2,092 research outputs found
Simple heuristics for the assembly line worker assignment and balancing problem
We propose simple heuristics for the assembly line worker assignment and
balancing problem. This problem typically occurs in assembly lines in sheltered
work centers for the disabled. Different from the classical simple assembly
line balancing problem, the task execution times vary according to the assigned
worker. We develop a constructive heuristic framework based on task and worker
priority rules defining the order in which the tasks and workers should be
assigned to the workstations. We present a number of such rules and compare
their performance across three possible uses: as a stand-alone method, as an
initial solution generator for meta-heuristics, and as a decoder for a hybrid
genetic algorithm. Our results show that the heuristics are fast, they obtain
good results as a stand-alone method and are efficient when used as a initial
solution generator or as a solution decoder within more elaborate approaches.Comment: 18 pages, 1 figur
Four payment models for the multi-mode resource constrained project scheduling problem with discounted cash flows
In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and outflows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: Lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a
genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it
Models and Strategies for Variants of the Job Shop Scheduling Problem
Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorably to more classical constraint programming methods relying on
propagation algorithms for global unary or cumulative resource constraints and
dedicated search heuristics. In particular, we described an approach that
combines restarting, with a generic adaptive heuristic and solution guided
branching on a simple model based on a decomposition of disjunctive
constraints. In this paper, we introduce an adaptation of this technique for an
important subclass of job shop scheduling problems (JSPs), where the objective
function involves minimization of earliness/tardiness costs. We further show
that our technique can be improved by adding domain specific information for
one variant of the JSP (involving time lag constraints). In particular we
introduce a dedicated greedy heuristic, and an improved model for the case
where the maximal time lag is 0 (also referred to as no-wait JSPs).Comment: Principles and Practice of Constraint Programming - CP 2011, Perugia
: Italy (2011
Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: a dynamic forward approach
Purpose: The issue resource over-allocating is a big concern for project engineers in the process
of scheduling project activities. Resource over-allocating drawback is frequently seen after
scheduling of a project in practice which causes a schedule to be useless. Modifying an
over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a
new and fast tracking method is proposed to schedule large scale projects which can help project
engineers to schedule the project rapidly and with more confidence.
Design/methodology/approach: In this article, a forward approach for maximizing net
present value (NPV) in multi-mode resource constrained project scheduling problem while
assuming discounted positive cash flows (MRCPSP-DCF) is proposed. The progress payment
method is used and all resources are considered as pre-emptible. The proposed approach
maximizes NPV using unscheduled resources through resource calendar in forward mode. For
this purpose, a Genetic Algorithm is applied to solve.
Findings: The findings show that the proposed method is an effective way to maximize NPV in
MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast
and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The
results are then compared with branch and bound method and simulated annealing algorithm and
it is found the proposed genetic algorithm can provide results with better quality. Then algorithm
is then applied for scheduling a hospital in practice.
Originality/value: The method can be used alone or as a macro in Microsoft Office Project®
Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities
after scheduling a project. This can help project engineers to schedule project activities rapidly
with more accuracy in practice.Peer Reviewe
The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review
Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe
Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints
The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Railway scheduling reduces the expected project makespan.
The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (1997), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling.Railway scheduling; Roadrunner scheduling; Feeding buffer; Priority list; Resource availability;
A novel class of scheduling policies for the stochastic resource-constrained project scheduling problem.
We study the resource-constrained project scheduling problem with stochastic activity durations. We introduce a new class of scheduling policies for this problem, which make a number of a-priori sequencing decisions in a pre-processing phase, while the remaining decisions are made dynamically during project execution. The pre-processing decisions entail the addition of precedence constraints to the scheduling instance, hereby resolving some potential resource conflicts. We compare the performance of this new class with existing scheduling policies for the stochastic resource-constrained project scheduling problem, and we observe that the new class is significantly better when the variability in the activity durations is medium to high.Project scheduling; Uncertainty; Stochastic activity durations; Scheduling policies;
A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem
This paper presents a genetic algorithm for the multimode
resource-constrained project scheduling problem (MRCPSP),
in which multiple execution modes are available for each of the
activities of the project. The objective function is the minimization of
the construction project completion time. To solve the problem, is
applied a two-level genetic algorithm, which makes use of two
separate levels and extend the parameterized schedule generation
scheme by introducing an improvement procedure. It is evaluated the
quality of the schedule and present detailed comparative
computational results for the MRCPSP, which reveal that this
approach is a competitive algorithm
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