Tradeoff Analysis for Optimal Multiobjective Inventory Model

Deterministic inventory model, the economic order quantity (EOQ), reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative

A Study of Cooperative Advertising in a Manufacturer-Retailer Supply Chain

[[abstract]]Cooperative advertising is often defined as an arrangement whereby a manufacturer pays for part of the expenditures of local advertising undertaken by a retailer who is responsible for selling products made by the manufacturer. Besides local advertising, manufacturers are interested in reinforcing the brand image in the eyes of potential consumers by national advertising. In a word, the emphasis of national branding is to create favorable product attitudes in the long run, whereas local advertising is often price-oriented for the short time. This paper employs two game-theoretic models to analyze cooperative advertising problem in a manufacturer-retailer supply chain without considering the ambiguous interaction between national branding and local advertising. Recent change in power shifting from manufacturers to retailers relaxes the leader-follower relationship and leads to a simultaneous-move game. The results show that manufacturer's subsidy policy is affected by the gaming structure. Other interesting results include that the Stackelberg game is Pareto-optimal to the Nash game for the co-op advertising. Comparative statics for Stackelberg game imply that the retailer is either a dependency of the leader or acting independently as she does in Nash game. Our effort calls for game theory to be an essential tool in the analysis of supply chains issues with multiple agents pursuing either conflicting or compatible objectives.[[journaltype]]國內[[incitationindex]]EI[[incitationindex]]TSSC

Evolutionary Pareto optimizers for continuous review stochastic inventory systems

Multi-objective inventory control has been studied for a long time. The trade-off analysis of cycle stock investment and workload, so called the exchange curve concept, possibly dates back to several decades ago. A classical way to such trade-off analysis is to utilize the Lagrangian relaxation technique or interactive method to search for the optimum in a sequence of single objective optimization problems. However, the field of optimization has been changed over the last few decades since the concept of evolutionary computation was introduced. In this paper, a continuous review stochastic inventory system with three objectives about cost and shortage is resolved by evolutionary computation in order to plan for the control policies under backordering and lost sales. Two evolutionary optimizers, multi-objective electromagnetism-like optimization (MOEMO) and multi-objective particle swarm optimization (MOPSO), are employed to well and fast approximate the non-dominated policies in term of lot size and safety stock. Trade-offs are observed in a non-dominated set that no one excels the others in all objectives. Computational results show that the evolutionary Pareto optimizers could generate trade-off solutions potentially ignored by the well-known simultaneous method. Comparisons between the results of backordering and lost sales indicate that decision makers will make more deliberate choices about lot sizing and safety stocking when unsatisfied demand is completely lost.Inventory control Evolutionary computation Multi-objective optimization

THE ASSOCIATION BETWEEN TECHNOLOGY TYPE AND DIFFUSION PATTERN UNDER DUOPOLISTIC COMPETITION

Abstract: Many firms consider adopting new technologies as a means for enhancing competitive advantages. Therefore, the subject of technology diffusion has been studied by many researchers from different disciplines in order to explore the diffusion profiles throughout the industry or the country. The argument has frequently been made that the pattern of diffusion associated with most new technologies will typically have certain characteristics. In general, the diffusion pattern within an industry will depend on the competitive arena and technology characteristics. Based on a duopolistic game-theoretic model, this paper tries to explore the association between technology type and diffusion pattern. The results show that the cost-reducing technologies are adopted sequentially within a duopoly. On the other hard, strategic technologies diffuse over time or in a swarm. Although both technologies might be adopted sequentially, the rate for strategic technologies is faster than that for cost-reducing technologies

Data Augmentation With CycleGAN to Build a Classifier for Novel Defects From the Dicing Stage of Semiconductor Package Assembly

Industry 4.0, a concept first proposed by Germany, has resulted in an increasing number of companies adopting a mass customization strategy. This strategy is widely used across various industries, enabling the production of small batches of diversified products to meet the diverse needs of customers. It encompasses the business process of providing customized goods that best fulfill individual customer requirements, thereby necessitating small-scale production of multiple products. Therefore, the product life cycle of mass customization is much shorter than other production strategies. When product line changes are frequent and customized products have high yield rates, accurately detecting potential defects from a limited number of images is a daunting challenge. If the defect identification classification model needs to maintain a certain level of identification accuracy and the model needs to be deployed quickly, it is impossible to wait until a large number of defect images are collected before deploying an accurate model for new defects. Obtaining a high-precision defect identification classification model is crucial. In this study, we employed the style transfer method of CycleGAN, which takes advantage of the unmatched training images, to successfully transfer the style of defective images from old products to defect-free images of new products. However, CycleGAN requires a large number of images for training, so this study primarily focuses on rare sample categories. We first obtained the defect mask through a semantic segmentation model and then separated the foreground defect from the wafer background using digital image processing techniques. We then copied and pasted the separated defect onto a new wafer background to generate fake defect images. Finally, a generative adversarial network architecture was used to perform image blending to make the fake defect images more natural and realistic. The effectiveness of the data augmentation method was verified through a convolutional neural network model. Through the proposed method in this study, the number of defect images in new products was successfully increased, which helps to deploy a defect identification classification model for new products quickly