37,100 research outputs found
Parallel machine scheduling using free software: an application
We will show how to implement large scale optimization by only using freely available software tools. We solve exactly a parallel machine scheduling problem with identical parallel machines and malleable tasks, subject to arbitrary release dates and due dates. The objective is to minimize a function of late work and setup costs. We use the COIN-OR BCP framework to implement column generation to solve a model that results from a Dantzig-Wolfe decomposition,
and also CRIFOR MCFZIB to solve an equivalent network flow model. Computational
results are presented
Flow shop scheduling with earliness, tardiness and intermediate inventory holding costs
We consider the problem of scheduling customer orders in a flow shop with the objective of minimizing the sum of tardiness, earliness (finished goods inventory holding) and intermediate (work-in-process) inventory holding costs. We formulate this problem as an integer program, and based on approximate solutions to two di erent, but closely related, Dantzig-Wolfe reformulations, we develop heuristics to minimize the total cost. We exploit the duality between Dantzig-Wolfe reformulation and Lagrangian relaxation to enhance our heuristics. This combined approach enables us to develop two di erent lower bounds on the optimal integer solution, together with intuitive approaches for obtaining near-optimal feasible integer solutions. To the best of our knowledge, this is the first paper that applies column generation to a scheduling problem with di erent types of strongly NP-hard pricing problems which are solved heuristically. The computational study demonstrates that our algorithms have a significant speed advantage over alternate methods, yield good lower bounds, and generate near-optimal feasible integer solutions for problem instances with many machines and a realistically large number of jobs
The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling
This dissertation discusses two independent topics: a resource-constrained shortest-path problem
(RCSP) and a literature review on scheduling problems involving sequence-dependent setup
(SDS) times (costs).
RCSP is often used as a subproblem in column generation because it can be used to
solve many practical problems. This dissertation studies RCSP with multiple resource
constraints on an acyclic graph, because many applications involve this configuration, especially
in column genetation formulations. In particular, this research focuses on a dynamic RCSP
since, as a subproblem in column generation, objective function coefficients are updated using
new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial
solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic
graph with the goal of effectively reoptimizing RCSP in the context of column generation. This
method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest
path problem (SPP) and then solves the resulting SPP after new values of dual variables are used
to update objective function coefficients (i.e., reduced costs) at each iteration. Network
reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several
coefficients change and for dealing with forbidden and prescribed arcs in the context of a column
generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is
also proposed. Computational tests are designed to show the effectiveness of the proposed
algorithms and procedures.
This dissertation also gives a literature review related to the class of scheduling
problems that involve SDS times (costs), an important consideration in many practical
applications. It focuses on papers published within the last decade, addressing a variety of
machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing
both optimizing and heuristic solution methods in each category. Since lot-sizing is so
intimately related to scheduling, this dissertation reviews work that integrates these issues in
relationship to each configuration. This dissertation provides a perspective of this line of
research, gives conclusions, and discusses fertile research opportunities posed by this class of
scheduling problems.
since, as a subproblem in column generation, objective function coefficients are updated using
new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial
solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic
graph with the goal of effectively reoptimizing RCSP in the context of column generation. This
method uses a one-tim
Parallel machine scheduling with precedence constraints and setup times
This paper presents different methods for solving parallel machine scheduling
problems with precedence constraints and setup times between the jobs. Limited
discrepancy search methods mixed with local search principles, dominance
conditions and specific lower bounds are proposed. The proposed methods are
evaluated on a set of randomly generated instances and compared with previous
results from the literature and those obtained with an efficient commercial
solver. We conclude that our propositions are quite competitive and our results
even outperform other approaches in most cases
Tactical fixed job scheduling with spread-time constraints
We address the tactical fixed job scheduling problem with spread-time constraints.
In such a problem, there are a fixed number of classes of machines and a fixed number of groups of jobs. Jobs of the same group can only be processed by machines of a given set of classes. All jobs have their fixed
start and end times. Each machine is associated with a cost according to its machine class. Machines have spread-time constraints, with which each machine
is only available for L consecutive time units from the start time of the earliest job assigned to it. The objective is to minimize the total cost of the machines used to process all the jobs. For this strongly NP-hard problem, we develop a branch-and-price algorithm, which solves instances with up to 300 jobs, as compared with CPLEX, which cannot solve instances of 100 jobs.
We further investigate the influence of machine flexibility by computational experiments. Our results show that limited machine flexibility is sufficient in most situations
Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling
High performance computing (HPC) systems underwent a significant increase in
their processing capabilities. Modern HPC systems combine large numbers of
homogeneous and heterogeneous computing resources. Scalability is, therefore,
an essential aspect of scientific applications to efficiently exploit the
massive parallelism of modern HPC systems. This work introduces an efficient
version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is
a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in
various domains, such as 3D object recognition, categorization, and 3D face
recognition. EPSIA refers to the extended version of the PSIA that integrates
various well-known dynamic loop scheduling (DLS) techniques. The present work:
(1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the
benefits of applying DLS techniques for optimizing the performance of the PSIA;
(3) Assesses the performance of the proposed EPSIA by conducting several
scalability experiments. The performance results are promising and show that
using well-known DLS techniques, the performance of the EPSIA outperforms the
performance of the PSIA by a factor of 1.2 and 2 for homogeneous and
heterogeneous computing resources, respectively
Scheduling Jobs in Flowshops with the Introduction of Additional Machines in the Future
This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/expert-systems-with-applications/.The problem of scheduling jobs to minimize total weighted tardiness in flowshops,\ud
with the possibility of evolving into hybrid flowshops in the future, is investigated in\ud
this paper. As this research is guided by a real problem in industry, the flowshop\ud
considered has considerable flexibility, which stimulated the development of an\ud
innovative methodology for this research. Each stage of the flowshop currently has\ud
one or several identical machines. However, the manufacturing company is planning\ud
to introduce additional machines with different capabilities in different stages in the\ud
near future. Thus, the algorithm proposed and developed for the problem is not only\ud
capable of solving the current flow line configuration but also the potential new\ud
configurations that may result in the future. A meta-heuristic search algorithm based\ud
on Tabu search is developed to solve this NP-hard, industry-guided problem. Six\ud
different initial solution finding mechanisms are proposed. A carefully planned\ud
nested split-plot design is performed to test the significance of different factors and\ud
their impact on the performance of the different algorithms. To the best of our\ud
knowledge, this research is the first of its kind that attempts to solve an industry-guided\ud
problem with the concern for future developments
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Computer-aided programming for multiprocessing systems
As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error-prone. This report discusses parallel models of computation and tools for computer-aided programming (CAP). Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. In particular, a CAP tool, named Hypertool, is described here. It performs scheduling and handles the communication primitive insertion automatically so that many errors are eliminated. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs. Experiments have shown that up to a 300% performance improvement can be achieved by computer-aided programming
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