80 research outputs found
A Bicriteria Simulated Annealing Algorithm for Scheduling Jobs on Parallel Machines with Sequence Dependent Setup Times
The study considers the scheduling problem of identical parallel machines subject to minimization of the maximum completion time and the maximum tardiness expressed in a linear convex objective function. The maximum completion time or makespan is the date when the last job to be completed leaves the system. The maximum tardiness is indicated by the job that is completed with the longest delay relative its due date. Minimizing both criteria can help assuring a high utilization of the production system as well as a high level of service towards the client. Due to the complexity of the problem, a Simulated Annealing (SA) heuristic has been implemented to be able to obtain an efficient solution in a reasonable running time. A set of n jobs is assigned, to one of the m identical parallel machines. Each job is processed in only one operation before its completion after which it leaves the system. Constraints, such as due dates for each job and setup times for the machines, are considered. The resolution procedure consists of two phases and begins with an initial solution generator. Then a SA heuristic is applied for further improvement of the solution. 4 generators are used to create an initial solution and 3 to generate neighbour solutions. To test and verify the performance of the proposed resolution procedure, a computational experimentation has been realized on a set of test problems generated ad-hoc
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
A Bicriteria Simulated Annealing Algorithm for Scheduling Jobs on Parallel Machines with Sequence Dependent Setup Times
The study considers the scheduling problem of identical parallel machines subject to
minimization of the maximum completion time and the maximum tardiness expressed in a
linear convex objective function. The maximum completion time or makespan is the date
when the last job to be completed leaves the system. The maximum tardiness is indicated by
the job that is completed with the longest delay relative its due date. Minimizing both criteria
can help assuring a high utilization of the production system as well as a high level of service
towards the client. Due to the complexity of the problem, a Simulated Annealing (SA)
heuristic has been implemented to be able to obtain an efficient solution in a reasonable
running time.
A set of n jobs is assigned, to one of the m identical parallel machines. Each job is processed
in only one operation before its completion after which it leaves the system. Constraints,
such as due dates for each job and setup times for the machines, are considered.
The resolution procedure consists of two phases and begins with an initial solution generator.
Then a SA heuristic is applied for further improvement of the solution. 4 generators are used
to create an initial solution and 3 to generate neighbour solutions.
To test and verify the performance of the proposed resolution procedure, a computational
experimentation has been realized on a set of test problems generated ad-hoc
The bi-objective travelling salesman problem with profits and its connection to computer networks.
This is an interdisciplinary work in Computer Science and Operational Research. As it is
well known, these two very important research fields are strictly connected. Among other
aspects, one of the main areas where this interplay is strongly evident is Networking. As far
as most recent decades have seen a constant growing of every kind of network computer connections,
the need for advanced algorithms that help in optimizing the network performances
became extremely relevant. Classical Optimization-based approaches have been deeply studied
and applied since long time. However, the technology evolution asks for more flexible and
advanced algorithmic approaches to model increasingly complex network configurations. In
this thesis we study an extension of the well known Traveling Salesman Problem (TSP): the
Traveling Salesman Problem with Profits (TSPP). In this generalization, a profit is associated
with each vertex and it is not necessary to visit all vertices. The goal is to determine
a route through a subset of nodes that simultaneously minimizes the travel cost and maximizes
the collected profit. The TSPP models the problem of sending a piece of information
through a network where, in addition to the sending costs, it is also important to consider
what “profit” this information can get during its routing. Because of its formulation, the
right way to tackled the TSPP is by Multiobjective Optimization algorithms. Within this
context, the aim of this work is to study new ways to solve the problem in both the exact
and the approximated settings, giving all feasible instruments that can help to solve it, and
to provide experimental insights into feasible networking instances
The 1st International Electronic Conference on Algorithms
This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems
Designing screws for polymer compounding in twin-screw extruders
Tese de doutoramento em Ciência e Engenharia de Polímeros e CompósitosConsidering its modular construction, co-rotating twin screw extruders can be easily adapted to
work with polymeric systems with more stringent specifications. However, their geometrical
flexibility makes the performance of these machines strongly dependent on the screw configuration.
Therefore, the definition of the adequate screw geometry to use in a specific polymer system is an
important process requirement which is currently achieved empirically or using a trial-and-error
basis.
The aim of this work is to develop an automatic optimization methodology able to define the best
screw geometry/configuration to use in a specific compounding/reactive extrusion operation,
reducing both cost and time. This constitutes an optimization problem where a set of different
screw elements are to be sequentially positioned along the screw in order to maximize the extruder
performance.
For that, a global modeling program considering the most important physical, thermal and
rheological phenomena developing along the axis of an intermeshing co-rotating twin screw extruder
was initially developed. The accuracy and sensitivity of the software to changes in the input
parameters was tested for different operating conditions and screw configurations using a
laboratorial Leistritz LSM 30.34 extruder. Then, this modeling software was integrated into an
optimization methodology in order to be possible solving the Twin Screw Configuration Problem.
Multi-objective versions of local search algorithms (Two Phase Local Search and Pareto Local
Search) and Ant Colony Optimization algorithms were implemented and adapted to deal with the
combinatorial, discrete and multi-objective nature of the problem. Their performance was studied
making use of the hypervolume indicator and Empirical Attainment Function, and compared with
the Reduced Pareto Search Genetic Algorithm (RPSGA) previously developed and applied to this
problem. In order to improve the quality of the results and/or to decrease the computational cost
required by the optimization methodology, different hybrid algorithms were tested. The approaches
developed considers the use of local search procedures (TPLS and PLS algorithms) into population
based metaheuristics, as MOACO and MOEA algorithms.
Finally, the optimization methodology developed was applied to the optimization of a starch
cationization reaction. Several starch cationization case studies, involving different screw elements screw lengths and conflicting objectives, were tested in order to validate this technique and to prove
the potential of this automatic optimization methodology.Devido à sua construção modular, as extrusoras de duplo-fuso co-rotativas podem ser facilmente
adaptadas a sistemas poliméricos que requerem especificações mais rigorosas. No entanto, esta
flexibilidade geométrica torna o seu desempenho fortemente dependente da configuração do
parafuso.
Por isso, a tarefa de definir a melhor configuração do parafuso para usar num determinado sistema
polimérico é um requisito importante do processo que é actualmente realizada empiricamente ou
utilizando um processo de tentativa erro.
O objectivo principal deste trabalho é desenvolver uma metodologia automática de optimização que
seja capaz de definir a melhor configuração/geometria do parafuso a usar num determinado
sistema de extrusão, reduzindo custos e tempo. Este problema é um problema de optimização,
onde os vários elementos do parafuso têm que ser sequencialmente posicionados ao longo do eixo
do parafuso de forma a maximizar o desempenho da extrusora.
Para isso, foi inicialmente desenvolvido um programa de modelação que considera os mais
importantes fenómenos físicos, térmicos e reológicos que ocorrem ao longo da extrusora de duplo
fuso co-rotativa. De forma a testar a precisão e a sensibilidade do software às alterações dos
parâmetros, diversas condições operativas e configurações de parafuso foram testadas tendo como
base uma extrusora laboratorial Leistritz LSM 30.34. Seguidamente, este software de modelação
foi integrado numa metodologia de optimização com vista à resolução do problema de
configuração da extrusora de duplo-fuso. Para lidar com a natureza combinatorial, discreta e
multi-objectiva do problema em estudo, foram adaptadas e implementadas versões multi-objectivas
de algoritmos de procura local (Two-Phase Local Search and Pareto Local Search) e Ant Colony
Optimization. O desempenho dos diversos algoritmos foi estudado usando o hipervolume e as
Empirical Attainment Functions. Os resultados foram comparados com os resultados obtidos com o
algoritmo genético Reduced Pareto Search Genetic Algorithm (RPSGA) desenvolvido e aplicado
anteriormente a este problema.
Com o objectivo de melhorar a qualidade dos resultados e/ou diminuir o esforço computacional
exigido pela metodologia de optimização, foram testadas diversas hibridizações. Os algoritmos híbridos desenvolvidos consideram a integração de algoritmos de procura local (TPLS e PLS)
noutras metheuristicas, como MOACO e MOEA.
Por fim, a metodologia de optimização desenvolvida neste trabalho foi testada na optimização de
uma reacção de cationização do amido. Para validar esta técnica e provar o seu potencial, foram
realizados vários estudos envolvendo diferentes elementos e comprimentos de parafusos, bem
como, a optimização de objectivos em conflito
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Tabu search for ship routing and scheduling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2006.This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems
TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING
The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses
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