WAITING LINES
Learning Objectives

After completing the lecture, we should be able to explain the formation of waiting lines in unloaded systems, identify the goal of queuing ( waiting line) analysis, list the measures of system performance that are used in queuing analysis. We should be able to understand the importance of simulation and at the same time we should look beyond the Production Operations Management class as business graduate professionals adding value to the society.
Visit to a Cricket Stadium

production and operations management  WAITING LINES

1.  Waiting in lines does not add enjoyment

2.  Waiting in lines does not generate revenue

3.  Waiting Lines

4.  Waiting lines are non-value added occurrences

5.  Are formed at airports, cricket stadiums, post offices.

6.  Formed due to non scheduled random arrivals

7.  Often regarded as poor service quality

Waiting Line Examples

1.  Orders waiting to be filled

2.  Trucks waiting to be loaded or unloaded

3.  Job waiting to be processed

4.  Equipment waiting to be loaded

5.  Machines waiting to be repaired.

Service Station as a Waiting Line Example

Service station is usually designed to provide service on average service time. At macro level system is unloaded at micro level the system is overloaded a Paradox Customers arrive at random rate Service requirements vary only oil change or even tuning or maintenance activity in order to change oil

Waiting Lines

Queuing theory: Mathematical approach to the analysis of waiting lines.

1.  Goal of queuing analysis is to minimize the sum of two costs Customer waiting costs and Service capacity costs.

2.  Waiting lines are non-value added occurrences

Implications of Waiting Lines

1.  Cost to provide waiting space

1.  Loss of business

1.  Customers leaving

2.  Customers refusing to wait

2.  Loss of goodwill

3.  Reduction in customer satisfaction

4.  Congestion may disrupt other business operations

Organizations carry out queuing analysis to ensure that they are able to balance the service levels with costs which the organization can incur. The ultimate goal of queuing analysis is to minimize the sum of two costs that is the service capacity cost (represented on x axis) and customer waiting costs.

production and operations management  WAITING LINES

Negative Exponential Distribution: Another example of Common Queuing System


production and operations management  WAITING LINES

Queue discipline is considered to be a primary requirement in service systems. However hospital emergency rooms, rush orders in a factory and main frame computer processing of jobs do not follow

Queue Discipline.

System Characteristics

1. Population Source

a.Infinite source: customer arrivals are unrestricted

b.Finite source: number of potential customers is limited

1.  Number of observers (channels)

2.  Arrival and service patterns

3.  Queue discipline (order of service)

Elements of Queuing System

Population Source, Arrivals, Waiting Lines, Processing Order, Service, System and Exit are the common identifiable elements of a Queuing System.

production and operations management  WAITING LINES

Queuing Systems

The System characteristics are

1.  Population Source

2.  Number of Servers( Channels)

3.  Arrival and Service Patterns

4.  Queue Discipline

production and operations management  WAITING LINES

Poisson Distribution

Poisson distribution is a discrete probability distribution and expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate, and are independent of the time since the last event.

0.25

0.2

0.15

0.1

0.05 0
production and operations management  WAITING LINES

Waiting Line Models

As a student of Operations Management we can identify the following types of Waiting Line Models in our day to day routine activities.

1.  Patient :Customers enter the waiting line and remain until served

2.  Reneging: Waiting customers grow impatient and leave the line

3.  Jockeying: Customers may switch to another line

4.  Balking: Upon arriving, decide the line is too long and decide not to enter the line Waiting Time vs. Utilization

The figure represents an increase in system utilization at the expense of increase in both length of the waiting line and average waiting time. These values increase as the utilization approaches 100 percent. The implication is that under normal circumstances, 100 percent utilization is not a realization goal.

production and operations management  WAITING LINES

Waiting Time vs. Utilization

System Performance

1.  Average number of customers waiting

2.  Average time customers wait

3.  System utilization

4.  Implied cost

5.  Probability that an arrival will have to wait

Example Service Station

Queuing Models: Infinite-Source

1.  Single channel, exponential service time

2.  Single channel, constant service time

3.  Multiple channel, exponential service time

4.  Multiple priority service, exponential service time

Priority Model


production and operations management  WAITING LINES

Finite-Source Formulas

T

Service Factor X =

T + U Average NumberWaiting L = N (1 − F ) L(T + U ) T (1− F )

AverageWaiting TimeW ==

N L XF Average Number Running J = NF (1− X ) Average Number being Served H = FNX Number in Population N = J + L + H

Finite-Source Queuing
Where we use the formula

production and operations management  WAITING LINES

Other Approaches Non Mathematical Approaches

    J + H

    Reduce perceived waiting time F =

    J + L + H

    Magazines in waiting rooms Radio/television In-flight movies Filling out forms Derive benefits from waiting Place impulse items near checkout Advertise other goods/services

    Simulation

    Simulation: a descriptive technique that enables a decision maker to evaluate the behavior of a model under various conditions.

    1.  Simulation models complex situations

    2.  Models are simple to use and understand

    3.  Models can play “what if” experiments

    4.  Extensive software packages available

    Simulation Process

    1.  Identify the problem

    2.  Develop the simulation model

    3.  Test the model

    4.  Develop the experiments

    5.  Run the simulation and evaluate results

    6.  Repeat 4 and 5 until results are satisfactory

    Monte Carlo Simulation

    Monte Carlo method: Probabilistic simulation technique used when a process has a random component

    1.  Identify a probability distribution

    2.  Setup intervals of random numbers to match probability distribution

    3.  Obtain the random numbers

    4.  Interpret the results

    Example Showing the use of Microsoft Excel

    An Operations Manager makes best use of the power of Microsoft Excel by carrying out simulation. The first picture below shows a snapshot which carries the formulae and the second picture represents the actual values.

    production and operations management  WAITING LINES

    production and operations management  WAITING LINES

    Simulating Distributions commonly used are the Poisson and Normal Distributions.

    Poisson distribution: Mean of distribution is required Normal Distribution: Need to know the mean and standard deviation

    Uniform Distribution

    F(x)

    production and operations management  WAITING LINES

    Computer Simulation

    Simulation languages

    1. SIMSCRIPT II.5

    1.  GPSS/H

    2.  GPSS/PC

    4. RESQ Advantages of Simulation

    1.  Solves problems that are difficult or impossible to solve mathematically

    2.  Allows experimentation without risk to actual system

    3.  Compresses time to show long-term effects

    4.  Serves as training tool for decision makers

    Limitations of Simulation

    1.  Does not produce optimum solution

    2.  Model development may be difficult

    3.  Computer run time may be substantial

    1.  Monte Carlo simulation only applicable to random systems Why Simulation is necessary

    1.  Mathematics involved is too complicated

    2.  Easier to manipulate than reality

    2.  Software and hardware permit modeling Simulation Steps

    1.  Problem formulation

    2.  Model building

    3.  Data acquisition

    4.  Model translation

    5.  Verification & validation

    6.  Experiment planning & execution

    7.  Analysis

    8.  Implementation & documentation

    Operations Strategy

    1.  The central idea for formulating an Operations Strategy for Waiting Line concept is designing a service system to achieve a balance between service capacity and customer waiting time.

    2.  The operations strategy should be able to identify an appropriate and acceptable level of service capacity as well as quality so waiting lines are not formed or formed which are manageable and acceptable to the customers.

    3.  Often Organizations when challenged by lack of practical solutions or space constraints opt for a more tangible quality based solutions by engaging the waiting customers in activities which give the customers not only an opportunity to make use of the time but also to make the waiting time less painful and more pleasant.

    Summary

    Analysis of waiting lines can be an important milestone in the design of improved service systems. Waiting lines have a tendency to form in even those systems which in a macro sense are under loaded or unloaded. The arrival of customers at random times and variability of service times combine to create temporary overloads. When this happens, waiting lines appear. A major consideration in the analysis of the queuing systems is whether the number of potential customers is limited (finite source) or whether entry to the system is unrestricted (infinite source).Of the 5 models we studied, 4 dealt with infinite source and 1 with finite source population. As a rule, the models assume that customer arrival rates are described by Poisson distribution and service time can be described by a negative exponential distribution.

    POMA Strategies beyond the final exam

    1.  In the long run (when factors of production change, any or combination of the factors of labor, land, technology), productivity growth is almost everything if not everything.

    2.  Do not create artificial non operational management strategies means to balance capacity to demand (It can cause competitive advantage to shift towards your competitor and your organization losing the competition.

    3.  How much does it really cost to manufacture a product or develop a service ( refer to the concept of total costs, which we learnt in our discussions on inventory management, alternative capacity, quality, maintenance and waiting lines)

    4.  Competitive advantage in operational and organization strategy creates a win win situation for the organization.

    5.  Operations Manager should learn to think at the margin (an addition in cost by 1 Rupee(unit cost) would increase or decrease the revenue by 1 Rupee(unit revenue/benefit)).

    6.  How we as Operations Manager can play a part in minimization of costs of most important of services in Pakistan i.e. education and medical. Trade off between Effectiveness and quality.

    7.  How and why Project Management concepts are equally important to Production Operations Management and vice versa.

    8.  The importance of coordinating, planning, controlling, budgeting operations and project activities in achieving our firms short and long term objectives.

    9.  The concepts of strategy, competitiveness and productivity, design of product and services, design of work systems and facilities, concept of quality and system improvement as applicable in organizations be applied to Pakistan.

    1. How as Operations Manager we can communicate to masses the importance of Pakistani domestic markets and how they help in capital formation. If we say no to foreign goods consumption, foreign good would not come to our place and we can generate a well deserved saving. That saving can be channelized to provide clean and drinkable water, better health care, education or even used for infrastructural issues. e.g. if 1 % of Pakistani population saves Rs. 10 per week for 1 year alone we would have almost 780 million rupees or 12 to 13 Million US dollars by which we can set biogas plants or waste incinerator boiler based power generation or clean drinking waters or even institutions of higher learning.
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