1,135 research outputs found

    Throughput Rate Optimization in High Multiplicity Sequencing Problems

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    Mixed model assembly systems assemble products (parts) of differenttypes in certain prespecified quantities. A minimal part set is a smallestpossible set of product type quantities, to be called the multiplicities,in which the numbers of assembled products of the various types are inthe desired ratios. It is common practice to repeatedly assemble minimalpart sets, and in such a way that the products of each of the minimalpart sets are assembled in the same sequence. Little is known howeverregarding the resulting throughput rate, in particular in comparison to thethroughput rates attainable by other input strategies. This paper investigatesthroughput and balancing issues in repetitive manufacturing environments.It considers sequencing problems that occur in this setting andhow the repetition strategy influences throughput. We model the problemsas a generalization of the traveling salesman problem and derive ourresults in this general setting. Our analysis uses well known concepts fromscheduling theory and combinatorial optimization.Economics ;

    Cyclic Lot-Sizing Problems with Sequencing Costs

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    We study a single machine lot-sizing problem, where n types of products need to be scheduled on the machine. Each product is associated with a constant demand rate, maximum production rate and inventory costs per time unit. Every time when the machine switches production between products, sequencing costs are incurred. These sequencing costs depend both on the product the machine just produced and the product the machine is about to produce. The goal is to find a cyclic schedule minimizing total average costs, subject to the condition that all demands are satisfied. We establish the complexity of the problem and we prove a number of structural properties largely characterizing optimal solutions. Moreover, we present two algorithms approximating the optimal schedules by augmenting the problem input. Due to the high multiplicity setting, even trivial cases of the corresponding conventional counterparts become highly non-trivial with respect to the output sizes and computational complexity, even without sequencing costs. In particular, the length of an optimal solution can be exponential in the input size of the problem. Nevertheless, our approximation algorithms produce schedules of a polynomial length and with a good quality compared to the optimal schedules of exponential length

    A polynomial time algorithm for makespan minimization on one machine with forbidden start and completion times

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    International audienceWe consider the problem of scheduling independent jobs on a single resource under a special unavailability constraint: a set of forbidden instants is given, where no job is allowed to start or complete. We show that a schedule without idle time always exists if the number of forbidden instants is less than the number of distinct processing times appearing in the instance. We derive quite a fast algorithm to find such a schedule, based on an hybridization between a list algorithm and local exchange. As a corollary minimizing the makespan for a fixed number of forbidden instants is polynomia

    Throughput rate optimization in high multiplicity sequencing problems

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    Mixed model assembly systems assemble products (parts) of different types in certain prespecified quantities. A minimal part set is a small-est possible set of product type quantities, to be called the multiplicities, in which the numbers of assembled products of the various types are in the desired ratios. It is common practice to repeatedly assemble minimal part sets, and in such a way that the products of each of the minimal part sets are assembled in the same sequence. Little is known however regarding the resulting throughput rate, in particular in comparison to the throughput rates attainable by other input strategies. This paper inves-tigates throughput and balancing issues in repetitive manufacturing envi-ronments. It considers sequencing problems that occur in this setting and how the repetition strategy influences throughput. We model the prob-lems as a generalization of the traveling salesman problem and derive our results in this general setting. Our analysis uses well known concepts from scheduling theory and combinatorial optimization

    Decentralized Resource Scheduling in Grid/Cloud Computing

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    In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution
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