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32nd IEEE Conference on Decision and Control

作者： GU, GX LOUISIANA STATE UNIV DEPT ELECT & COMP ENGNBATON ROUGELA 70803

ISBN: (纸本)0780312988

New algorithms based on convex programming are proposed for worst case system identification. The algorithms are optimal within a factor of two asymptotically. Further, model validation, or data consistency is embedded in the identification process. Explicit worst case identification error bounds in H/sup /spl infin// norm are also derived for both uniformly and nonuniformly spaced frequency response samples.

关键词： identification convex programming frequency response computational complexity approximation theory suboptimal algorithms worst case identification model validation convex programming system identification data consistency error bounds frequency response samples approximation theory computational complexity

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Technical Note-Near-Optimal Bayesian Online Assortment of Reusable Resources

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OPERATIONS RESEARCH 2024年第5期72卷 iii-vii, 1751-2261, C2-C3页

作者： Feng, Yiding Niazadeh, Rad Saberi, Amin Univ Chicago Booth Sch Business Chicago IL 60637 USA Stanford Univ Management Sci & Engn Stanford CA 94305 USA

Motivated by the applications of rental services in e-commerce, we consider revenue maximization in online assortment of reusable resources for a stream of arriving consumers with different types. We design competitive online algorithms with respect to the optimum online policy in the Bayesian setting in which types are drawn independently from known heterogeneous distributions over time. In the regime where the minimum of initial inventories cmin is large, our main result is a near-optimal 1. min 12, root log(c(min))=c(min). p. competitive algorithm for the general case of reusable resources. Our algorithm relies on an expected LP benchmark for the problem, solves this LP, and simulates the solution through an independent randomized rounding. The main challenge is obtaining point-wise inventory feasibility in a computationally efficient fashion from these simulation-based algorithms. To this end, we use several technical ingredients to design discarding policies-one for each resource. These policies handle the trade-off between the inventory feasibility under reusability and the revenue loss of each of the resources. However, discarding a unit of a resource changes the future consumption of other resources. To handle this new challenge, we also introduce postprocessing assortment procedures that help with designing and analyzing our discarding policies as they run in parallel, which might be of independent interest. As a side result, by leveraging techniques from the literature on prophet inequality, we further show an improved near-optimal 1. 1= root c(min) + 3v competitive algorithm for the special case of nonreusable resources. We finally evaluate the performance of our algorithms using the numerical simulations on the synthetic data.

关键词： analysis of algorithms suboptimal algorithms inventory/production approximations/heuristics

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Update with out-of-sequence measurements in tracking: Exact solution

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2002年第3期38卷 769-778页

作者： Bar-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

In target tracking systems measurements are typically collected in "scans" or "frames" and then they are transmitted to a processing center. In multisensor tracking systems that operate in a centralized manner, there are usually different time delays in transmitting the scans or frames from the various sensors to the center. This can lead to situations where measurements from the same target arrive out of sequence. Such "out-of-sequence" measurement (OOSM) arrivals can occur even in the absence of scan/frame communication time delays. The resulting "negative-time measurement update" problem, which is quite common in real multisensor systems, was solved previously only approximately in the literature. The exact state update equation, for such a problem is presented. The optimal and two suboptimal algorithms are compared on a number of realistic examples, including a GMTI (ground moving target indicator) radar case.

关键词： delays radar tracking sensor fusion target tracking GMTI radar ground moving target indicator multisensor tracking systems negative-time measurement update problem optimal algorithms out-of-sequence measurements suboptimal algorithms target tracking syst Multisensor systems Optimization algorithms Sensor fusion Moving target detector radar target tracking Target tracking delay

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Dynamic Pricing (and Assortment) Under a Static Calendar

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MANAGEMENT SCIENCE 2021年第4期67卷 2292-2313页

作者： Ma, Will Simchi-Levi, David Zhao, Jinglong Columbia Univ Grad Sch Business New York NY 10027 USA MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Operat Res Ctr 77 Massachusetts Ave Cambridge MA 02139 USA

This work is motivated by our collaboration with a large consumer packaged goods (CPG) company. We have found that whereas the company appreciates the advantages of dynamic pricing, they deem it operationally much easier to plan out a static price calendar in advance. We investigate the efficacy of static control policies for revenue management problems whose optimal solution is inherently dynamic. In these problems, a firm has limited inventory to sell over a finite time horizon, over which heterogeneous customers stochastically arrive. We consider both pricing and assortment controls, and derive simple static policies in the form of a price calendar or a planned sequence of assortments, respectively. In the assortment planning problem, we also differentiate between the static vs. dynamic substitution models of customer demand. We show that our policies are within 1-1/e (approximately 0.63) of the optimum under stationary demand, and 1/2 of the optimum under nonstationary demand, with both guarantees approaching 1 if the starting inventories are large. We adapt the technique of prophet inequalities from optimal stopping theory to pricing and assortment problems, and our results are relative to the linear programming relaxation. Under the special case of stationary demand single-item pricing, our results improve the understanding of irregular and discrete demand curves, by showing that a static calendar can be (1-1/e)-approximate if the prices are sorted high-to-low. Finally, we demonstrate on both data from the CPG company and synthetic data from the literature that our simple price and assortment calendars are effective.

关键词： analysis of algorithms suboptimal algorithms inventory-production policies pricing empirical pricing

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An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search

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ANNALS OF OPERATIONS RESEARCH 1997年第s期70卷 281-306页

作者： DauzerePeres, S Paulli, J ECOLE MINES DEPT AUTOMAT CONTROL & PROD ENGN F-44070 NANTES 03 FRANCE AARHUS UNIV DEPT OPERAT RES DK-8000 AARHUS C DENMARK

The problem considered in this paper is an important extension of the classical job-shop scheduling problem, where the same operation can be performed on more than one machine. The problem is to assign each operation to a machine and to sequence the operations on the machines, such that the makespan of a set of jobs is minimized. We introduce an extended version of the disjunctive graph model, that is able to take into account the fact that operations have to be assigned to machines. This allows us to present an integrated approach, by defining a neighborhood structure for the problem where there is no distinction between reassigning or resequencing an operation. This neighborhood is proved to be connected. A tabu search procedure is proposed and computational results are provided.

关键词： analysis of algorithms suboptimal algorithms tabu search production/scheduling sequencing deterministic multiple machine multiprocessor job-shop scheduling disjunctive graph model

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Maximum commonality problems: Applications and analysis

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MANAGEMENT SCIENCE 2008年第1期54卷 194-207页

作者： Dawande, Milind Kumar, Subodha Mookerjee, Vijay Sriskandarajah, Chelliah Univ Texas Dallas Sch Management Richardson TX 75083 USA Univ Washington Michael G Foster Sch Business Seattle WA 98195 USA

Recently, an agile software development technique called extreme programming has caught the attention of practitioners and researchers in the software industry. A core practice of extreme programming is pair programming, where two developers work on the same piece of code. We introduce the problem of assigning pairs of developers to modules so as to maximize the commonality -a measure of the extent to which common developers work on related modules -subject to a load-balancing constraint that is motivated by the need to control the completion time of the project. We consider two variants of this problem. In MCAP(n), a developer is teamed up with exactly one other developer to form a pair that works together for the entire duration of the project. In MCAP(s), we allow a developer to pair with more than one other developer during the project. This "pair-splitting" version of the problem facilitates knowledge dissemination among developers, but can increase the effort needed for a developer to adjust to the work habits of several partners. The difference between the commonality achieved with and without pair splitting crucially depends on the underlying structure of the problem. For trees, we show that the value of the maximum commonality is the same for both MCAPn and MCAPs. Additionally, we obtain polynomial-time algorithms for both of these variants. For general graphs, both problems MCAPn and MCAPs are shown to be strongly NP-complete. We prove that the maximum commonality for MCAPs is at most 3 2 times the maximum commonality of MCAPn. We also provide polynomial-time algorithms and approximation results for a number of special cases of these problems.

关键词： analysis of algorithms computational complexity integer programming applications suboptimal algorithms

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A 5/3 approximation algorithm for the clustered traveling salesman tour and path problems

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OPERATIONS RESEARCH LETTERS 1999年第1-2期24卷 29-35页

作者： Anily, S Bramel, J Hertz, A Columbia Univ Grad Sch Business New York NY 10027 USA Tel Aviv Univ Fac Management IL-69978 Tel Aviv Israel Ecole Polytech Fed Lausanne Dept Math Lausanne Switzerland

We consider the ordered cluster traveling salesman problem (OCTSP). In this problem, a vehicle starting and ending at a given depot must visit a set of n points. The points are partitioned into K, K less than or equal to n, prespecified clusters. The vehicle must first visit the points in cluster 1, then the points in cluster 2,..., and finally the points in cluster K so that the distance traveled is minimized. We present a 5/3-approximation algorithm for this problem which runs in O(n(3)) time. We show that our algorithm can also be applied to the path version of the OCTSP: the ordered cluster traveling salesman path problem (OCTSPP). Here the (different) starting and ending points of the vehicle may or may not be prespecified. For this problem, our algorithm is also a 5/3-approximation algorithm. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： analysis of algorithms suboptimal algorithms transportation vehicle routing

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Energy-efficient user grouping algorithms for power minimisation in multi-carrier code division multiple access systems

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IET COMMUNICATIONS 2011年第18期5卷 2640-2647页

作者： Phasouliotis, A. So, D. K. C. Prawatmuang, W. Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

Energy efficiency has become increasingly important in wireless communications nowadays. Saving energy will not only reduce operating cost but also reduces greenhouse gas emissions, which is important for combating climate change. The authors propose energy-efficient user grouping algorithms to provide power minimisation of grouped multi-carrier code division multiple access (MC-CDMA) and space-time block coding MC-CDMA systems in a cellular environment. Depending on the channel fading conditions, power control is utilised to minimise the total transmitted power under a bit error rate constraint. When the allocation is performed without a fair data rate requirement, the authors provide the optimal solution to the minimisation problem. However, when some fairness is considered, the optimal solution requires high computational complexity. Thus, the authors solve the problem by proposing two suboptimal algorithms. Simulation results illustrate a significantly reduced power consumption in comparison with other techniques.

关键词： minimisation problem power control radiocommunication minimisation optimal solution operating cost Optimisation techniques channel fading conditions bit error rate constraint energy-efficient user grouping algorithms error statistics Codes suboptimal algorithms climate change fading channels code division multiple access energy conservation energy efficiency grouped multicarrier code division multiple access Multiple access communication power consumption wireless communications space-time block coding MC-CDMA systems greenhouse gas emissions transmitted power multicarrier code division multiple access systems cellular environment fair data rate requirement space-time block codes computational complexity power minimisation Radio links and equipment Other topics in statistics

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The Assortment Packing Problem: Multiperiod Assortment Planning for Short-Lived Products

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MANAGEMENT SCIENCE 2014年第11期60卷 2701-2721页

作者： Caro, Felipe Martinez-de-Albeniz, Victor Rusmevichientong, Paat Univ Calif Los Angeles UCLA Anderson Sch Management Los Angeles CA 90095 USA Univ Navarra IESE Business Sch Barcelona 08034 Spain Univ So Calif USC Marshall Sch Business Los Angeles CA 90089 USA

Motivated by retailers' frequent introduction of new items to refresh product lines and maintain their market shares, we present the assortment packing problem in which a firm must decide, in advance, the release date of each product in a given collection over a selling season. Our formulation models the trade-offs among profit margins, preference weights, and limited life cycles. A key aspect of the problem is that each product is short-lived in the sense that, once introduced, its attractiveness lasts only a few periods and vanishes over time. The objective is to determine when to introduce each product to maximize the total profit over the selling season. Even for two periods, the corresponding optimization problem is shown to be NP-complete. As a result, we study a continuous relaxation of the problem that approximates the problem well, when the number of products is large. When margins are identical and product preferences decay exponentially, its solution can be characterized: it is optimal to introduce products with slower decays earlier. The structural properties of the relaxation also help us to develop several heuristics, for which we establish performance guarantees. We test our heuristics with data on sales and release dates of women's handbags from an accessories retailer. The numerical experiments show that the heuristics perform very well and can yield significant improvements in profitability.

关键词： analysis of algorithms suboptimal algorithms marketing product policy fractional programming attraction model assortment optimization

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Capacity Constraints Across Nests in Assortment Optimization Under the Nested Logit Model

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OPERATIONS RESEARCH 2015年第4期63卷 812-822页

作者： Feldman, Jacob B. Topaloglu, Huseyin Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

We consider assortment optimization problems when customers choose according to the nested logit model and there is a capacity constraint limiting the total capacity consumption of all products offered in all nests. When each product consumes one unit of capacity, our capacity constraint limits the cardinality of the offered assortment. For the cardinality constrained case, we develop an efficient algorithm to compute the optimal assortment. When the capacity consumption of each product is arbitrary, we give an algorithm to obtain a 4-approximate solution. We show that we can compute an upper bound on the optimal expected revenue for an individual problem instance by solving a linear program. In our numerical experiments, we consider problem instances involving products with arbitrary capacity consumptions. Comparing the expected revenues from the assortments obtained by our 4-approximation algorithm with the upper bounds on the optimal expected revenues, our numerical results indicate that the 4-approximation algorithm performs quite well, yielding less than 2% optimality gap on average.

关键词： analysis of algorithms suboptimal algorithms marketing choice models

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