Anulark Techanitisawad

Simultaneous price and due date settings for multiple customer classes

Extending the model of [Eur. J. Oper. Res. 116 (2) (1999) 305] that, under contingent capacity, simultaneously optimizes the bidding price and due date for each incoming order, we propose a bidding model with multiple customer segments classified based on parameters of willingness to pay, sensitivity to short delivery time, quality level requirement, and intensity of competition. The winning probability function was also modified to be of more practical and robust model in reflecting stochastic nature of customers decision. Two sequencing rules, namely the early-due-date (EDD) for time-critical orders and first-come-first-serve (FCFS) for regular orders, were applied to determine the sequencing position of each incoming order, and a simplified pattern search algorithm was used to improve the efficiency in searching for optimal price and due date. The simulation results show that, in general, our proposed model and method can significantly increase the marginal revenue to the firm.

A genetic algorithm for the multi-class contingent bidding model

Abstract.This paper proposes a Genetic Algorithm (GA) in searching for a near-optimal sequence of jobs in a make-to-order (MTO) production system in order to maximize the average marginal revenue earned per bid in the bidding model that allows contingent orders. Even though the complexity of the sequencing problem is NP-hard by nature, it is found to be a key determinant in improving the capacity allocation and the expected tardiness cost for an arriving order. The model incorporates operational constraints and marketing policies to effectively reflect the interests of customers. A simulation study was conducted to analyze the relative performance of the proposed system in a finite horizon. The results show the significant impact of the ordering sequence on the average marginal revenue and that the GA is an effective and efficient method to search for a good sequence and can improve the profit margin of the MTO firm and satisfaction of its customers.

An application of the neural network energy function to machine sequencing

Abstract.We apply a neural network approach for solving a one-machine sequencing problem to minimize either single- or multi-objectives, namely the total tardiness, total flowtime, maximimum tardiness, maximum flowtime, and number of tardy jobs. We formulate correspondingly nonlinear integer models, for each of which we derive a quadratic energy function, a neural network, and a system of differential equations. Simulation results based on solving the nonlinear differential equations demonstrate that our approach can effectively solve the sequencing problems to optimality in most cases and near optimality in a few cases. The neural network approach can also be implemented on a parallel computing network, resulting in significant runtime savings over the optimization approach.

A coordinated planning model with demand uncertainty

This paper develops a coordinated planning model for production and order planning for single-manufacturer and single-retailers, and single-manufacturer and two-retailers. The application is for products with short lifecycles, such as fashion goods. The demand is assumed to be random with a normal distribution, and is linearly correlated in the case of two retailers. The coordinated policy is compared with a ‘returns’ policy model where the manufacturer buys back all the unsold items at a pre-specified price. Order quantities and profits for manufacturer and retailer are derived for base model, returns policy model, and for coordinated planning model, all cast as a newsvendor problem. Results show that the expected total profits are higher for coordinated model than for other models. A compensation or profit sharing scheme is then suggested based on returns policy and it is shown that the coordinated model with such sharing yields a ‘win-win’ situation for both suppliers and retailers. Numerical results are presented to illustrate the profit patterns under various conditions. The model shows that coordination of such planning in supply chains has considerable economic benefits.