Benefits of retailer-supplier partnership initiatives under time-varying demand:a comparative analytical study

This paper aims to help supply chain managers to determine the value of retailer-supplier partnership initiatives beyond information sharing (IS) according to their specific business environment under time-varying demand conditions. For this purpose, we use integer linear programming models to quantify the benefits that can be accrued by a retailer, a supplier and system as a whole from shift in inventory ownership and shift in decision-making power with that of IS. The results of a detailed numerical study pertaining to static time horizon reveal that the shift in inventory ownership provides system-wide cost benefits in specific settings. Particularly, when it induces the retailer to order larger quantities and the supplier also prefers such orders due to significantly high setup and shipment costs. We observe that the relative benefits of shift in decision-making power are always higher than the shift in inventory ownership under all the conditions. The value of the shift in decision-making power is greater than IS particularly when the variability of underlying demand is low and time-dependent variation in production cost is high. However, when the shipment cost is negligible and order issuing efficiency of the supplier is low, the cost benefits of shift in decision-making power beyond IS are not significant

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in cloud-based manufacturing is handling of datasets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such datasets. The framework proposed in this research uses a hybrid approach to deal with big dataset for smarter decisions. Furthermore, we compare the performance of radial basis function based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in cloud-based manufacturing, is to predict the effect of data errors on quality due to highly imbalance unstructured dataset. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones

An Integer Programming Model for Locating Vehicle Emissions Testing Stations

Connecticut and other states not in compliance with federal air quality standards are required to implement a motor vehicle inspection program to test vehicles for pollutants---hydrocarbons and carbon monoxide. The problem is to determine the number, size, and locations of stations given constraints on the maximum travel distance from each town to its nearest station and the average waiting time at a station. In this paper we use simulation to find the maximum allowable arrival rates (in vehicles per hour) of stations of different sizes and formulate the station location problem as a set covering model. We generate a range of solutions through sensitivity analysis, varying both the average waiting time and maximum distance constraints. Comparing the current configuration of stations in Connecticut to our integer programming solutions we find that the integer programming approach reduces the objective function by at least $3 million. The current configuration has more stations than the IP solutions but they are not as well distributed.optimal location, public facility location, integer programming location models, emissions testing stations location models

Infrastructure Development for Conversion to . . .

An important concern for any nation wishing to convert to alternate,environm,K[CVG friendly energy sources is the developmKW of appropriate fuel distribution infrastructure. We address theproblem ofoptim)VK locating gas station facilities for developing nations, like India, which are in the process of convertingfrom leaded to unleaded fuel.Im portantly, asimx]K approachmp be used in developed countries, which are in the process of converting toautom)KW[G using hydrogen or electrical energy

An Effective Approach for Job-Shop Scheduling with Uncertain Processing Requirements

Production systems often involve various uncertainties such as unpredictable customer orders or inaccurate estimate of processing times. Managing such uncertainties is becoming critical in the era of "time-based competition." For example, if a schedule is generated without considering possible orders in the future, new orders of significant urgency may interrupt those already scheduled, causing serious violation of their promised delivery dates. The consideration of uncertainties, however, has been proven to be very difficult because of the combinatorial nature of discrete optimization compounded further by the presence of uncertain factors

Copyright C ○ “IIE” ISSN: 0740-817X print

Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lot