2,444 research outputs found
A survey of scheduling problems with setup times or costs
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
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Three Essays on Data-Driven Optimization for Scheduling in Manufacturing and Healthcare
This dissertation consists of three essays on data-driven optimization for scheduling in manufacturing and healthcare. In Chapter 1, we briefly introduce the optimization problems tackled in these essays. The first of these essays deals with machine scheduling problems. In Chapter 2, we compare the effectiveness of direct positional variables against relative positional variables computationally in a variety of machine scheduling problems and we present our results. The second essay deals with a scheduling problem in healthcare: the team primary care practice. In Chapter 3, we build upon the two-stage stochastic integer programming model introduced by Alvarez Oh (2015) to solve this challenging scheduling problem of determining patient appointment times to minimize a weighted combination of patient wait and provider idle times for the team practice. To overcome the computational complexity associated with solving the problem under the large set of scenarios required to accurately capture uncertainty in this setting, our approach relies on a lower bounding technique based on solving an exhaustive and mutually exclusive group of scenario subsets. Our computational results identify the structure of optimal schedules and quantify the impact of nurse flexibility, patient crossovers and no-shows. We conclude with practical scheduling guidelines for team primary care practices. The third essay deals with another scheduling problem observed in a manufacturing setting similar to first essay, this time in aerospace industry. In Chapter 4, we propose mathematical models to optimize scheduling at a tactical and operational level in a job shop at an aerospace parts manufacturer and implement our methods using real-life data collected from this company. We generalize the Multi-Level Capacitated Lot-Sizing Problem (MLCLSP) from the literature and use novel computational techniques that depend on the data structure observed to reduce the size of the problem and solve realistically-sized instances in this chapter. We also provide a sensitivity analysis of different modeling techniques and objective functions using key performance indicators (KPIs) important for the manufacturer. Chapter 5 proposes extensions of models and techniques that are introduced in Chapters 2, 3 and 4 and outlines future research directions. Chapter 6 summarizes our findings and concludes the dissertation
Studying the effect of server side constraints on the makespan of the no-wait flow shop problem with sequence dependent setup times.
Peer ReviewedThis paper deals with the problem of scheduling the no-wait flow-shop system with sequence-dependent set-up times and server side-constraints. No-wait constraints state that there should be no waiting time between consecutive operations of jobs. In addition, sequence-dependent set-up times are considered for each operation. This means that the set-up time of an operation on its respective machine is dependent on the previous operation on the same machine. Moreover, the problem consists of server side-constraints i.e. not all machines have a dedicated server to prepare them for an operation. In other words, several machines share a common server. The considered performance measure is makespan. This problem is proved to be strongly NP-Hard. To deal with the problem, two genetic algorithms are developed. In order to evaluate the performance of the developed frameworks, a large number of benchmark problems are selected and solved with different server limitation scenarios. Computational results confirm that both of the proposed algorithms are efficient and competitive. The developed algorithms are able to improve many of the best-known solutions of the test problems from the literature. Moreover, the effect of the server side-constraints on the makespan of the test problems is explained using the computational results
Weighted tardiness minimization for unrelated machines with sequence-dependent and resource-constrained setups
Motivated by the need of quick job (re-)scheduling, we examine an elaborate
scheduling environment under the objective of total weighted tardiness
minimization. The examined problem variant moves well beyond existing
literature, as it considers unrelated machines, sequence-dependent and
machine-dependent setup times and a renewable resource constraint on the number
of simultaneous setups. For this variant, we provide a relaxed MILP to
calculate lower bounds, thus estimating a worst-case optimality gap. As a fast
exact approach appears not plausible for instances of practical importance, we
extend known (meta-)heuristics to deal with the problem at hand, coupling them
with a Constraint Programming (CP) component - vital to guarantee the
non-violation of the problem's constraints - which optimally allocates
resources with respect to tardiness minimization. The validity and versatility
of employing different (meta-)heuristics exploiting a relaxed MILP as a quality
measure is revealed by our extensive experimental study, which shows that the
methods deployed have complementary strengths depending on the instance
parameters. Since the problem description has been obtained from a textile
manufacturer where jobs of diverse size arrive continuously under tight
deadlines, we also discuss the practical impact of our approach in terms of
both tardiness decrease and broader managerial insights
Scheduling on parallel machines with a common server in charge of loading and unloading operations
This paper addresses the scheduling problem on two identical parallel
machines with a single server in charge of loading and unloading operations of
jobs. Each job has to be loaded by the server before being processed on one of
the two machines and unloaded by the same server after its processing. No delay
is allowed between loading and processing, and between processing and
unloading. The objective function involves the minimization of the makespan.
This problem referred to as P2, S1|sj , tj |Cmax generalizes the classical
parallel machine scheduling problem with a single server which performs only
the loading (i.e., setup) operation of each job. For this NP-hard problem, no
solution algorithm was proposed in the literature. Therefore, we present two
mixedinteger linear programming (MILP) formulations, one with completion-time
variables along with two valid inequalities and one with time-indexed
variables. In addition, we propose some polynomial-time solvable cases and a
tight theoretical lower bound. In addition, we show that the minimization of
the makespan is equivalent to the minimization of the total idle times on the
machines. To solve large-sized instances of the problem, an efficient General
Variable Neighborhood Search (GVNS) metaheuristic with two mechanisms for
finding an initial solution is designed. The GVNS is evaluated by comparing its
performance with the results provided by the MILPs and another metaheuristic.
The results show that the average percentage deviation from the theoretical
lower-bound of GVNS is within 0.642%. Some managerial insights are presented
and our results are compared with the related literature.Comment: 40 pages, 4 figures, 16 table
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
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