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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.06.034 Byline: T.S. Ng, L.H. Lee, E.P. Chew Keywords: Production planning; Approved vendor matrix; Branch-and-price; Network flows; Heuristics Abstract: We study a production planning problem based on hard-disk drive assembly, where the customers choose their preferred suppliers for pairs of inter-dependent components through the approved vendor matrix. The problem is to develop production plans that minimize the expected total shortage and holding costs while observing the matrix restrictions and limited components supplies. We provide a mixed-integer formulation of the problem, whose linear approximation and relaxation is solved using the column generation method. This is then embedded in a branch-and-price framework to solve the mixed-integer problem. Special branching rules are devised to preserve the structure of the pricing sub-problem at each branch-and-bound node. Finally we present some computational results of our implementation. Author Affiliation: Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260, Singapore Article History: Received 13 September 2004; Accepted 28 June 2005 Article Note: (footnote) [star] This work is sponsored by NUS RP R-266-000-013-112 and NUS RP R-266-000-026-490.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.03.018 Byline: L.H. Lee, C. Lee, J. Bao Keywords: Inventory control; Electronic marketplace; EM policy; Ordering policy Abstract: This paper studies a periodic review inventory model in the presence of an electronic marketplace (EM). Emergency orders can be placed in the EM for additional cost, and excess inventory can be sold to the EM. When the order leadtime from the supplier is one period, the optimal inventory control policy is developed from a dynamic programming model of the problem. The policy is characterized by three critical inventory levels. When the order leadtime from the supplier is longer than one period, an EM policy is developed to determine the quantities of inventory to purchase from and sell to the EM in each period. Based on this EM policy, three ordering policies are proposed to determine the order quantity from the supplier. Numerical results show that significant cost reductions can be obtained by using the EM to adjust the inventory level in each period. The amount of cost reduction is greatly affected by system parameters, especially the order leadtime from the supplier and the costs for transactions in the EM. Author Affiliation: Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260 Singapore, Singapore Article History: Received 4 October 2004; Accepted 15 March 2005

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.11.019 Byline: K.C. Tan (a), Y.H. Chew (a), L.H. Lee (b) Keywords: Vehicle routing; Evolutionary algorithms; Multi-objective optimization Abstract: This paper considers a transportation problem for moving empty or laden containers for a logistic company. Owing to the limited resource of its vehicles (trucks and trailers), the company often needs to sub-contract certain job orders to outsourced companies. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability of trailers. To solve such a multi-objective and multi-modal combinatorial optimization problem, a hybrid multi-objective evolutionary algorithm (HMOEA) featured with specialized genetic operators, variable-length representation and local search heuristic is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multi-objective optimization results as well as to examine the correlations among different variables, such as the number of trucks and trailers, the trailer exchange points, and the utilization of trucks in the routing solutions. It has been shown that the HMOEA is effective in solving multi-objective combinatorial optimization problems, such as finding useful trade-off solutions for the TTVRP routing problem. Author Affiliation: (a) Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, Singapore 117576, Singapore (b) Department of Industrial and System Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore Article History: Received 18 March 2004; Accepted 9 November 2004

In this paper we formulate a particular statistical model validation problem in which we wish to determine the probability that a certain hypothesized parametric uncertainty model is consistent with a given input-output data record. Using a Bayesian approach and ideas from the field of hypothesis testing, we show that in many cases of interest this problem reduces to computing relative weighted volumes of convex sets in [R.sup.N] (where N is the number of uncertain parameters). We also present and discuss a randomized algorithm based on gas kinetics, as well as the existing Hit-and-Run family of algorithms, for probable approximate computation of these volumes.

The structures of different microstrip lines and coplanar waveguides with superconducting ground planes were investigated. This involved the application of an efficient full-wave spectral-domain volume-integral- equation method for the analysis of the complex propagation constant, quality factor, characteristic impedance, current distribution and power-handling capability of the transmission lines. It was found that the structures of microstrip lines are more applicable to high-powered applications compared to coplanar waveguides.

We study a production planning problem of product assembly with random demand, where the customers choose their preferred suppliers for pairs of inter-dependent components through the approved vendor matrix. The problem is to develop production plans that minimise the expected total shortage and holding costs while observing the matrix restrictions and limited component supplies. We provide a mathematical programming formulation of the problem with a large number of decision variables, whose cost function is the solution of a parametric stochastic transportation problem. We present a gradient-based interior-point approach to solve this problem where the gradient is estimated by the shadow price from the solution of such a transportation problem. A column generation scheme is integrated into the approach to handle the large problem issue. Computational results show that our algorithm significantly improves the computational time when compared with the approach without column generation. In addition, we also discuss some extensions of the basic problem to the multi-period rolling horizon case. [ABSTRACT FROM AUTHOR]

A novel scarce-state-transition (SST) type trellis decoding system for (n, n - 1) convolutional codes with coherent BPSK signals is proposed. The new system retains the same number of binary comparisons as the syndrome-former trellis decoding technique. Like the original SST-type encoder trellis technique, the proposed system is also suitable for CMOS VLSI implementation. A combination of the two techniques results in a less complex and low power consumption decoding system.

Byline: L.H. Lee, E.P. Chew, M.S. Sim In this paper, we introduce a revenue management model for an ocean carrier with two classes of orders, namely the ad hoc orders and the contractual orders. When the contractual order is accepted, the carrier can either deliver it immediately or postpone to the next ship on the shipping schedule. This problem is formulated as a stochastic dynamic programming model. We show the existence of the optimal threshold policy. Some important properties of the threshold policy are discussed. Finally, the numerical result shows that the percentage improvement of the threshold policy over an existing policy may be as high as 199%.