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The purpose of this paper through the redundant monitoring unit reflecting the real-time temperature change of the array, an adaptive refresh circuit based on temperature is designed.

This paper proposed a circuit design for temperature-adaptive refresh with a fixed refresh frequency of traditional memory, high refresh power consumption at low temperature and low refresh frequency at high temperature.

Adding a metal oxide semiconductor (MOS) redundancy monitoring unit consistent with the storage unit to the storage bank can monitor the temperature change of the storage bank in real time, so that temperature-based memory adaptive refresh can be implemented.

According to the characteristics that the data holding time of dynamic random access memory storage unit decreases with the increase of temperature, a MOS redundant monitoring unit which is consistent with the storage unit is added to the storage array with the 2T storage unit as the core.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

The purpose of this paper is to investigate efficient designs of a shell-and-tube latent thermal energy storage system through an approach based on the analysis of entropy generation. It proposes innovative branched fins to maximize the performance of the system.

A computational fluid dynamic (CFD) model is first used to detail the thermo-fluid dynamic transient behavior of the latent heat storage system. The model account for phase change, buoyancy driven fluid flow and heat transfer during the process of energy retrieval from the storage unit (solidification). The CFD model is then used to evaluate locally the entropy generation rate during the process. On the basis of the insight gathered through the analysis of the entropy generation, the design of the fins is gradually modified aiming at the maximization of the performance of the storage system.

The best fins design leads to a twofold increase of the solidification rate in the latent heat storage unit. The corresponding second-law efficiency shows an increase of 13 percent compared with traditional fins.

The analysis is based on a single tube configuration of the storage system which implies that non-homogeneous effects due to multiple tubes are not considered. Nevertheless, the proposed design procedure is general and could be applied to different configurations of latent heat thermal storage systems.

Entropy generation analysis provides a very useful design approach to develop configurations of latent heat storage systems that may overcome current performance limitations. Also, practitioners in the field may also benefit of the results for improving current installations of energy storage systems.

Entropy generation is adapted and used to find an optimal design for a time dependent process. That is, a geometrical configuration is found for maximizing the performance over a span of time. This is a key aspect of the work because there is a strong trend toward energy systems operating under transient conditions.

With the inclusion of significant wind power into the power system, the unit commitment (UC) has become challenging due to frequent variations in wind power, load and requirement of reserves with sufficient ramp rate. The pumped storage units with lesser startup time and cost can take care of these sudden variations and reduce their impact on power system operation. The aim of this paper is to provide a solution model for UC problem in a hybrid power system.

The model developed has been implemented through GAMS optimization tool with CONOPT solver. The model has been called into MATLAB platform by using GAMS‐MATLAB interfacing to obtain solutions.

The model provides an efficient operating schedule for conventional units and pumped storage units to minimize operating cost and emission. The effects of wind power and load profiles on emission, operating cost and reserve with enough ramping capabilities have been minimized with the use of pumped storage unit. The commitment schedule of thermal and pumped storage units have been obtained with significant wind power integrated into the system for best cost commitment (BCC) and for a combined objective of cost and emission minimization.

This paper finds that the operating cost and emission in a commitment problem can be reduced significantly during variable wind and load conditions in a hybrid system. The model proposed provides operational schedules of conventional and pumped storage units with variable wind power and load conditions throughout operating horizon. The coordinated optimization approach has been implemented on a hybrid system with IEEE‐30 bus system.

Providers of cloud computing storage services (CCSS) charge offers in several unit bundles for a lump sum per bundle. This non-linear pricing approach is known as a bucket-pricing plan (BPP). If a customer exploits the purchased bucket, he/she can opt for the next higher bucket or refrain from further CCSS use. CCSS suppliers are faced with an optimization problem concerning the number of buckets as well as their lower and upper storage volume boundaries. The purpose of this paper is to develop a model, which supports CCSS suppliers in deriving a BPP-structure and which maximizes their profit in varying market constellations.

The authors develop a multi-period model of tariff choice decisions of private customers of CCSS. The model is applied in Monte Carlo simulations to determine profit-maximal tariff structures as a function of different market characteristics such as median demand saturation, demand heterogeneity, average price per storage unit and bucket ceiling allocation (identical size of each bucket within the frame set by the lower and upper overall boundary, varying sizes of the buckets offered, so that the interval between two ceilings consecutively increases for subsequent buckets) and type of a customer’s utility function.

The simulation analysis suggests that demand heterogeneity and average price per unit are the most influential factors for CCSS tariff structure optimization. Price plans with more than two buckets tend to generate higher profits than simple schemes with two buckets only if demand heterogeneity is low and the average price per storage unit is high and/or median saturation level of customers is low.

Despite the popularity of BPP among providers of CCSS for consumers, there is a lack of scholarly modeling work on the profit implications of the number of buckets entailed in a scheme and the size/ceilings of the various buckets on offer. The model suggested in this paper is a first step toward narrowing this research gap.

Deals with the issues related to the implementation, in a manual finished goods warehouse, of an ERP information system aimed at improving warehouse internal logistics efficiency. Specifically it is observed that the mere implementation of an integrated warehouse management information system does not actually guarantee the optimization of warehouse logistics. Rather, to improve the overall systems efficiency, it is required that ERP implementation be combined with the redesign and the reorganization of warehouse logistics and processes. This means that to achieve an effective synergic effect and thus effectively increase systems efficiency, both these steps have to be undertaken concurrently, grounding the reorganization and redesign phase on the implementation fallouts )in first place higher traceability levels gained(. The whole project is then applied to a real case of a finished goods manual warehouse, with pile storage systems.

Planning to add a hard disk to your computer? One of the possibilities you should consider is a unit that combines the disk with a magnetic tape drive. The disk will store your data; the tape will provide the backup security you need to protect against loss of valuable files.

The purpose of this paper is to develop new model formulation for reducing the workload in pre‐batching at a manufacturer of flavors and fragrances, by optimally assigning ingredients to different storage types, taking into account past usage of ingredients and several restrictions about volumes and number of storage units.

Two models were developed, using mathematical programming, accommodating either fixed or variable shelf settings in vertical carousel storage, along with other varying storage types. The models were validated, yielding a sizeable reduction in workload, and run under varying scenarios of storage additions to reduce workload even further.

For different storage addition scenarios, application of the new model formulation yielded a reduction of 22 percent in workload. In addition, aside from space savings, approximately 40 percent of refill order total waiting time was reduced due to improved allocation.

The authors' analysis was limited by considering direct cost savings only. Yet, reduced workloads in pre‐batching may also yield indirect cost savings, such as reduced quality costs, inventory costs, and investment savings by not having to extend the pre‐batching area. Such considerations may be addressed in future research, provided data on indirect savings are available.

While intuitive storage allocation overloads the pre‐batching department and increases space utilization, suggesting an incorrect perception that more personnel and space are needed, the proposed approach provides a better alternative through optimized allocations.

The present paper adds to the literature on carousel storage location by explicitly addressing the storage sizing issue, as well as workload balancing.

The purpose of this paper is to examine the feasibility of an ideal power network that combines many different renewable energy technologies such as wind power, concentrated solar power (CSP) and hydroelectric power. This paper emphasizes in finding the benefits arising from hydrothermal coordination compared to the non-regulated integration of the hydroelectric units, as well as the benefits from the integration of wind power and CSP.

Artificial Neural Networks were used to estimate wind power output. As for the CSP system, a three-tier architecture which includes a solar field, a transmission-storage system and a production unit was used. Each one of those separate sections is analyzed and the process is modeled. As for the hydroelectric plant, the knowledge of the water’s flow rated has helped estimating the power output, taking into account the technical restrictions and losses during transmission. Also, the economic dispatch problem was solved by using artificial intelligence methods.

Hydrothermal coordination leads to greater thermal participation reduction and cost reduction than a non-regulated integration of the hydrothermal unit. The latter is independent from the degree of integration of the other renewable sources (wind power, CSP).

Hydrothermal coordination in a power system which includes thermal units and CSP for cost and emissions reduction.

The ‘Office of the Future’, ‘Office Technology’, ‘Word Processing’, ‘Electronic Mail’, ‘Electronic Communications’, ‘Convergence’, ‘Information Management’. These are all terms included in the current list of buzz words used to describe current activities in the office technology area. Open the pages of almost any journal or periodical today and you will probably find an article or some reference to one or more of the above subjects. Long, detailed and highly technical theses are appearing on new techniques to automate and revolutionize the office environment. Facts and figures are quoted ad nauseam on the high current cost of writing a letter, filing letters, memos, reports and documents, trying to communicate with someone by telephone or other telecommunication means and, most significant of all, the high cost of people undertaking these never‐ending tasks. The high level of investment in factories and plants and the ever‐increasing fight to improve productivity by automating the dull, routine jobs are usually quoted and compared with the extremely low investment in improving and automating the equally tedious routine jobs in the office environment; the investment in the factory is quoted as being ten times greater per employee than in the office. This, however, is changing rapidly and investment on a large scale is already taking place in many areas as present‐day inflation bites hard, forcing many companies and organizations to take a much closer look at their office operations.