Single Machine Scheduling with Time-Dependent Learning Effect and Non-Linear Past-Sequence-Dependent Setup Times

This paper studies a single machine scheduling problem with time-dependent learning and setup times. Time-dependent learning means that the actual processing time of a job is a function of the sum of the normal processing times of the jobs already scheduled. The setup time of a job is proportional to the length of the already processed jobs, that is, past-sequence-dependent (psd) setup time. We show that the addressed problem remains polynomially solvable for the objectives, i.e., minimization of the total completion time and minimization of the total weighted completion time. We also show that the smallest processing time (SPT) rule provides the optimum sequence for the addressed problem.

The Application of Innovative Automobile Maintenance Services from Customers’ Viewpoints: A Hybrid Method

Company experts and academicianshave paid significant attention to issues of new product/service development. However, few studies have been carried outtodiscuss innovative product/service development mindful of the competitive positions between competitors. This study introduces a hybrid method of positioning analysis, conjoint analysis and rough set theory to understand the competition positions and facilitate innovative product/service development from the customers’ perspective. The hybrid method is also supported by in-depth interviewing, factor analysis, preference regression, ideas simulation, ideas selection, and specific weight valuation methods. We choose the automobile maintenance industry in Taiwan, whose objective is to improve product/service qualities and enhance customers’ satisfaction and loyalty.This is also the subject of our empirical study. The results show that the proposed hybrid method is effective for innovative product/service development. Moreover, the empirical findings provide useful information for automobile maintenance providers so that they may be better able to pay attention to their competitive positions and their customers’ preferences, and better able to facilitate their innovative automobile maintenance service development, in order to achieve sustainable competitive advantages.

Adjusted Reasoning Module for Deep Visual Question Answering Using Vision Transformer

Visual Question Answering(VQA)is an interdisciplinary artificial intelligence(AI)activity that integrates com-puter vision and natural language processing.Its purpose is to empower machines to respond to questions by utilizing visual information.A VQA system typically takes an image and a natural language query as input and produces a textual answer as output.One major obstacle in VQA is identifying a successful method to extract and merge textual and visual data.We examine“Fusion”Models that use information from both the text encoder and picture encoder to efficiently perform the visual question-answering challenge.For the transformer model,we utilize BERT and RoBERTa,which analyze textual data.The image encoder designed for processing image data utilizes ViT(Vision Transformer),Deit(Data-efficient Image Transformer),and BeIT(Image Transformers).The reasoning module of VQA was updated and layer normalization was incorporated to enhance the performance outcome of our effort.In comparison to the results of previous research,our proposed method suggests a substantial enhancement in efficacy.Our experiment obtained a 60.4%accuracy with the PathVQA dataset and a 69.2%accuracy with the VizWiz dataset.

Mathematical Modelling for a Multi-Product Inventory Routing Problem with Split Delivery

In this article, a multi-product inventory routing problem is studied. One-depot and many retailers in a finite time period are considered, and split delivery is allowed as well for the addressed problem. The objective is to minimize the overall cost including vehicle cost, inventory holding cost and transportation cost while the delivery schedule and the quantity of each product for each retailer have to be decided simultaneously. A mathematical model is presented for solving the addressed optimally and example is illustrated as well.

Conditional Lot Splitting to Avoid Setups While Reducing Flow Time

Previous research has clearly and consistently shown that flow time advantages accrue from splitting production lots into smaller transfer batches or sub-lots. Less extensively discussed, and certainly undesired, is the fact that lot splitting may dramatically increase the number of setups required, making it impractical in some settings. This paper describes and demonstrates a primary cause of these “extra” setups. It then proposes and evaluates decision rules which selectively invoke lot splitting in an attempt to avoid extra setups. For the closed job shop environment tested, our results indicate that conditional logic can achieve a substantial portion of lot splitting’s flow time improvement while avoiding the vast majority of the additional setups which would be caused by previously tested lot splitting schemes.

A STUDY ON FACTORS FOR RETAILERS IMPLEMENTING CPFR-A FUZZY AHP ANALYSIS

Since collaborative, planning, forecasting, and replenishment （CPFR） was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of research has emphasized reduced costs, increased sales and improved forecasting ability, there has been a lack of research on the importance of each of the various factors which affect such implementations. In order to find out the critical success factors affecting CPFR implementation, this paper first collected related influence factors regarding adopting CPFR or business to business （B2B） information systems, and further constructed a factor table with a three-layer hierarchical structure. A pair wise analytic hierarchy process （AHP） questionnaire was designed and distributed to experts who were familiar with implementing CPFR in the retailing industry. After questionnaires were returned, we found out the weights of each impact factor by using a fuzzy analytic hierarchy process （fuzzy AHP） approach. The importance of each critical impact factor was investigated, and the paths of the critical success factors were also analyzed. The results of this study can provide more precise information with regard to allocating optimal resources for retailers implementing CPFR.