The Impact of Information Sharing on Different Performance Indicators in a Multi-Level Supply Chain

Enterprises can use different methods/principles to obtain competitive advantages. Information sharing (IS) among supply chain (SC) partners is also one of these methods used in enterprises and it has positive effects on overall system performance like reduced inventory level, decreased cost, bullwhip effects and increased profit. In this paper, our aim is to present the impacts of IS on different costs like ordering, holding and penalty costs of each SC member and total system costs in multi SC. We want to show the effects of sharing different types of information simultaneously or separately on SC partners as cost change. Besides, this paper presents the situation of order quantity estimation according to the proximity of actual order quantity in decentralized or centralized demand sharing. A model is developed to determine IS influence on the cost of SC partners. Various IS scenarios are studied in this paper. The customer demand, warehouse order quantity and warehouse-manufacturer lead time are the shared information of scenarios. Results are tested and analysed by using analysis of variance (ANOVA).The findings of this study show that IS especially simultaneously sharing reduces system costs. Lead time sharing provides the lowest cost between other types of sharing. For every system member, holding cost reduces the most during IS. The more accurate demand forecasting is performed in centralized demand sharing compared to decentralized sharing

Evolutionary algorithms for multi-objective flexible job shop cell scheduling

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation requiring minimization of makespan and total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution to each of the ‘medium’ to ‘large’ size instances considering even the simplified version of the problem. In this study, we present evolutionary algorithms for solving that bi-objective problem and apply genetic and memetic algorithms that use three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all evolutionary algorithms with various configurations is investigated across forty-three benchmark instances from ‘small’ to ‘large’ size including a large real-world problem instance. The experimental results show that the transgenerational memetic algorithm using weighted sum outperforms the rest producing the best-known results for almost all bi-objective flexible job shop cell scheduling instances, in overall

A benchmark dataset for multi-objective flexible job shop cell scheduling

This data article presents a description of a benchmark dataset for the multi-objective flexible job shop scheduling problem in a cellular manufacturing environment. This problem considers intercellular moves, exceptional parts, sequence-dependent family setup and intercellular transportation times, and recirculation requiring minimization of makespan and total tardiness simultaneously. It is called a flexible job shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation times (FJCS-SDFSTs-ITTs) problem. The dataset has been developed to evaluate the multi-objective evolutionary algorithms of the FJCS-SDFSTs-ITTs problems that are presented in ‘Evolutionary algorithms for multi-objective flexible job shop cell scheduling’. The dataset contains forty- three benchmark instances from ‘small’ to ‘large’, including a large real-world problem instance. Researchers can use the dataset to evaluate the future algorithms for the FJCS-SDFSTs- ITTs problems and compare the performance with the existing algorithms

Термины физики в татарском языке: автореферат диссертации на соискание ученой степени к.филол.н.: специальность 10.02.02

The approach taken in this paper is twofold. First manufacturing environment is simplified for the purposes of planning and control without losing any of the essential characteristics. Second, a simple GT model is applied to the shop floor area and real time MRP is applied to the assembly area. The aim of this study is to develop and compare with a simulation of similar proposal except that jobshop is used in the shop floor area instead. The variable factors in both models were the set up time to operation time ratio and the intensity of the loading on the machines. In the highly loaded situations, the GT model faired better than the job shop model. However, for low loaded situations the performances of the two models were similar

An application of fuzzy clustering to manufacturing cell design

There are several methods and techniques for manufacturing cell design. Many clustering techniques have focused on operations that have been made on part-machine matrix. Some methods have used array-based techniques while others have used similarity coefficient or distance criteria in order to determine clusters. It has been seen from the recent researches, solutions may be changeable related to using algorithms. In order to investigate the applicability of several techniques it is necessary to obtain in comparison with each other. Artificial intelligence technologies are commonly in use in clustering problems as other manufacturing issues. In this study, fuzzy logic approach is studied in design of part families and machine cells simultaneously. The aim at this study is to compare manufacturing cell design which made of fuzzy clustering algorithm (Fuzzy C-Means) with the crisp methods. It has been seen from the result of the study, fuzzy clustering solutions may be efficient than the crisp method for the selected data sets

Are We There Yet? A Progress Report from Three Turkish University Pioneers in Distance Education and E-Learning

The international literature provides little in-depth analysis of distance education and e-learning activities, achievements, and challenges in Turkish higher education other than the country's mega-university, Anadolu. This paper examines the development of, and lessons to be learned from, such undertakings by three pioneers - two regular state universities, Ankara University and Sakarya University, and the private, non-profit Turkish-Kazakhstan Ahmet Yesevi University. Drawing on the collective experience of the authors, the paper reaches some overall conclusions about embarking on distance education and e-learning, which may apply in other Turkish universities and similar economies

An industrial visual inspection system that uses inductive learning

This paper presents an industrial visual inspection system that uses inductive learning. The system employs RULES-3 inductive learning algorithm to extract the necessary set of rules and template matching technique to process an image. Twenty 3 x 3 masks are used to represent an image. Each example consists of 20 frequencies of each mask. The system was tested on five different types of tea or water cups in order to classify the good and bad items. The system was trained using five good cups and then tested for 113 unseen examples. The results obtained showed the high performance of the system: the efficiency of the system for correctly classifying unseen examples was 100%. The system can also decide what type of the cup is being processed