The problem of scheduling in permutation flowshops is considered in this paper with the objectives of minimizing the sum of weighted flowtime/sum of weighted tardiness/sum of weighted flowtime and weighted tardiness/s...
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The problem of scheduling in permutation flowshops is considered in this paper with the objectives of minimizing the sum of weighted flowtime/sum of weighted tardiness/sum of weighted flowtime and weighted tardiness/sum of weighted flowtime, weighted tardiness and weighted earliness of jobs, with each objective considered separately. Lower bounds on the given objective (corresponding to a node generated in the scheduling tree) are developed by solving an assignment problem. branch-and-bound algorithms are developed to obtain the best permutation sequence in each case. Our algorithm incorporates a job-based lower bound (integrated with machine-based bounds) with respect to the weighted flowtime/weighted tardiness/weighted flowtime and weighted tardiness, and a machine-based lower bound with respect to the weighted earliness of jobs. The proposed algorithms are evaluated by solving many randomly generated problems of different problem sizes. The results of an extensive computational investigation for various problem sizes are presented. In addition, one of the proposed branch-and-bound algorithms is compared with a related existing branch-and-bound algorithm. Journal of the Operational Research Society (2009) 60, 991-1004. doi:10.1057/***.2602642
This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and us...
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This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy-Shafer architecture to propagate in join trees;the second is exact and is a modification of Rocha and Cozman's branch-and-bound algorithm, but applied to general directed acyclic graphs, (C) 2006 Elsevier Inc. All rights reserved.
Monte Carlo simulations have long been a widely used method in the industry for control system validation. They provide an accurate probability measure for sufficiently frequent phenomena but are often time-consuming ...
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Monte Carlo simulations have long been a widely used method in the industry for control system validation. They provide an accurate probability measure for sufficiently frequent phenomena but are often time-consuming and may fail to detect very rare events. Conversely, deterministic techniques such as mu or IQC-based analysis allow fast calculation of worst-case stability margins and performance levels, but in the absence of a probabilistic framework, a control system may be invalidated on the basis of extremely rare events. Probabilistic mu-analysis has therefore been studied since the 1990s to bridge this analysis gap by focusing on rare but nonetheless possible situations that may threaten system integrity. The solution adopted in this paper implements a branch-and-bound algorithm to explore the whole uncertainty domain by dividing it into smaller and smaller subsets. At each step, sufficient conditions involving mu upper bound computations are used to check whether a given requirement-related to the delay margin in the present case-is satisfied or violated on the whole considered subset. Guaranteed bounds on the exact probability of delay margin satisfaction or violation are then obtained, based on the probability distributions of the uncertain parameters. The difficulty here arises from the exponential term e-tau s classically used to represent a delay tau, which cannot be directly translated into the Linear Fractional Representation (LFR) framework imposed by mu-analysis. Two different approaches are proposed and compared in this paper to replace the set of delays e-tau s,tau is an element of[0 phi]. First, an equivalent representation using a rational function with unit gain and phase variations that exactly cover those of the original delays, resulting in an LFR with frequency-dependent uncertainty bounds. Then, Pad & eacute;approximations, whose order is chosen to handle the trade-off between conservatism and complexity. A constructive way to derive minimal
Despite the importance of ranked queries in numerous applications involving multi-criteria. decision making, they are not efficiently supported by traditional database systems. In this paper, we propose a simple yet p...
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Despite the importance of ranked queries in numerous applications involving multi-criteria. decision making, they are not efficiently supported by traditional database systems. In this paper, we propose a simple yet powerful technique for processing such queries based on multi-dimensional access methods and branch-and-bound search. The advantages of the proposed methodology are: (i) it is space efficient, requiring only a single index on the given relation (storing each tuple at most once), (ii) it achieves significant (i.e., orders of magnitude) performance gains with respect to the current state-of-the-art, (iii) it can efficiently handle data updates, and (iv) it is applicable to other important variations of ranked search (including the support for non-monotone preference functions), at no extra space overhead. We confirm the superiority of the proposed methods with a detailed experimental study. (C) 2006 Elsevier B.V. All rights reserved.
Distributed processing has been a subject of recent interest due to the availability of computer networks. Over the past few years it has lead to the identification of several challenging problems. One of these is the...
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Distributed processing has been a subject of recent interest due to the availability of computer networks. Over the past few years it has lead to the identification of several challenging problems. One of these is the problem of optimally distributing program modules over a distributed processing system. In this paper we present an LC (Leas Cost) branch-and-bound algorithm to find an optimal assignment that minimizes the sum of execution costs and communication costs. Experimental results show that, for over half of the randomly generated instances, the saving rates exceed 99%. Moreover, it appears that the saving rates rise as the size of the instances increases. Finally, we also introduce two reduction rules to improve the efficiency of the algorithm for some special cases.
We consider the asymmetric capacitated vehicle routing problem (CVRP), a particular case of the standard asymmetric vehicle routing problem in which only the vehicle capacity constraints are imposed. CVRP is known to ...
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We consider the asymmetric capacitated vehicle routing problem (CVRP), a particular case of the standard asymmetric vehicle routing problem in which only the vehicle capacity constraints are imposed. CVRP is known to be NP-hard and finds practical applications in distribution and scheduling. We describe two new bounding procedures for CVRP, based on the so-called additive approach. Each procedure computes a sequence of nondecreasing lower bounds, obtained by solving different relaxations of CVRP. Effective implementations of the procedures are also outlined which considerably reduce the computational effort. The two procedures are combined into an overall bounding algorithm. A branch-and-bound exact algorithm is then proposed, whose performance is enhanced by means of reduction procedures, dominance criteria, and feasibility checks. Extensive computational results on both real-world and random test problems are presented, showing that the proposed approach favorably compares with previous algorithms from the literature.
It is a common vision that connected and automated vehicles (CAVs) will increasingly appear on the road in the near future and share roads with traditional vehicles. Through sharing real-time locations and receiving g...
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It is a common vision that connected and automated vehicles (CAVs) will increasingly appear on the road in the near future and share roads with traditional vehicles. Through sharing real-time locations and receiving guidance from infrastructure, a CAV's arrival and request for green light at intersections can be approximately predicted along their routes. When many CAVs from multiple approaches at intersections place such requests, a central challenge is how to develop an intersection automation policy (IAP) to capture complex traffic dynamics and schedule resources (green lights) to serve both CAV requests (interpreted as request for green lights on a particular signal phase at time t) and traditional vehicles. To represent heterogeneous vehicle movements and dynamic signal timing plans, we first formulate the IAP optimization as a special case of machine scheduling problem using a mixed integer linear programming formulation. Then we develop a novel phase-time-traffic (PTR) hypernetwork model to represent heterogeneous traffic propagation under traffic signal operations. Since the IAP optimization;by nature, is a special sequential decision process, we also develop sequential branch-and-bound search algorithms over time to IAP optimization considering both CAVs and traditional vehicles in the PTR hypernetwork. As the critical part of the branch-and-bound search, special dominance and bounding rules are also developed to reduce the search space and find the exact optimum efficiently. Multiple numerical experiments are conducted to examine the performance of the proposed IAP optimization approach. (C) 2017 Elsevier Ltd. All rights reserved.
We present a branch-and-bound algorithm for the multicommodity location-allocation problem with balancing requirements. Although the formulation displays a number of characteristics common to classical location models...
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We present a branch-and-bound algorithm for the multicommodity location-allocation problem with balancing requirements. Although the formulation displays a number of characteristics common to classical location models, it also unveils, due to the presence of the balancing requirements, a network flow structure that is most favorable to efficient algorithmic developments. In particular, tight bounds may be efficiently computed by using a reformulation of the weak relaxation of the problem as a minimum cost multicommodity flow problem. We also develop and analyse various branching criteria, and show that the branching rules which form the most efficient branch-and-bound procedure for the present problem are quite different from those used to solve classical location problems. The experimentation has been conducted both on several randomly generated problems and on a large scale application.
In this paper, Lipschitz univariate constrained global optimization problems where both the objective function and constraints can be multiextremal are considered. The constrained problem is reduced to a discontinuous...
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In this paper, Lipschitz univariate constrained global optimization problems where both the objective function and constraints can be multiextremal are considered. The constrained problem is reduced to a discontinuous unconstrained problem by the index scheme without introducing additional parameters or variables. A branch-and-bound method that does not use derivatives for solving the reduced problem is proposed. The method either determines the infeasibility of the original problem or finds lower and upper bounds for the global solution. Not all the constraints are evaluated during every iteration of the algorithm, providing a significant acceleration of the search. Convergence conditions of the new method are established. Extensive numerical experiments are presented.
branch-and-bound (B&B) algorithms are time-intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major ...
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ISBN:
(纸本)9781467324229
branch-and-bound (B&B) algorithms are time-intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major complementary way to speed up those methods. The focus is put on the bounding mechanism of B&B algorithms, which is the most time consuming part of their exploration process. We propose a parallel B&B algorithm based on a GPU-accelerated bounding model. The proposed approach concentrate on optimizing data access management to further improve the performance of the bounding mechanism which uses large and intermediate data sets that do not completely fit in GPU memory. Extensive experiments of the contribution have been carried out on well-known FSP benchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtained performances to a single and a multi-threaded CPU-based execution. Accelerations up to x100 are achieved for large problem instances.
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