Optimizing the use of resources plays an important role in today’s modern manufacturing and service organizations. Scheduling, involving setup times and costs, leads to better allocation of resources over time to perform a collection of required tasks. This compilation dissertation examines how the learning effect of workers and a combination of setup activities can be used to optimize resource utilization in manufacturing systems and maintenance services. The learning effect is a technique that can model improvement in worker’s ability as a result of repeating similar tasks. By considering the learning effect, setup times will be reduced, and a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose is to schedule a set of jobs in a hybrid flow shop environment while minimizing two criteria that represent the manufacturers’ and consumers’ concerns: namely maximum completion time (makespan) and total tardiness. Combining setup activities can also reduce setup times and costs. In the maintenance of systems consisting of multiple components, costs can be saved when several components are jointly maintained. By using this technique, a schedule can be determined to minimize the total cost of maintenance and renewal projects for various components and their relevant setup activities. Mathematical programming models that incorporate these aspects of the problem are developed in this research and the performance of the proposed models are tested on a set of problem instances. The results of this work show that the proposed techniques perform well in reducing setup times and costs and eliminate the need for setups through scheduling. This work proposes several exact, heuristic, and meta-heuristic methods to solve the developed models and compare their efficiency. This study contributes to the theoretical discussion of multi-criteria production and maintenance scheduling. For practitioners, this dissertation work provides optimization techniques and tools through scheduling that can help keep costs down and allow companies to operate according to time and budget constraints.

1. RESOURCE OPTIMIZATION TECHNIQUES IN SCHEDULING
Applications to production and maintenance systems
30th November 2017
Doctoral candidate: Farzad Pargar
Opponents: Prof. Juha-Matti Lehtonen
Dr. Jussi Hakanen
Kustos: Prof. Jaakko Kujala
INDUSTRIAL ENGINEERING
AND MANAGEMENT

2. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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COMPILATION DISSERTATION
DOCTORAL DISSERTATION: HTTP://JULTIKA.OULU.FI/FILES/ISBN9789526216942.PDF

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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INTRODUCTION
SCHEDULING
Scheduling is a decision-making process of allocating resources over
time to perform a collection of tasks.
 The resources and tasks in every organization can take different forms.
 Each task may have a certain due date.

4. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
The decision to provide multiple tasks on common resources results in the
need for setup activities.
The setup process is not a value-added factor.
 Machine setup time may easily consume more than 20% of machine capacity if not
well handled.
Pinedo, M. (2015). Scheduling. Springer.
 Minimizing total setup cost at the manufacturing facility of has saved more
than $1 million per year and production volume has increased as much as 35%.
Loveland, J. L., Monkman, S. K., & Morrice, D. J. (2007). Dell uses a new production-scheduling algorithm to
accommodate increased product variety. Interfaces, 37(3), 209-219.
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SCHEDULING
IMPORTANCE OF SETUP TIMES & COSTS

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH PROBLEM
OPTIMIZATION IN TWO APPLICATION AREAS
Finding the sequence of jobs with
minimum completion time and delays
Finding the sequence of maintenance
actions with minimum cost and down
time.
Multi-unit systemsHybrid flow shopsTextile productionCircuit boards productionIron and steel production Distributed pipelineRoadwaysElectricity distribution networksRailways

6. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH PROBLEM
OPTIMIZATION IN TWO APPLICATION AREAS
Finding the sequence of jobs with
minimum completion time and delays
Finding the sequence of maintenance
actions with minimum cost and down
time.
Multi-unit systemsHybrid flow shopsTextile productionCircuit boards production Distributed pipelineRoadwaysElectricity distribution networks

7. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH PROBLEM
OPTIMIZATION IN TWO APPLICATION AREAS
Finding the sequence of jobs with
minimum completion time and delays
Finding the sequence of maintenance
actions with minimum cost and down
time.
Multi-unit systemsHybrid flow shopsTextile production Distributed pipeline
RoadwaysPipelines

8. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH PROBLEM
OPTIMIZATION IN TWO APPLICATION AREAS
Finding the sequence of jobs with
minimum completion time and delays
Finding the sequence of maintenance
actions with minimum cost and down
time.
Multi-unit systemsHybrid flow shops

9. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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IMPORTANCE OF THE RESEARCH PROBLEM
• Minimizing makespan
• Minimizing tardiness
• Minimizing maintenance cost
• Minimizing downtime
OBJECTIVES & BUSINESS RELATIONS

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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TASK’S DUE DATES
CONSTRAINT OR OBJECTIVE!
Finding the sequence of jobs with
minimum completion time and delays
Finding the sequence of maintenance
actions with minimum cost

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH APPROACH
OPERATIONS RESEARCH & OPTIMIZATION
 Operations research (OR) is a discipline that deals with the application of
advanced analytical methods (mathematical models) to help make better
decisions
 The term optimize is “to make perfect”. Choosing the best element from
set of available alternatives.

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH APPROACH
MATHEMATICAL MODEL FOR MAINTENANCE SCHEDULING
Subject to:

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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RESEARCH QUESTIONS
RESEARCH PROBLEM
How to optimize resource utilization in production and maintenance
scheduling problems, and how to find an optimal or near-optimal solution
for the selected performance criteria.

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Farzad Pargar- Resource Optimization Techniques in Scheduling
RELATIONSHIP AMONG RESEARCH AREAS, RESEARCH
QUESTIONS, AND ARTICLES

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Farzad Pargar- Resource Optimization Techniques in Scheduling
ARTICLE RELATIONS
VIEW OF OPTIMIZATION TYPES AND APPLICATION AREAS

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Farzad Pargar- Resource Optimization Techniques in Scheduling
 Exact, heuristic, and meta-heuristic optimization algorithms are the three
key solution approaches addressed to solve the proposed scheduling
problems.
For example: B&B, SPTCH, Decomposition, RKGA, WFA, NSGA-II, NRGA,
NSWFA, NRWFA.
 Importance of resource optimization techniques in the scheduling of
activities in production and maintenance systems is shown.
 Learning effect, combining setup activities (grouping), and balancing are
the techniques used in this dissertation to optimize the performance of
hybrid flow shop manufacturing systems and maintenance of multi-unit
systems.
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FINDINGS

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BNEFITS OF LEARNING
EXAMPLE: 1 STAGE, 1 MACHINE, 4 JOBS
Learning is effected by the number of jobs that have been processed.
Setup time of job i to job j, scheduled in position r at stage t.

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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BNEFITS OF LEARNING
EXAMPLE: 2 STAGES, 2 MACHINES, 6 JOBS

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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BENEFITS OF GROUPING
EXAMPLE: 3 UNITS, 2 COMPONENTS, 3 TIME PERIODS
Comparing three scenarios for carrying out the replacement of component 1
in units 1-3 and maintenance of component 2 in units 1-2.

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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BENEFITS OF BALANCING
EXAMPLE: 1 UNIT, 1 COMPONENT
 When an asset ages, maintenance is required increasingly often.
 The latest possible time for carrying out the next maintenance/renewal
relevant to the previous maintenance is known (planning cycle).
Wear stock
Time
Technical life : Renewal
: Maintenance
New
Planning horizon
Maintenance
threshold
PM cycle 1

21. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
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BENEFITS OF BALANCING
EXAMPLE: 1 UNIT, 1 COMPONENT
 When an asset ages, maintenance is required increasingly often.
 The latest possible time for carrying out the next maintenance/renewal
relevant to the previous maintenance is known (planning cycle).
Wear stock
Time
: Renewal
: Maintenance
New
Maintenance
threshold
Planning horizon
Wear stock
Time
PM cycle 1
Economic life

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Farzad Pargar- Resource Optimization Techniques in Scheduling
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BENEFITS OF GROUPING & BALANCING
EXAMPLE: 3 UNITS, 2 COMPONENTS, 16 TIME PERIODS

23. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
 Integrating several resource optimization techniques in production and
maintenance scheduling problems.
 Developing mathematical models and proposing heuristic and meta-
heuristic optimization algorithms and analyzing their efficiency
 Showing the importance of considering different stakeholders’ concerns
simultaneously.
 Identifying time and cost reduction potential of the resource optimization
techniques
Building good models is an art!
Essentially, good models are not those in papers but models that are
in use.
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CONCLUSIONS
THEORETICAL AND PRACTICAL IMPLICATIONS

24. University of Oulu
Farzad Pargar- Resource Optimization Techniques in Scheduling
Thank you!
farzad.pargar@oulu.fi

25. RESOURCE OPTIMIZATION TECHNIQUES IN SCHEDULING
Applications to production and maintenance systems
30th November 2017
Doctoral candidate: Farzad Pargar
Opponents: Prof. Juha-Matti Lehtonen
Dr. Jussi Hakanen
Kustos: Prof. Jaakko Kujala
INDUSTRIAL ENGINEERING
AND MANAGEMENT