Workforce scheduling is an increasingly complex task as the labor market expands to include shifts, greater flexibility in working hours, part-time jobs and working several jobs. Not to mention the various considerations required when computing an efficient work schedule. A schedule is a comprehensive list of employees in an organization with assigned responsibilities and specific working times. An active schedule is termed as such because it weighs the needs of individual employees against the goals of an organization to reach a suitable compromise for both parties. There are several principles for consideration during workforce scheduling as listed below:
Demand modelling: This is considered when attempting to establish the exact number of staff and workers needed for each shift to clear the workload. Planners have employed the use of work forecasting tools in order to ensure there is no undersupply of resources at crucial times and also to ensure that there is no undersupply.
Scheduling workforce according to time: This method is applied mainly in organizations that have to maintain operations 24/7. The human resources department needs to prepare the schedule keeping in mind that they need to allow employees enough time between breaks and give them sufficient days off (Alfares, 2007, 53).
Cyclic or acyclic rosters: These are applied for organizations that have overlapping duties with employees having the same qualifications and responsibilities with the difference coming in through allocation of different starting times for different shifts (Alfares, 2008, 24). This is similar to the model applied in transportation industries, where though each worker has independent starting times the schedule can be synchronized to ensure optimum workforce supply.
Performance measures: This is applied in institutions that desire to optimize their workforce and ensure the administration incurs minimal expenditure and production time while at the same time maximizing output levels.
Regulations: Labor regulations in different jurisdictions play a significant role in workforce scheduling as the planner needs to ensure that the organization’s schedule conforms to industry regulations where the plant is located. Ideal for organizations with a large workforce.
Context: This is necessary in coming up with a plan to be applied to client organizations. It is not as simple as simply copying and pasting the schedule of a similar organization, as factors such as the lowest number of employees required for each shift, the organization’s workforce demands, the type of work being done by an organization, i.e., the industry and to some extent employee preference can be considered.
The scheduling process has since been automated and employs the use of mathematical algorithms in software to create and maintain a schedule. As such it plays an essential role in ensuring performance levels are at maximum capacity specifically in labor-intensive industries such as power plants. An objective that can be achieved in many ways, including employing proper workforce scheduling techniques. A key impediment to scheduling for the maintenance team in a power plant is to provide the right amount of workforce depending on scheduled maintenance plans while at the same time considering the unplanned maintenance needs that could arise. Power plants by their nature, specifically in the maintenance department, require the correct number of staff assigned who can adequately respond in the event of a plant emergency. To reduce plant downtime and also effectively lowering response times for emergency teams. In most labor-intensive industries the cost of maintaining the workforce can be a significant impediment to increasing profit margins. This serves as one of the underlying reasons behind the importance placed on effective workforce scheduling as a critical resource in the workplace. Scheduling is essential in picking the correct number of workers with the right qualifications assigned to perform duties at specific times. This reduces the workload and improves on the quality of output expected from personnel.
Power plants by their nature require close maintenance to ensure continuous output of power into the national grid. The power plant has been encountering availability and reliability problems within the grid system which increases high maintenance demand which in turn increases demand for overtime to meet the demand. The plant’s management has raised concerns about the excessive overtime that is being taken by the maintenance department on official workdays and during weekends. As a department, it records the highest number of overtime requests within the organization. This necessitated a closer look at the department intending to reduce the requests and optimizing the workforce schedule. Management also hoped to figure out a way to reduce the costs associated with these additional operations. The plants demand personnel can be filled satisfactorily through the schedule. An efficient schedule gives the minimum number of qualified employees required, encompass break times and leave days, be in compliance with labor requirements, i.e., fulfilling contractual obligations and regarding holidays, keep in mind varying shift classifications. The aims of effective scheduling can be classified into three categories, reducing overtime hours being worked, avoiding the undersupply of workers and having a schedule with minimal cost implications.
The maintenance department is split into four divisions; mechanical, electrical, instrument & control division. These divisions are charged with general maintenance functions including: Preventive maintenance (PM) which is a scheduled maintenance operation carried out periodically and Corrective maintenance (CM) which is unscheduled maintenance that occurs in the event of a disruption of services within the plant and Special Maintenance (SM) in compliance with management or statutory directives at any time. Based on the data collected it is evident that the corrective maintenance ratio is higher than preventive maintenance ratio which is the reason behind excessive overtime demand. During normal operations, plant workers are needed to be within the vicinity of the plant. They perform daily operations around the plant checking drainage points, meters and being on the lookout for any red flags in the system. Their duties include inspecting cylinders, cleaning parts of the machines, replacing lubrication oil on moving parts and changing worn out parts. To minimize maintenance problems and reduce overtime, there is a need for effective workforce scheduling. Since the most of workload goes under the mechanical section, this paper will focus on mechanical maintenance workforce scheduling.
Before embarking on the model, one needs insight into power plant maintenance, staffing, and scheduling objectives. These objectives are specifically tailored to reduce costs and to leverage the reliability of workers and plant personnel at any given moment. Workforce scheduling has been split into three distinct categories, daily shift schedules, daily schedules and weekly schedules. (Baker, 1975) As well as a look into the consequences of poor workforce scheduling decisions. A poor workplace schedule will lose credibility to employees and may inadvertently also lead to increased instances of workplace stress and increased absenteeism among staff members. A weak schedule could also result in a lack of necessary personnel at a given time leading to loss of productivity and wasted time. Managing becomes more of hustle, especially when expected to maintain consistency in output with a weak schedule.
Based on the collected data, the optimum number of maintenance staff will be determined by developing two mathematical programming models. The first model is to reduce the workers or workforce size under such operating constraints while the other approach is to recommend a different workforce schedule in various scenarios to save on costs. By combining flexible shift scheduling and a loose shift with a well-designed and automated scheduling and incorporating flexibility both regarding shift schedule as well as task scheduling to establish the staff that is required to ensure the flexible operation and smooth running of the plant.
The scheduling models are used in two scenarios; the first will be the current scenario of five official working days and the second scenario, which is to switch from five to seven days-off schedules. The results of the three scenarios will be compared to show the recommended solution that comes with cost saving. Three alternatives were compared; five-day workweek, switch to seven-day workweek for morning shift only and seven-day workweek schedule for all shifts (Al-Zubaidi and Christer, 2017, 31). This was done to identify the schedule needed to satisfy the pressing need for labor protection and maintenance with less cost and better efficiency.
At the moment, the official working days for the mechanical team are from Sunday to Thursday, 5-days a week, and the total number of mechanical technicians is 13 employees. The mechanical team claims that the number of workers now is not enough to handle daily activities both preventive and corrective. However, the management tries to optimize the number of employees since they believe the problem to be based on resource planning and scheduling rather than being understaffed. The power plant pays its employees a salary of 6500 with the extra option of 27 for every hour worked overtime. In 2016, the plant recorded, 48519 overtime hours worked and paid out 582228, which translates to an average of 25 hours a day and eight workers working overtime a day. Management requires an algorithm that will ultimately limit the increasing demand for overtime during the five working days and two days of the weekend and reducing the cost of labor overtime by utilizing the available work time within minimum requirements. See tables below on employee data (Dorn, 2016, 42).
Employee Total hours 150% Salary Pay per hour Monthly pay
Employee 1 59 88.5 6500 27 2389.5
Employee 2 95 142.5 6500 27 3847.5
Employee 3 112 168 6500 27 4536
Employee 4 121 181.5 6500 27 4900.5
Employee 5 64 96 6500 27 2592
Employee 6 110 165 6500 27 4455
Employee 7 90.5 135.75 6500 27 3665.25
Employee 8 160 240 6500 27 6480
Employee 9 80 120 6500 27 3240
Employee 10 96.5 144.75 6500 27 3908.25
Employee 11 56 84 6500 27 2268
Employee 12 73 109.5 6500 27 2956.5
Employee 13 81 121.5 6500 27 3280.5
Total hours per day 48519
Staff Demand Su Mo Tu We Th WEEK # 1 MECH 0 12 0 0 2 WEEK # 2 MECH 2 4 4 8 4 Week # 3 MECH 10 4 6 3 2 WEEK # 4 MECH 0 10 2 2 2 WEEK # 5 MECH 2 0 6 0 4 Week # 6 MECH 0 2 2 0 0 WEEK # 7 MECH 0 0 8 0 2 WEEK # 8 MECH 2 2 2 0 4 WEEK # 9 MECH 8 8 6 0 2 WEEK # 10 MECH 0 8 6 2 10 WEEK # 11 MECH 2 2 4 10 6 WEEK # 12 MECH 2 0 12 8 10 WEEK # 13 MECH 8 2 6 4 2 WEEK # 14 MECH 2 0 0 2 0 WEEK # 15 MECH 0 0 4 0 0 WEEK # 16 MECH 4 0 4 8 4 WEEK # 17 MECH 2 8 0 12 10 WEEK # 18 MECH 2 14 4 8 4 WEEK # 19 MECH 2 0 0 0 2 WEEK # 20 MECH 0 4 4 8 0 WEEK # 21 MECH 12 10 6 6 2 WEEK # 22 MECH 10 8 2 2 4 WEEK # 23 MECH 6 2 2 2 2 WEEK # 24 MECH 6 0 8 4 0 WEEK # 25 MECH 2 2 6 0 10 WEEK # 26 MECH 14 0 6 6 2 WEEK # 27 MECH 2 2 6 2 18 WEEK # 28 MECH 2 0 2 0 2 WEEK # 29 MECH 8 0 4 18 0 WEEK # 30 MECH 10 4 6 10 0 WEEK # 31 MECH 4 10 8 12 14 WEEK # 32 MECH 12 18 8 2 6 WEEK # 33 MECH 12 16 4 4 0 WEEK # 34 MECH 6 6 2 10 12 WEEK # 35 MECH 2 8 4 6 6 WEEK # 36 MECH 20 10 4 8 6 WEEK # 37 MECH 0 4 2 0 8 WEEK # 38 MECH 12 18 8 0 0 WEEK # 39 MECH 8 10 0 4 8 WEEK # 40 MECH 8 2 8 8 6 WEEK # 41 MECH 8 12 8 0 12 WEEK # 42 MECH 12 12 22 6 6 WEEK # 43 MECH 4 4 10 14 0 WEEK # 44 MECH 8 4 8 6 4 WEEK # 45 MECH 10 2 12 10 16 WEEK # 46 MECH 8 8 4 16 8 WEEK # 47 MECH 20 10 2 4 18 WEEK # 48 MECH 16 8 8 14 10 WEEK # 49 MECH 10 8 12 8 6 WEEK # 50 MECH 30 14 8 12 2 WEEK # 51 MECH 16 12 6 10 6 WEEK # 52 MECH 12 0 8 8 6 Average 7 6 5 6 5 Annual Average 6
The organization has seven official working days stretching from Sunday to Thursday, and two weekend days on Friday and Saturday. The plant runs for 24 hours in a day as the supply of power cannot be interrupted without serious economic implications for both the national grid, which receives power from the plant and the plant itself. This means that the plant requires to maintain employees for all the times when the plant is operational. Due to the exorbitant costs associated with this, the organization hopes to minimize the labor cost while also at the same time eliminating time wastage at the plant. This can be done through integer programming model formulation, which is mostly the idea of representing real-world problems as variables concerning linear inequalities in a research model (Al-Sugair, 2014, 7).
Ideally, because different organizations have different varying specifications, it means that algorithms should be developed specifically for these kinds of organizations and for diverse applications. Developing this required algorithm will need the plant to employ a demand modelling study, from collected employee data. Demand modelling is done in a bid to be able to adequately foresee the plant’s requirements for staff at different times and at the same time weighing this against the size of the workforce present and able to sufficiently complete assigned duties. After the required demands have been ascertained the scheduling team should then be able to determine possible solution models that fit in within the context of the plant (Dufaa and Al-Sutan, 2017, 32). This enables the team to come up with a tool that would adequately meet staffing requirements of the plant within specific limitations that come up from regulations and plant objectives and goals for the workforce. After formulation of the model the team should then develop a reporting function to assess the There are several factors or constraints to be kept into account when formulating a model to generate an optimal working schedule:
There is always one worker on vacation, training or sick leave so, it is assumed that only 12 workers are available throughout the year.
Labour office regulations require plants to have 8 hour working days for five days a week.
Critical system failure must be repaired immediately.
The workforce is available any time and can operate for any division.
The study will be conducted on the mechanical maintenance section in which most of the workload goes under it.
Each employee should have two or three days.
To complete the model, the following data will be required to stand in as variables
The number of the daily requirement of staff. This can be determined by maintenance management system at the plant where statistical data is available. The demand of workforce depends on the number of daily maintenance work orders.
The number of available workforces.
Average overtime wage for one hour for the employees.
Total overtime cost for one year (specifically for 2016).
In order to fulfill the first objective which is to specify optimum workforce schedules, at the power plant, to limit the increasing demand for overtime during the five working days of the week and two days of a weekend. The problem is to reveal and determine the staff and workers required to assign on each day based on the plant’s labor requirements as specified in the data all while reducing overall costs.
4.2 Model Formulation
The primary two objectives of this research study are:
a) Specifying optimum workforce schedules, at a power plant, to limit the increasing demand for overtime during the five working days and two days of the weekend.
b) Reducing the cost of labor overtime by utilizing the available worktime within minimum requirements.
There are several factors or constraints to be kept into account when formulating a model to generate an optimal working schedule:
There is always one worker on vacation, training or sick leave so, it is assumed that only 12 workers are available throughout the year.
Labour office regulations require plants to have 8 hour working days for five days a week.
Critical system failure must be repaired immediately.
The workforce is available any time and can operate for any division.
The study will be conducted on the mechanical maintenance section in which most of the workload goes under it.
Each employee should have two consecutive days off per week.
The below is a model or an approach that can be used to calculate:
Given daily requirements (same for every week) which has been determined to be at six workers
Find the minimum number of employees to cover a 7-day-week
Constraints:
1) Total number of employees and total workers or staff on the day, for example, j is nj, j=1,…, 7
2) Individual employees are entitled to k1 in of every k2 of the weekends that the employee is off.
3) All employed persons work precisely five days in a week (Sun to Sat)
4) Employees should work at most six consecutive days
The letter W is the minimum number of workers and employees. This is required for computation of the workforce scheduling algorithm and allows the team to calculate several bounds to work with.
What lower bounds can you find?
The number of workers and the total employees must be at least the maximum daily demand.

daily demand lower bound

Total employee days per week must be at least total weekly demand.

Then,

total demand lower bound
The average number of employees available each weekend must be at least the maximum weekend demand.
Over k2 weekends:

Then,

lower bound for the weekend
Let W* be the maximum of these three bounds, then W>=W*
Example

Each employee is given one out of three weekends off.
1) max(n1,…,n7) = 3, then W>=3
2), so W>=3
3) max (n1, n7) = 2, k1 = 1 and k2 = 3, so

As a result of the three bounds W* = 3, so W>=3
There is an algorithm (outlined below) that finds a possible schedule with workforce equal to a maximum of the three lower bounds above.
This implies that
Notation:
maximum weekend demand: n = max (n1, n7)
surplus of employees on day j:
uj = W – nj, for j=2,…,6 and uj = n – nj, for j=1,7
Note that
due to total demand bound
Calculate the three lower bounds and find W, their maximum
Number employees from 1 to W
W – n employees can have a weekend off
Start the schedule on Sunday (weekend 1)
Sequentially provide a schedule and a work plan for each and every week
Algorithm Overview:

Daily Surplus:

-27432014033500
Step 2: Find additional off day pairs.
Build and generate a plan or a list of n pairs of the off days:
Pick and identify a day with the maximum level or surplus
Identify another day or week with a positive increase or surplus. However, if none exists, it is advisable to pick the first day)
Add all these pairs to the list and continue updating the surplus values
Example (continued):
Pair 1: Sunday -Monday
Pair 1: Monday -Monday
Set i=1 for Step 3
-32004018415000
Step 3: Categorize employees in week i
Based on off days needed, employees are categorized into 4
Step 4: Assign off day pairs to employees in different categories in week i
Remarks:
The days-off scheduling problem has a solution that is cyclic in nature, i.e., the same pattern repeats itself
The model above can be applied to this specific workforce scheduling problem as explained above. This can be done as follows:
Variables to be used during the model
X(g): Number of personnel scheduled at time g, g=0,4,8,12,16,20
It is assumed that the problem will be repeated in a simulation for an undefined number of days with the same staff requirement and that x(g) is the number used in every day at time g.
Constraints
There is always one worker on vacation, training or sick leave so, it is assumed that only 12 workers are available throughout the year.
Labour office regulations require plants to have 8 hour working days for five days a week.
Critical system failure must be repaired immediately.
The workforce is available any time and can operate for any division.
The study will be conducted on the mechanical maintenance section in which most of the workload goes under it.
Each employee should have two consecutive days off per week.
Constraints ensure that personnel scheduled for respective times are adequate to clear the workload. For the shift starting at time 0 to 4, the personnel from time 20 of the day ending previously and time 0 of the present moment would cover the timeframe from time 0 to 4.
x(20)+x(0)≥4
The remaining constraints are:

X(0)+x(4)≥8, x(4)+x(8)+x(12)≥7, x(12)+x(16)+x20≤4
The objective would be to

Minimize z=x(0)+x(4)+x(8)+x(12)+x(16)+x(20)
Expressed the model algebraically would appear as follows:

Or can be written or expressed as a matrix as follows:

The optimal solution would then appear as follows for all the constraints, which would then be 26 workers for each period. This would then be the optimum number of workers required to operate at maximum capacity and efficiency however at the lowest cost.

We will then proceed to draft a model required for implementation under different working scenarios.
1) Days-off Scheduling:
This special case is a cyclic staffing problem:
Each employee is guaranteed two days off per week, including every other weekend
Each employee cannot work more than six consecutive days
IP formulation:

2) Cyclic Staffing with Overtime
Three shift of 8 hours each: spread throughout the day to allow for easy coagulation
Overtime of up to 8 hrs possible for each shift, however, it is management’s
4569491963481 0 01
01 1 0
0 01 1
001 0 01
01 1 0
0 01 1
3657602032000036576020320000141732020320000155448020320000
O1: triangular matrix with 1’s to the right of the diagonal
36576014986000141732014986000
3) Cyclic Workers and Staff with Linear Penalties for Overstaffing and Understaffing.
The demand in each and every period is flexible and not fixed
There is a penalty indicated as ui that is used for understaffing and another marked as oi and used for overstaffing in period i
(oi may be harmful due to benefits of overstaffing)

Conclusion and Recommendations
This section of the paper seeks to discuss the key findings and results that can be derived from both the case analysis and the model formulation sections. It identifies the principles that will be utilized for workforce scheduling, it discusses the constraints, and identifies the opportunities available to facilitate a seamstress scheduling of staff. This part of the research also offers an organizational and workforce overview, and identifies a model formation plan. Most importantly, it discusses the exact data, computations, and quantifications that will be leveraged as part of designing and applying the scheduling model for a successful workforce restructuring. This analysis believes that a review of various areas of analysis, and a radical examination of the underlying factors that affect workforce scheduling and how they can be amended to suit changing situations.
Organizational and Workforce Overview
The paper identifies that the selected organization has seven official working days stretching from Sunday to Thursday, and two weekend days on Friday and Saturday. The plant runs for 24 hours in a day as the supply of power cannot be interrupted without serious economic implications for both the national grid, which receives power from the plant and the plant itself. This means that the plant requires to maintain employees for all the times when the plant is operational. Due to the exorbitant costs associated with this, the organization hopes to minimize the labor cost while also at the same time eliminating time wastage at the plant. This can be done through integer programming model formulation, which is mostly the idea of representing real-world problems as variables concerning linear inequalities in a research model.
Principles for Workforce Scheduling
There are several principles for consideration during workforce scheduling as listed below. The first principle identified in this analysis is the demand modelling. This is explicitly considered when attempting to determine the exact number of workers needed for each shift to clear the workload. Planners have employed the use of work forecasting tools in order to ensure there is no undersupply of resources at crucial times and also to ensure that there is no undersupply. Another principle is to scheduling the workforce according to time. This method is applied mainly in organizations that have to maintain operations 24/7. The human resources department needs to prepare the schedule keeping in mind that they need to allow employees enough time between breaks and give them sufficient days off.
Cyclic or acyclic rosters are crucial evaluation metrics. These are applied for organizations that have overlapping duties with employees having the same qualifications and responsibilities with the difference coming in through allocation of different starting times for different shifts. This is similar to the model applied in transportation industries, where though each worker has independent starting times the schedule can be synchronized to ensure optimum workforce supply.
Performance measures: This is applied in institutions that desire to optimize their workforce and ensure the administration incurs minimal expenditure and production time while at the same time maximizing output levels. Under regulations, labor laws in different jurisdictions play a significant role in workforce scheduling as the planner needs to ensure that the organization’s schedule conforms to industry regulations where the plant is located. Ideal for organizations with a large workforce. The context is identified as the final principle and value for workforce scheduling. This is necessary in order to come up with a plan that would easily be applied to client organizations. It is not as simple as simply copying and pasting the schedule of a similar organization, as factors such as the lowest number of employees required for each shift, the organization’s workforce demands, the type of work being done by an organization, i.e., the industry and to some extent employee preference can be considered.
Model Formulation Plan
The designed approaches and models will be applied to two scenarios; the first will be the current scenario of five official working days and the second scenario, which is to switch from five to seven days-off schedules. The model has been made accordingly based on real-life factory data and thus manage to demonstrate its usefulness. A key consideration is the use of the ‘’use factor’ which shows and determines how often the maintenance engineers and technicians are busy in handling their tasks. The use factor also makes it possible to determine any unprecedented maintenance workloads. The results of the three scenarios will be compared to show the recommended solution that comes with cost saving. Three alternatives were compared. The first option was the utilization of a five-day workweek. The second was to switch to seven-day workweek for morning shift only. And the final alternative was the seven-day workweek schedule for all shifts to identify the required schedule to satisfy the ever-rising labor requirements with less cost and better efficiency.
Constraints in the Formulation of a Scheduling Model
This paper identifies there are several factors or constraints to be kept into account when formulating a model to generate an optimal working schedule. There is always one worker on vacation, training or sick leave so, it is assumed that only 12 workers are available throughout the year. Also, labour office regulations require plants to have 8 hour working days for five days a week. Similarly, critical system failure must be repaired immediately. The workforce is available any time and can operate for any division. The study will be conducted on the mechanical maintenance section in which most of the workload goes under it. Each employee should have two consecutive days off per week.
Data Required for Model Formulation
To complete the model, the following data will be required to stand in as variables. This include the number of the daily requirement of staff. This can be determined by maintenance management system at the plant where statistical data is available. The demand of workforce depends on the number of daily maintenance work orders. The number of available workforces. Average overtime wage for one hour for the employees. And the total overtime cost for one year (specifically for 2016). In order to fulfill the first objective which is to specify optimum workforce schedules, at the power plant, to limit the increasing demand for overtime during the five working days of the week and two days of a weekend. The problem is to determine the number of workers to assign on each day based on the plant’s labor requirements as specified in the data all while reducing overall costs.
Considerations for Model Formulation
This research also establishes that before embarking on the staff scheduling model, one needs insight into power plant maintenance, scheduling and staffing goals. These objectives are specifically tailored to reduce costs and to maximize the availability and reliability of plant personnel at any given moment. Workforce scheduling has been split into three distinct categories, daily shift schedules, daily schedules and weekly schedules. (Baker, 1975). As well as a look into the consequences of poor workforce scheduling decisions. A poor workplace schedule will lose credibility to employees and may inadvertently also lead to increased instances of workplace stress and increased absenteeism among staff members. A weak schedule could also result in a lack of necessary personnel at a given time leading to loss of productivity and wasted time. Managing becomes more of hustle, especially when expected to maintain consistency in output with a weak schedule. The above analysis surmises the paper on workforce scheduling and creates insight on how private and public sector organizations can approach workforce restructuring as part of creating effective and highly productive staff.
References
Alfares, H.K. (2007), “Maintenance work schedule for Dhahran fixed-wing aircraft”, Project
Report Number 14297, Dhahran, Saudi Arabia, September.
Alfares, H.K. (2008), “An efficient two-phase algorithm for cyclic days-off scheduling”,
Computers & Operations Research, Vol. 25, pp. 913-23.
Al-Sugair, A.S. (2014), “Aircraft maintenance division manpower”, a presentation to the aviation
department management, Saudi Aramco, Dhahran, Saudi Arabis, June.
Al-Zubaidi, H. and Christer, A.H. (2017), “Maintenance workforce modeling for a hospital
building complex”, European Journal of Operational Research, Vol. 99, pp. 603-18.
Baker, K.R. (2014), “Workforce allocation in cyclical scheduling problems: a survey”, Operational
Research Quarterly, Vol. 27, pp. 155-67.
Dorn, M.D. (2016), “Effects of maintenance human factors in maintenance-related aircraft
accidents”, Transportation Research Record, No. 1517, pp. 17-28.
Duffuaa, S.O. and Al-Sultan, K.S. (2017), “Mathematical programming approaches for the
management of maintenance planning and scheduling”, Journal of Quality in Maintenance
Engineering, Vol. 3, pp. 163-76.