Abstract: Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied.
Abstract: The Flow Shop Scheduling Problem (FSSP) is a typical problem that is faced by production planning managers in Flexible Manufacturing Systems (FMS). This problem consists in finding the optimal scheduling to carry out a set of jobs, which are processed in a set of machines or shared resources. Moreover, all the jobs are processed in the same machine sequence. As in all the scheduling problems, the makespan can be obtained by drawing the Gantt chart according to the operations order, among other alternatives. On this way, an FMS presenting the FSSP can be modeled by Petri nets (PNs), which are a powerful tool that has been used to model and analyze discrete event systems. Then, the makespan can be obtained by simulating the PN through the token game animation and incidence matrix. In this work, we present an adaptive PN to obtain the makespan of FSSP by applying PN analytical tools.
Abstract: The agenda of showing the scheduled time for
performing certain tasks is known as timetabling. It is widely used in
many departments such as transportation, education, and production.
Some difficulties arise to ensure all tasks happen in the time and
place allocated. Therefore, many researchers invented various
programming models to solve the scheduling problems from several
fields. However, the studies in developing the general integer
programming model for many timetabling problems are still
questionable. Meanwhile, this thesis describes about creating a
general model which solves different types of timetabling problems
by considering the basic constraints. Initially, the common basic
constraints from five different fields are selected and analyzed. A
general basic integer programming model was created and then
verified by using the medium set of data obtained randomly which is
much similar to realistic data. The mathematical software, AIMMS
with CPLEX as a solver has been used to solve the model. The model
obtained is significant in solving many timetabling problems easily
since it is modifiable to all types of scheduling problems which have
same basic constraints.
Abstract: In this paper, we propose two algorithms to optimally
solve makespan and total completion time scheduling problems with
learning effect and job dependent delivery times in a single machine
environment. The delivery time is the extra time to eliminate adverse
effect between the main processing and delivery to the customer. In
this paper, we introduce the job dependent delivery times for some
single machine scheduling problems with position dependent learning
effect, which are makespan are total completion. The results with
respect to two algorithms proposed for solving of the each problem
are compared with LINGO solutions for 50-jobs, 100-jobs and 150-
jobs problems. The proposed algorithms can find the same results in
shorter time.
Abstract: Batch production plants provide a wide range of
scheduling problems. In pharmaceutical industries a batch process
is usually described by a recipe, consisting of an ordering of tasks
to produce the desired product. In this research work we focused
on pharmaceutical production processes requiring the culture of
a microorganism population (i.e. bacteria, yeasts or antibiotics).
Several sources of uncertainty may influence the yield of the culture
processes, including (i) low performance and quality of the cultured
microorganism population or (ii) microbial contamination. For
these reasons, robustness is a valuable property for the considered
application context. In particular, a robust schedule will not collapse
immediately when a cell of microorganisms has to be thrown away
due to a microbial contamination. Indeed, a robust schedule should
change locally in small proportions and the overall performance
measure (i.e. makespan, lateness) should change a little if at all.
In this research work we formulated a constraint programming
optimization (COP) model for the robust planning of antibiotics
production. We developed a discrete-time model with a multi-criteria
objective, ordering the different criteria and performing a
lexicographic optimization. A feasible solution of the proposed
COP model is a schedule of a given set of tasks onto available
resources. The schedule has to satisfy tasks precedence constraints,
resource capacity constraints and time constraints. In particular
time constraints model tasks duedates and resource availability
time windows constraints. To improve the schedule robustness, we
modeled the concept of (a, b) super-solutions, where (a, b) are input
parameters of the COP model. An (a, b) super-solution is one in
which if a variables (i.e. the completion times of a culture tasks)
lose their values (i.e. cultures are contaminated), the solution can be
repaired by assigning these variables values with a new values (i.e.
the completion times of a backup culture tasks) and at most b other
variables (i.e. delaying the completion of at most b other tasks).
The efficiency and applicability of the proposed model is
demonstrated by solving instances taken from a real-life
pharmaceutical company. Computational results showed that
the determined super-solutions are near-optimal.
Abstract: The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems.
Abstract: A reconfigurable manufacturing system (RMS) is an
advanced system designed at the outset for rapid changes in its hardware
and software components in order to quickly adjust its production
capacity and functionally. Among various operational decisions, this
study considers the scheduling problem that determines the input
sequence and schedule at the same time for a given set of parts. In
particular, we consider the practical constraints that the numbers of
pallets/fixtures are limited and hence a part can be released into the
system only when the fixture required for the part is available. To
solve the integrated input sequencing and scheduling problems, we
suggest a priority rule based approach in which the two sub-problems
are solved using a combination of priority rules. To show the effectiveness
of various rule combinations, a simulation experiment was
done on the data for a real RMS, and the test results are reported.
Abstract: In this paper, mathematical models for permutation flow shop scheduling and job shop scheduling problems are proposed. The first problem is based on a mixed integer programming model. As the problem is NP-complete, this model can only be used for smaller instances where an optimal solution can be computed. For large instances, another model is proposed which is suitable for solving the problem by stochastic heuristic methods. For the job shop scheduling problem, a mathematical model and its main representation schemes are presented.
Abstract: Artificial Immune System is applied as a Heuristic
Algorithm for decades. Nevertheless, many of these applications
took advantage of the benefit of this algorithm but seldom proposed
approaches for enhancing the efficiency. In this paper, a
Self-evolving Artificial Immune System is proposed via developing
the T and B cell in Immune System and built a self-evolving
mechanism for the complexities of different problems. In this
research, it focuses on enhancing the efficiency of Clonal selection
which is responsible for producing Affinities to resist the invading of
Antigens. T and B cell are the main mechanisms for Clonal
Selection to produce different combinations of Antibodies.
Therefore, the development of T and B cell will influence the
efficiency of Clonal Selection for searching better solution.
Furthermore, for better cooperation of the two cells, a co-evolutional
strategy is applied to coordinate for more effective productions of
Antibodies. This work finally adopts Flow-shop scheduling
instances in OR-library to validate the proposed algorithm.
Abstract: Avionics software is safe-critical embedded software
and its architecture is evolving from traditional federated architectures
to Integrated Modular Avionics (IMA) to improve resource usability.
ARINC 653 (Avionics Application Standard Software Interface) is a
software specification for space and time partitioning in Safety-critical
avionics Real-time operating systems. Arinc653 uses two-level
scheduling strategies, but current modeling tools only apply to simple
problems of Arinc653 two-level scheduling, which only contain time
property. In avionics industry, we are always manually allocating
tasks and calculating the timing table of a real-time system to ensure
it-s running as we design. In this paper we represent an automatically
generating strategy which applies to the two scheduling problems with
dependent constraints in Arinc653 partition run-time environment. It
provides the functionality of automatic generation from the task and partition models to scheduling policy through allocating the tasks to the partitions while following the constraints, and then we design a simulating mechanism to check whether our policy is schedulable or
not
Abstract: In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.
Abstract: This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.
Abstract: The solution algorithm, based on Lagrangian relaxation, a sub-gradient method and a heuristic to find the upper bound of the solution, is proposed to solve the coordinated fleet routing and flight scheduling problems. Numerical tests are performed to evaluate the proposed algorithm using real operating data from two Taiwan airlines. The test results indicate that the solution algorithm is a significant improvement over those obtained with CPLEX, consequently they could be useful for allied airlines to solve coordinated fleet routing and flight scheduling problems.
Abstract: The process of wafer fabrication is arguably the most
technologically complex and capital intensive stage in semiconductor
manufacturing. This large-scale discrete-event process is highly reentrant,
and involves hundreds of machines, restrictions, and
processing steps. Therefore, production control of wafer fabrication
facilities (fab), specifically scheduling, is one of the most challenging
problems that this industry faces. Dispatching rules have been
extensively applied to the scheduling problems in semiconductor
manufacturing. Moreover, lot release policies are commonly used in
this manufacturing setting to further improve the performance of such
systems and reduce its inherent variability. In this work, simulation is
used in the scheduling of re-entrant flow shop manufacturing systems
with an application in semiconductor wafer fabrication; where, a
simulation model has been developed for the Intel Five-Machine Six
Step Mini-Fab using the ExtendTM simulation environment. The
Mini-Fab has been selected as it captures the challenges involved in
scheduling the highly re-entrant semiconductor manufacturing lines.
A number of scenarios have been developed and have been used to
evaluate the effect of different dispatching rules and lot release
policies on the selected performance measures. Results of simulation
showed that the performance of the Mini-Fab can be drastically
improved using a combination of dispatching rules and lot release
policy.
Abstract: Scheduling for the flexible job shop is very important
in both fields of production management and combinatorial
optimization. However, it quit difficult to achieve an optimal solution
to this problem with traditional optimization approaches owing to the
high computational complexity. The combining of several
optimization criteria induces additional complexity and new
problems. In this paper, a Pareto approach to solve the multi
objective flexible job shop scheduling problems is proposed. The
objectives considered are to minimize the overall completion time
(makespan) and total weighted tardiness (TWT). An effective
simulated annealing algorithm based on the proposed approach is
presented to solve multi objective flexible job shop scheduling
problem. An external memory of non-dominated solutions is
considered to save and update the non-dominated solutions during
the solution process. Numerical examples are used to evaluate and
study the performance of the proposed algorithm. The proposed
algorithm can be applied easily in real factory conditions and for
large size problems. It should thus be useful to both practitioners and
researchers.
Abstract: Flow-shop scheduling problem (FSP) deals with the
scheduling of a set of jobs that visit a set of machines in the same
order. The FSP is NP-hard, which means that an efficient algorithm
for solving the problem to optimality is unavailable. To meet the
requirements on time and to minimize the make-span performance of
large permutation flow-shop scheduling problems in which there are
sequence dependent setup times on each machine, this paper
develops one hybrid genetic algorithms (HGA). Proposed HGA
apply a modified approach to generate population of initial
chromosomes and also use an improved heuristic called the iterated
swap procedure to improve initial solutions. Also the author uses
three genetic operators to make good new offspring. The results are
compared to some recently developed heuristics and computational
experimental results show that the proposed HGA performs very
competitively with respect to accuracy and efficiency of solution.
Abstract: Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.
Abstract: Renewable and non-renewable resource constraints have been vast studied in theoretical fields of project scheduling problems. However, although cumulative resources are widespread in practical cases, the literature on project scheduling problems subject to these resources is scant. So in order to study this type of resources more, in this paper we use the framework of a resource constrained project scheduling problem (RCPSP) with finish-start precedence relations between activities and subject to the cumulative resources in addition to the renewable resources. We develop a branch and bound algorithm for this problem customizing precedence tree algorithm of RCPSP. We perform extensive experimental analysis on the algorithm to check its effectiveness and performance for solving different instances of the problem in question.
Abstract: This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems.
Abstract: E-Appointment Scheduling (EAS) has been developed
to handle appointment for UMP students, lecturers in Faculty of
Computer Systems & Software Engineering (FCSSE) and Student
Medical Center. The schedules are based on the timetable and
university activities. Constraints Logic Programming (CLP) has been
implemented to solve the scheduling problems by giving
recommendation to the users in part of determining any available
slots from the lecturers and doctors- timetable. By using this system,
we can avoid wasting time and cost because this application will set
an appointment by auto-generated. In addition, this system can be an
alternative to the lecturers and doctors to make decisions whether to
approve or reject the appointments.