266 research outputs found
A procedure based on branch-and-bound for the Cyclic Hoist Scheduling Problem
When various kinds of products must receive the same treatments in a production line of tanks and the size of batches is high, a cyclic manufacturing composed of a job from each batch can be scheduled. A hoist ensures the auto-
mated transfer of the jobs between tanks. The probl
em consists in the scheduling of repetitive hoist movements, which is known as CHSP (Cyclic Hoist Scheduling Problem). The objective is to find a sequence which minimizes the cycle time for
jobs from different products. We consider the problem where types of products must be treated and we search an n-cyclic schedule.
The algorithm is based on the resolution of different sequences of products. For each one, a branch-and-bound is solved which considers only coherent subsequences. It enables to reduce the com-
putational times most of the time for instances with 5 tanks and 4 product typesPostprint (published version
Aerospace Manufacturing Industry: A Simulation-Based Decision Support Framework for the Scheduling of Complex Hoist Lines
The hoist scheduling problem is a critical issue in the design and control of Automated Manufacturing Systems. To deal with the major complexities appearing in such problem, this work introduces an advanced simulation model to represent the short-term scheduling of complex hoist lines. The aim is to find the best jobs schedule that minimizing the makespan while maximizing throughput with no defective outputs. Several hard constraints are considered in the model: single shared hoist, heterogeneous recipes, eventual recycles flows, and no buffers between workstations. Different heuristic-based strategies are incorporated into the computer model in order to improve the solutions generated over time. The alternative solutions can be quickly evaluated by using a graphical user interface developed together with the simulation model.Fil: Basán, Natalia Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Pulido, Raul. Universidad Politécnica de Madrid; EspañaFil: Coccola, Mariana Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin
Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards
This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use
A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems
Hoist scheduling has become a bottleneck in electroplating industry
applications with the development of autonomous devices. Although there are a
few approaches proposed to target at the challenging problem, they generally
cannot scale to large-scale scheduling problems. In this paper, we formulate
the hoist scheduling problem as a new temporal planning problem in the form of
adapted PDDL, and propose a novel hierarchical temporal planning approach to
efficiently solve the scheduling problem. Additionally, we provide a collection
of real-life benchmark instances that can be used to evaluate solution methods
for the problem. We exhibit that the proposed approach is able to efficiently
find solutions of high quality for large-scale real-life benchmark instances,
with comparison to state-of-the-art baselines
Optimizing Production Schedule with Energy Consumption and Demand Charges in Parallel Machine Setting
Environmental sustainability concerns, along with the growing need for electricity and associated costs, make energy-cost reduction an inevitable decision-making criterion in production scheduling. In this research, we study the problem of production scheduling on nonidentical parallel machines with machine-dependent processing times and known job release dates to minimize total completion time and energy costs. The energy costs in this study include demand and consumption charges. We present a mixed-integer nonlinear model to formulate the problem. The model is then linearized and its performance is tested through numerical experiments
Intelligent design of manufacturing systems.
The design of a manufacturing system is normally performed in two distinct stages, i.e.
steady state design and dynamic state design. Within each system design stage a variety of
decisions need to be made of which essential ones are the determination of the product
range to be manufactured, the layout of equipment on the shopfloor, allocation of work
tasks to workstations, planning of aggregate capacity requirements and determining the lot
sizes to be processed.
This research work has examined the individual problem areas listed above in order to
identify the efficiency of current solution techniques and to determine the problems
experienced with their use. It has been identified that for each design problem. although
there are an assortment of solution techniques available, the majority of these techniques are
unable to generate optimal or near optimal solutions to problems of a practical size. In
addition, a variety of limitations have been identified that restrict the use of existing
techniques. For example, existing methods are limited with respect to the external
conditions over which they are applicable and/or cannot enable qualitative or subjective
judgements of experienced personnel to influence solution outcomes.
An investigation of optimization techniques has been carried out which indicated that
genetic algorithms offer great potential in solving the variety of problem areas involved in
manufacturing systems design. This research has, therefore, concentrated on testing the use
of genetic algorithms to make individual manufacturing design decisions. In particular, the
ability of genetic algorithms to generate better solutions than existing techniques has been
examined and their ability to overcome the range of limitations that exist with current
solution techniques.
IIFor each problem area, a typical solution has been coded in terms of a genetic algorithm
structure, a suitable objective function constructed and experiments performed to identify
the most suitable operators and operator parameter values to use. The best solution
generated using these parameters has then been compared with the solution derived using a
traditional solution technique. In addition, from the range of experiments undertaken the
underlying relationships have been identified between problem characteristics and optimality
of operator types and parameter values.
The results of the research have identified that genetic algorithms could provide an
improved solution technique for all manufacturing design decision areas investigated. In
most areas genetic algorithms identified lower cost solutions and overcame many of the
limitations of existing techniques
Compilers that learn to optimise: a probabilistic machine learning approach
Compiler optimisation is the process of making a compiler produce better code, i.e. code that,
for example, runs faster on a target architecture. Although numerous program transformations
for optimisation have been proposed in the literature, these transformations are not always beneficial and they can interact in very complex ways. Traditional approaches adopted by compiler
writers fix the order of the transformations and decide when and how these transformations
should be applied to a program by using hard-coded heuristics. However, these heuristics require a lot of time and effort to construct and may sacrifice performance on programs they have
not been tuned for.This thesis proposes a probabilistic machine learning solution to the compiler optimisation problem that automatically determines "good" optimisation strategies for programs. This
approach uses predictive modelling in order to search the space of compiler transformations.
Unlike most previous work that learns when/how to apply a single transformation in isolation or
a fixed-order set of transformations, the techniques proposed in this thesis are capable of tackling the general problem of predicting "good" sequences of compiler transformations. This is
achieved by exploiting transference across programs with two different techniques: Predictive
Search Distributions (PSD) and multi-task Gaussian process prediction (multi-task GP). While
the former directly addresses the problem of predicting "good" transformation sequences, the
latter learns regression models (or proxies) of the performance of the programs in order to
rapidly scan the space of transformation sequences.Both methods, PSD and multi-task GP, are formulated as general machine learning techniques. In particular, the PSD method is proposed in order to speed up search in combinatorial
optimisation problems by learning a distribution over good solutions on a set of problem in¬
stances and using that distribution to search the optimisation space of a problem that has not
been seen before. Likewise, multi-task GP is proposed as a general method for multi-task learning that directly models the correlation between several machine learning tasks, exploiting the
shared information across the tasks.Additionally, this thesis presents an extension to the well-known analysis of variance
(ANOVA) methodology in order to deal with sequence data. This extension is used to address the problem of optimisation space characterisation by identifying and quantifying the
main effects of program transformations and their interactions.Finally, the machine learning methods proposed are successfully applied to a data set that
has been generated as a result of the application of source-to-source transformations to 12 C
programs from the UTDSP benchmark suite
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