72,625 research outputs found
Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach
Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.postprin
Scheduling over Scenarios on Two Machines
We consider scheduling problems over scenarios where the goal is to find a
single assignment of the jobs to the machines which performs well over all
possible scenarios. Each scenario is a subset of jobs that must be executed in
that scenario and all scenarios are given explicitly. The two objectives that
we consider are minimizing the maximum makespan over all scenarios and
minimizing the sum of the makespans of all scenarios. For both versions, we
give several approximation algorithms and lower bounds on their
approximability. With this research into optimization problems over scenarios,
we have opened a new and rich field of interesting problems.Comment: To appear in COCOON 2014. The final publication is available at
link.springer.co
Minimizing value-at-risk in the single-machine total weighted tardiness problem
The vast majority of the machine scheduling literature focuses on deterministic
problems, in which all data is known with certainty a priori. This may be a reasonable assumption when the variability in the problem parameters is low. However, as variability in the parameters increases incorporating this uncertainty explicitly into a scheduling model is essential to mitigate the resulting adverse effects. In this paper, we consider the celebrated single-machine total weighted tardiness (TWT) problem in the presence of uncertain problem parameters. We impose a probabilistic constraint on the random TWT and introduce a risk-averse stochastic programming model. In particular, the objective of the proposed model is to find a non-preemptive static job processing sequence that minimizes the value-at-risk (VaR) measure on the random
TWT at a specified confidence level. Furthermore, we develop a lower bound on the optimal VaR that may also benefit alternate solution approaches in the future. In this study, we implement a tabu-search heuristic to obtain reasonably good feasible solutions and present results to demonstrate the effect of the risk parameter and the value of the proposed model with respect to a corresponding risk-neutral approach
Single machine scheduling problems with uncertain parameters and the OWA criterion
In this paper a class of single machine scheduling problems is discussed. It
is assumed that job parameters, such as processing times, due dates, or weights
are uncertain and their values are specified in the form of a discrete scenario
set. The Ordered Weighted Averaging (OWA) aggregation operator is used to
choose an optimal schedule. The OWA operator generalizes traditional criteria
in decision making under uncertainty, such as the maximum, average, median or
Hurwicz criterion. It also allows us to extend the robust approach to
scheduling by taking into account various attitudes of decision makers towards
the risk. In this paper a general framework for solving single machine
scheduling problems with the OWA criterion is proposed and some positive and
negative computational results for two basic single machine scheduling problems
are provided
The robust single machine scheduling problem with uncertain release and processing times
In this work, we study the single machine scheduling problem with uncertain
release times and processing times of jobs. We adopt a robust scheduling
approach, in which the measure of robustness to be minimized for a given
sequence of jobs is the worst-case objective function value from the set of all
possible realizations of release and processing times. The objective function
value is the total flow time of all jobs. We discuss some important properties
of robust schedules for zero and non-zero release times, and illustrate the
added complexity in robust scheduling given non-zero release times. We propose
heuristics based on variable neighborhood search and iterated local search to
solve the problem and generate robust schedules. The algorithms are tested and
their solution performance is compared with optimal solutions or lower bounds
through numerical experiments based on synthetic data
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄŤnĂ práce je zaměřena na optimalizaci prĹŻbÄ›hu pracovnĂch operacĂ v logistickĂ˝ch skladech a distribuÄŤnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plánovánĂ, rozvrhovánĂ a odbavovánĂ. JelikoĹľ jde o problĂ©m patĹ™ĂcĂ do tĹ™Ădy sloĹľitosti NP-teĹľkĂ˝, je vĂ˝poÄŤetnÄ› velmi nároÄŤnĂ© nalĂ©zt optimálnĂ Ĺ™ešenĂ. MotivacĂ pro Ĺ™ešenĂ tĂ©to práce je vyplnÄ›nĂ pomyslnĂ© mezery mezi metodami zkoumanĂ˝mi na vÄ›deckĂ© a akademickĂ© pĹŻdÄ› a metodami pouĹľĂvanĂ˝mi v produkÄŤnĂch komerÄŤnĂch prostĹ™edĂch. Jádro optimalizaÄŤnĂho algoritmu je zaloĹľeno na základÄ› genetickĂ©ho programovánĂ Ĺ™ĂzenĂ©ho bezkontextovou gramatikou. HlavnĂm pĹ™Ănosem tĂ©to práce je a) navrhnout novĂ˝ optimalizaÄŤnĂ algoritmus, kterĂ˝ respektuje následujĂcĂ optimalizaÄŤnĂ podmĂnky: celkovĂ˝ ÄŤas zpracovánĂ, vyuĹľitĂ zdrojĹŻ, a zahlcenĂ skladovĂ˝ch uliÄŤek, kterĂ© mĹŻĹľe nastat bÄ›hem zpracovánĂ ĂşkolĹŻ, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacĂch pĹ™ĂkladĹŻ, kterĂ© mohou slouĹľit jako referenÄŤnĂ vĂ˝sledky pro dalšà vĂ˝zkum, a dále c) pokusit se pĹ™edÄŤit stanovenĂ© referenÄŤnĂ vĂ˝sledky dosaĹľenĂ© kvalifikovanĂ˝m a trĂ©novanĂ˝m operaÄŤnĂm manaĹľerem jednoho z nejvÄ›tšĂch skladĹŻ ve stĹ™ednĂ EvropÄ›.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
Analysis, classification and comparison of scheduling techniques for software transactional memories
Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated
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