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作者：L'Ecuyer, Pierre; Lecot, Christian; Tuffin, Bruno
作者单位：Universite de Montreal; Universite de Montreal; Universite Savoie Mont Blanc; Universite Paris Saclay; Universite de Rennes
摘要：We introduce and study a randomized quasi-Monte Carlo method for the simulation of Markov chains up to a random (and possibly unbounded) stopping time. The method simulates n copies of the chain in parallel, using a (d + 1)-dimensional, highly uniform point set of cardinality n, randomized independently at each step, where d is the number of uniform random numbers required at each transition of the Markov chain. The general idea is to obtain a better approximation of the state distribution, at...
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作者：Axsater, Sven; Marklund, Johan
作者单位：Lund University
摘要：A continuous-review two-echelon inventory system with one central warehouse and a number of nonidentical retailers is considered. The retailers face independent Poisson demand and apply standard (R, Q) policies. The retailer order quantities are fixed integer multiples of a certain batch size, representing the smallest pallet or container size transported in the system. A warehouse order may consist of one or several such batches. We derive a new policy for warehouse ordering, which is optimal...
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作者：Hochbaum, Dorit S.
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：We introduce the pseudoflow algorithm for the maximum-flow problem that employs only pseudoflows and does not generate flows explicitly. The algorithm solves directly a problem equivalent to the minimum-cut problem-the maximum blocking-cut problem. Once the maximum blocking-cut solution is available, the additional complexity required to find the respective maximum-flow is O(m log n). A variant of the algorithm is a new parametric maximum-flow algorithm generating all breakpoints in the same c...
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作者：Gaglioppa, Francesco; Miller, Lisa A.; Benjaafar, Saif
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Target Corporation; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
摘要：We consider the planning and scheduling of production in a multitask/multistage batch manufacturing process typical of industries such as chemical manufacturing, food processing, and oil refining. We allow instances in which multiple sequences of tasks may be used to produce end products. We formulate the problem as a mixed-integer linear program and show that the linear programming relaxation has a large integrality gap and requires significant computational effort to solve to optimality for ...
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作者：Amaral, Andre R. S.
作者单位：Universidade Federal do Espirito Santo
摘要：The one-dimensional facility layout problem is concerned with arranging n departments of given lengths on a line, while minimizing the weighted sum of the distances between all pairs of departments. The problem is NP-hard because it is a generalization of the minimum linear arrangement problem. In this paper, a 0-1 quadratic programming model consisting of only O(n(2)) 0-1 variables is proposed for the problem. Subsequently, this model is cast as an equivalent mixed-integer program and then re...
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作者：Lu, Xiangwen; Song, Jing-Sheng; Zhu, Kaijie
作者单位：Cisco Systems Inc; Cisco USA; Duke University; Shanghai Jiao Tong University; Hong Kong University of Science & Technology
摘要：We consider a multiperiod inventory system of a perishable product with unobservable lost sales. Demand distribution parameters are unknown and are updated periodically using the Bayesian approach based on the censored historical sales data. We develop an explicit expression of the first-order condition for optimality that demonstrates the key trade-off of the problem. The result generalizes partial characterizations of this trade-off in the literature. It shows that the myopic solution is a l...
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作者：Shang, Kevin H.
作者单位：Duke University
摘要：We propose a heuristic for finding base order quantities for stochastic inventory models. The heuristic includes two steps. The first clusters the stages according to cost parameters. The second solves a single-stage problem for each cluster with the original problem data. In a numerical study, we show that the heuristic is near optimal.
