53 research outputs found
Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint : heuristic approaches
Job selection and scheduling are among the most important decisions for production planning in today's manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs' processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational tim
A new hybrid meta-heuristic algorithm for solving single machine scheduling problems
A dissertation submitted in partial ful lment of the
degree of Master of Science in Engineering (Electrical) (50/50)
in the
Faculty of Engineering and the Built Environment
Department of Electrical and Information Engineering
May 2017Numerous applications in a wide variety of elds has resulted in a rich history of research
into optimisation for scheduling. Although it is a fundamental form of the problem, the
single machine scheduling problem with two or more objectives is known to be NP-hard.
For this reason we consider the single machine problem a good test bed for solution
algorithms. While there is a plethora of research into various aspects of scheduling
problems, little has been done in evaluating the performance of the Simulated Annealing
algorithm for the fundamental problem, or using it in combination with other techniques.
Speci cally, this has not been done for minimising total weighted earliness and tardiness,
which is the optimisation objective of this work.
If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible
solution schedules. It is thus of de nite practical necessity to reduce the search space in
order to nd an optimal or acceptable suboptimal solution in a shorter time, especially
when scaling up the problem size. This is of particular importance in the application
area of packet scheduling in wireless communications networks where the tolerance for
computational delays is very low. The main contribution of this work is to investigate
the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo
methods before running the Simulated Annealing algorithm on the pruned search space
can result in overall reduced running times.
The search space is divided into a number of sections and Metropolis-Hastings Markov
Chain Monte Carlo is performed over the sections in order to reduce the search space for
Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time
and number of sections of the pre-sampling algorithm, and the run time of Simulated
Annealing for minimising the percentage deviation of the nal result from the optimal
solution cost. Algorithm performance is determined both by computational complexity
and the quality of the solution (i.e. the percentage deviation from the optimal). We
nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation
from the optimal, as compared to the basic Simulated Annealing algorithm on the full
search space. More importantly, we are able to reduce the complexity of nding the
optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to
O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis-
Hastings iterations, r inner samples and m sections.MT 201
Integrating sustainability into production scheduling in hybrid flow-shop environments
Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in
2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop
foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance
of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into
scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of
human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling
problems (HFSP) considering time-of-use electricity pricing, workersâ fexibility, and sequence-dependent setup time (SDST).
Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective
optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution
method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto
evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs
better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality
metrics, providing more versatile and better solutions for production and energy efciency
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