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348
An inventorylocation model: Formulation, solution algorithm and computational
, 2002
"... Abstract. We introduce a distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the ..."
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Cited by 46 (13 self)
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Abstract. We introduce a distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the use of a fixed cost term. The model is formulated as a nonlinear integerprogramming problem. Model properties are outlined. A Lagrangian relaxation solution algorithm is proposed. By exploiting the structure of the problem we can find a loworder polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation subproblems. A number of heuristics are outlined for finding good feasible solutions. In addition, we describe two variable forcing rules that prove to be very effective at forcing candidate sites into and out of the solution. The algorithms are tested on problems with 88 and 150 retailers. Computation times are consistently below one minute and compare favorably with those of an earlier proposed set partitioning approach for this model (Shen, 2000; Shen, Coullard and Daskin, 2000). Finally, we discuss the sensitivity of the results to changes in key parameters including the fixed cost of placing orders. Significant reductions in these costs might be expected from ecommerce technologies. The model suggests that as these costs decrease it is optimal to locate additional facilities. 1.
The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming
 IN PROC. IJCAI01
, 2001
"... Boolean linear programs (BLPs) are ubiquitous in AI. Satisfiability testing, planning with resource constraints, and winner determination in combinatorial auctions are all examples of this type of problem. Although increasingly wellinformed by work in OR, current AI research has tended to focu ..."
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Cited by 46 (2 self)
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Boolean linear programs (BLPs) are ubiquitous in AI. Satisfiability testing, planning with resource constraints, and winner determination in combinatorial auctions are all examples of this type of problem. Although increasingly wellinformed by work in OR, current AI research has tended to focus on specialized algorithms for each type of BLP task and has only loosely patterned new algorithms on effective methods from other tasks. In this paper we introduce a single generalpurpose local search procedure that can be simultaneously applied to the entire range of BLP problems, without modification. Although one might suspect that a generalpurpose algorithm might not perform as well as specialized algorithms, we find that this is not the case here. Our experiments show that our generic algorithm simultaneously achieves performance comparable with the state of the art in satisfiability search and winner determination in combinatorial auctions two very different BLP problems. Our algorithm is simple, and combines an old idea from OR with recent ideas from AI.
Adaptive Penalty Methods For Genetic Optimization Of Constrained Combinatorial Problems
 INFORMS Journal on Computing
, 1996
"... The application of genetic algorithms (GA) to constrained optimization problems has been hindered by the inefficiencies of reproduction and mutation when feasibility of generated solutions is impossible to guarantee and feasible solutions are very difficult to find. Although several authors have ..."
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Cited by 41 (15 self)
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The application of genetic algorithms (GA) to constrained optimization problems has been hindered by the inefficiencies of reproduction and mutation when feasibility of generated solutions is impossible to guarantee and feasible solutions are very difficult to find. Although several authors have suggested the use of both static and dynamic penalty functions for genetic search, this paper presents a general adaptive penalty technique which makes use of feedback obtained during the search along with a dynamic distance metric. The effectiveness of this method is illustrated on two diverse combinatorial applications; (1) the unequalarea, shapeconstrained facility layout problem and (2) the seriesparallel redundancy allocation problem to maximize system reliability given cost and weight constraints. The adaptive penalty function is shown to be robust with regard to random number seed, parameter settings, number and degree of constraints, and problem instance. 1. Introduction ...
A video compression scheme with optimal bit allocation between displacement vector field and displaced frame difference
 in Proc. IEEE International Conference on Image Processing
, 1997
"... In objectbased video, the encoding of the video data is decoupled into the encoding of shape, motion and texture information, which enables certain functionalities like contentbased interactivity and scalability. However, the problem of how to jointly encode these separate signals to reach the bes ..."
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Cited by 38 (12 self)
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In objectbased video, the encoding of the video data is decoupled into the encoding of shape, motion and texture information, which enables certain functionalities like contentbased interactivity and scalability. However, the problem of how to jointly encode these separate signals to reach the best coding efficiency has never been solved thoroughly. In this paper, we present an operational ratedistortion optimal bit allocation scheme that provides a solution to this problem. Our approach is based on the Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed optimal encoding approach has considerable gains over an adhoc method without optimization. Furthermore the proposed algorithm is much more efficient than exhaustive search. 1.
Dynamic Rate Shaping of Compressed Digital Video
 In Proc. of 2 nd IEEE Intl. Conf. on Image Processing
, 1995
"... We discuss new theoretical and experimental results on the Dynamic Rate Shaping (DRS) approach for transcoding compressed video bitstreams (MPEG1, MPEG2, MPEG4, H.261 as well as JPEG). A set of low complexity algorithms for both constrained and unconstrained DRS are presented. We present the firs ..."
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Cited by 38 (4 self)
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We discuss new theoretical and experimental results on the Dynamic Rate Shaping (DRS) approach for transcoding compressed video bitstreams (MPEG1, MPEG2, MPEG4, H.261 as well as JPEG). A set of low complexity algorithms for both constrained and unconstrained DRS are presented. We present the first extensive experimental study on the various DRS algorithms (causally optimal, memoryless, and ratebased) both in their constrained and generalized forms. The study proves the computational viability of the DRS approach to transcoding and identifies a range of rate shaping ratios for which it is better than requantization, both complexitywise as well as in performance. We then substantiate the almostoptimal experimental performance of the memoryless algorithm by analyzing the behavior of the DRS problem assuming a first order Autoregressive source. By deriving the statistical and ratedistortion characteristics of different components of the interframe rate shaping problem, we offer an explanation as to why the set of optimal breakpoint values for any frame is somewhat invariant to the accumulated motion compensated shaping error from past frames. This result is significant as it opens up the way to construct much simpler memoryless algorithms that give minimal penalty in achieved quality, not just for this, but possibly other types of algorithms. Of equal, if not more, importance is the very first use of matrix perturbation theory for tracking the spectral behavior of the autocorrelation matrix of the source signal and the motion residual it yields. 1 1
Vehicle routing with time windows
 Operations Research
, 1987
"... Operations Research is currently published by INFORMS. ..."
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Cited by 37 (0 self)
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Operations Research is currently published by INFORMS.
Penalty Guided Genetic Search For Reliability Design Optimization
 Computers and Industrial Engineering
, 1996
"... Reliability optimization has been studied in the literature for decades, usually using a mathematical programming approach. Because of these solution methodologies, restrictions on the type of allowable design have been made, however heuristic optimization approaches are free of such binding rest ..."
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Cited by 34 (6 self)
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Reliability optimization has been studied in the literature for decades, usually using a mathematical programming approach. Because of these solution methodologies, restrictions on the type of allowable design have been made, however heuristic optimization approaches are free of such binding restrictions. One difficulty in applying heuristic approaches to reliability design is the highly constrained nature of the problems, both in terms of number of constraints and the difficulty of satisfying constraints. This paper presents a penalty guided genetic algorithm which efficiently and effectively searches over promising feasible and infeasible regions to identify a final, feasible optimal, or near optimal, solution. The penalty function is adaptive and responds to the search history. Results obtained on 33 test problems from the literature dominate previous solution techniques. 1. Introduction to the Redundancy Allocation Problem of Reliability Design Development of new syste...
Scheduling Of Manufacturing Systems Using The Lagrangian Relaxation Technique
 IEEE Transactions on Automatic Control
, 1993
"... Scheduling is one of the most basic but the most difficult problems encountered in the manufacturing industry. Generally, some degree of timeconsuming and impractical enumeration is required to obtain optimal solutions. Industry has thus relied on a combination of heuristics and simulation to solve ..."
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Cited by 34 (9 self)
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Scheduling is one of the most basic but the most difficult problems encountered in the manufacturing industry. Generally, some degree of timeconsuming and impractical enumeration is required to obtain optimal solutions. Industry has thus relied on a combination of heuristics and simulation to solve the problem, resulting in unreliable and often infeasible schedules. Yet, there is a great need for an improvement in scheduling operations in complex and turbulent manufacturing environments. The logical strategy is to find scheduling methods which consistently generate good schedules efficiently. However, it is often difficult to measure the quality of a schedule without knowing the optimum. In this paper, the practical scheduling of three manufacturing environments are examined in the increasing order of complexity. The first problem considers scheduling singleoperation jobs on parallel, identical machines; the second one is concerned with scheduling multipleoperation jobs with simple ...
A Lagrangian Relaxation Network for Graph Matching
 IEEE Trans. Neural Networks
, 1996
"... A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing ..."
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Cited by 31 (7 self)
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A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing. Our approach is in the same spirit as a Lagrangian decomposition approach in that the row and column constraints are satisfied separately with a Lagrange multiplier used to equate the two "solutions." Due to the unavoidable symmetries in graph isomorphism (resulting in multiple global minima), we add a symmetrybreaking selfamplification term in order to obtain a permutation matrix. With the application of a fixpoint preserving algebraic transformation to both the distance measure and selfamplification terms, we obtain a Lagrangian relaxation network. The network performs minimization with respect to the Lagrange parameters and maximization with respect to the permutation matrix variable...