At present, it is projected that about 4 zettabytes (or 10**21 bytes) of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors (i.e., services, manufacturing, construction, agriculture and mining) of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien (2003) forewarned about the data rich, information poor (DRIP) problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed — yet, not necessarily defensible or valid — decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges (identified by the National Academy of Engineering in 2008), underpin the 10 breakthrough technologies (compiled by the Massachusetts Institute of Technology in 2013) and support the Third Industrial Revolution of mass customization.

One of the key challenges for implementing RFID systems in supply chain management is the difficulty in economic justification. Such difficulty is further amplified by its public participation nature as multiple self-interest beneficiaries may receive diverse paybacks, and their incentives to join the system are difficult to align. This paper aims to address these problems by a collaborative design from two aspects. First, we propose to introduce a centralized planning mechanism in the chain to facilitate the participation, so that the cost of the overall system can be minimized. Second, we propose to analyze the multi-facet economic return from multi-purpose applications to achieve the full potential of RFID systems. To illustrate our approach, its application for inventory inaccuracy and product recall in RFID system is presented.

Based on the stochastic market demand, this paper considers the order decision-making strategies of the supply chain by introducing statement strategies. Consequently, the time-variant variance in the demands of the market is incorporated into the model. The retailer simultaneously determines the purchase time (i.e., lead time) and order quantity, and the manufacturer determines the statement strategy and the reserved profit rate. The results show that the no overtime statement strategy can induce the retailer to place more orders in advance by limiting the available order quantity within the available time. Finally, we also adopt numerical examples to support the conclusion of this paper.

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values of inputs and outputs, but it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and ordinal data. In this study, a cross-efficiency method is introduced into the DEA model to calculate the interval of cross-efficiency values, based on which a new TOPSIS method is proposed to rank the DMUs. Two examples are presented to illustrate and validate the proposed method.

Improving relations between the People’s Republic of China (PRC) and the United States of America (US) and ensuring that they work together as allies rather than as competitors can serve as a stabilizing force against armed conflict, particularly with surrounding nations. The economic, social, and political relationships between the PRC and US have progressed along a hilly journey. As the second largest economy in the world, the PRC has continued to develop its military and is determined to climb the technological ladder. This growth has led the US and the PRC to be referred to as a G-2 of superpowers. As the US hegemony continues to weaken this G-2 relationship is becoming more important. With significant economic, political, and security issues at stake it is crucial that efforts to strengthen these relations are prioritized and implemented. A rigorous prioritization process, the Analytic Network Process (ANP) is used herein to prioritize the efforts and initiatives in the G-2 relationship. The model is presented with results and the extensive sensitivity analysis present additional insight into the suggested solutions.

This paper proposes a mixed integer programming model for the allocation of rail mounted gantry cranes for four basic yard activities with different priorities. The model pays special attention to the typical features of this kind of gantry cranes, such as a restricted traveling range and a limited number of adjustments during loading and discharging operations. In contrast to most of the literature dealing with these four yard activities individually, this paper models them into an integrated problem, whose computational complexity is proved to be NP-hard. We are therefore motivated to develop a Lagrangian relaxation-based heuristic to solve the problem. We compare the proposed heuristic with the branch-and-bound method that uses commercial software packages. Extensive computational results show that the proposed heuristic achieves competitive solution qualities for solving the tested problems.