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Gas storage facilities are expensive and their siting is determined by the customer not the supplier. Accurate determination of optimum capacity is a “must”.

The purpose of this paper is to consider the use of temporary storage offered by intermodal transshipment points to position some stock of fast moving consumer goods in advance of demand; this floating stock concept combines transport and inventory management. Intermodal transport is compared with direct road transport for a supply chain.

First an analytical comparison is made which shows that the floating stock concept has advantages in inventories over pure road and intermodal transport. Next, a simulation study of a real case is made which quantifies the cost‐differences in detail.

It is found that both storage costs can be lowered and shorter response times be gotten by sending shipments in advance to intermodal terminals. The advance positioning can offset the disadvantage of a longer transit time in intermodal transport.

Demand needs to be somewhat predictable. The pooling effects depend on geographical layout of the customers. The availability of intermodal transport options is based on the situation in Western Europe.

The floating stock concept considers both the transport and inventory issues. By positioning some of the stock at transshipment points close to the customer in anticipation of demand, the concept can yield lower inventory costs as well as a lower customer lead time. The benefit for logistics service providers is a more regular supply chain. Using intermodal transport provides an opportunity to green the supply chain as the environmental impact per ton/kilometer is lower than road transport.

This paper draws on the areas of logistics and inventory management to consider the choice of transport mode; most studies look at these issues in isolation. Considering the holding and storage costs in addition to the distribution strategy enables a more thorough comparison of the transport modes.

In order to achieve competitive advantage over the physical marketplace, the e-retailers are insisted on endowing with lenient return policies. The piece-wise returns-and-reordering process incurs excessive buffering and unwanted logistics costs which raises overall fulfillment charges. The objective of this study is to re-design e-retail distribution policy by providing temporal storage at logistics service provides' (LSP) location. The impact of recurrent returns on pricing and profit margins are also investigated over time continuum.

A framework is developed to reduce the non-value added (NVA) storage and distribution efforts by providing collaborative buffering between LSP and e-retailer. The knapsack based buffering approach is tested and compared with traditional e-retail distribution practices. The revenue sharing concept is mathematically modelled and implemented in GAMS, which finally validated through multiple return scenarios.

The proposed model outperforms the existing one under all scenarios with different configuration settings of re-ordering, profit margins, and buffer time windows. The distribution cost is found, linearly related to the necessary product buffering space. The findings help to re-design sustainable return policies for individual products so that maximum customer value can be yield with minimum costs.

This study helps to determine the NVA efforts incurred while storing and delivering multi-time returned products to ensure desired service levels. The revenue sharing model provides pricing strategies for e-retail practitioners deciding which product should store in what quantity for how much time at the shipping agency location so that it fulfils the re-ordering at least waiting and sufficient buffering.

The proposed model extends the role of LPSs as temporary buffer providers to reduce returns-and-reordering fulfilment efforts in the e-retail network. This Collaborative framework offers an opportunity to amend the distribution contracts and policies time by time that enhances e-retailer's performance and customer satisfaction.

Logisticians in the worldwide industry are frequently faced with the problem of measuring the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation, when different kind of warehousing systems are applied.

A multiple case study analysis is here developed and supported by a methodological framework directly derived from the working group discussions and brainstorming activities. Two different field of application are considered: one related to five companies with manual warehousing systems operating with traditional fork lift trucks; the other is among five companies operating with automated storage/retrieval systems (AS/RS) to store inventories.

The multi-case study helps to understand how the holding cost parameter is currently computed by industrial managers and how much the difference between manual and automated/automatic warehousing systems impacts on the inventory cost structure definition. The insights from the ten case studies provide evidence that the kind of storage system adopted inside the factory can impact on the holding cost rate computation and permit to derive important considerations.

The final aim of this work is to help industrial engineers and logisticians in correctly understanding the inventory costs involved in their systems and their cost structure. In addition, the multi-case analysis leads to considerations, to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the presented methodology, and with aid from the data from this paper.

The relevance of this work is to help industrial engineers and logisticians in understanding correctly the inventory costs involved in their logistics systems and their cost structure. In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the methodology and extrapolating the data from this paper.

Companies using just‐in‐time principles are currently moving from centralized to decentralized storage areas. A question that needs to be addressed is whether this move is cost efficient. Presents a costing model which can be used to determine if further consideration should be given to decentralized storage in a facility currently utilizing centralized storage. Evaluates whether the savings in material handling flow costs associated with moving from centralized to decentralized storage outweighs the additional costs associated with implementing and utilizing decentralized storage for a designated period of time. Through the process of solving the cost model, a new layout will be created for the decentralized storage system. The layout includes the placement of the additional material storage and receipt areas as well as the rearrangement of the other departments in the facility.

The purpose of this paper is to use cloud storage in digital preservation by analyzing the pricing and data retrieval models. The author recommends strategies to minimize the costs and believes cloud storage is worthy of serious consideration.

Few articles have been published to show the uses of cloud storage in libraries. The cost is the main concern. An overview of cloud storage pricing shows a price drop once every one or one-and-a-half years. The author emphasize the data transfer-out costs and demonstrate a case study. Comparisons and analysis of S3 and Glacier have been conducted to show the differences in retrieval and costs.

Cloud storage solutions like Glacier can be very attractive for long-term digital preservation if data can be operated within the provider’s same data zone and data transfer-out can be minimized.

Institutions can benefit from cloud storage by understanding the cost models and data retrieval models. Multiple strategies are suggested to minimize the costs.

The paper is intended to bridge the gap of uses of cloud storage. Cloud storage pricing especially data transfer-out pricing charts are presented to show the price drops over the past eight years. Costs and analysis of storing and retrieving data in Amazon S3 and Glacier are discussed in details. Comparisons of S3 and Glacier show that Glacier has uniqueness and advantages over other cloud storage solutions. Finally strategies are suggested to minimize the costs of using cloud storage. The analysis shows that cloud storage can be very useful in digital preservation.

In transportation and distribution systems, the shipment decisions, fleet capacity, and storage capacity are interrelated in a complex way, especially when the authors take into account uncertainty of the demand rate and shipment lead time. While shipment planning is tactical or operational in nature, increasing storage capacity often requires top management’s authority. The purpose of this paper is to present a new method to integrate both operational and strategic decision parameters, namely shipment planning and storage capacity decision under uncertainty. The ultimate goal is to provide a near optimal solution that leads to a striking balance between the total logistics costs and product availability, critical in maritime logistics of bulk shipment of commodity items.

The authors use simulation as research method. The authors develop a simulation model to investigate the effects of various factors on costs and service levels of a distribution system. The model mimics the transportation and distribution problems of bulk cement in a major cement company in Indonesia consisting of a silo at the port of origin, two silos at two ports of destination, and a number of ships that transport the bulk cement. The authors develop a number of “what-if” scenarios by varying the storage capacity at the port of origin as well as at the ports of destinations, number of ships operated, operating hours of ports, and dispatching rules for the ships. Each scenario is evaluated in terms of costs and service level. A full factorial experiment has been conducted and analysis of variance has been used to analyze the results.

The results suggest that the number of ships deployed, silo capacity, working hours of ports, and the dispatching rules of ships significantly affect both total costs and service level. Interestingly, operating fewer ships enables the company to achieve almost the same service level and gaining substantial cost savings if constraints in other part of the system are alleviated, i.e., storage capacities and working hours of ports are extended.

Cost is a competitive factor for bulk items like cement, and thus the proposed scenarios could be implemented by the company to substantially reduce the transportation and distribution costs. Alleviating storage capacity constraint is obviously an idea that needs to be considered when optimizing shipment planning alone could not give significant improvements.

Existing research has so far focussed on the optimization of shipment planning/scheduling, and considers shipment planning/scheduling as the objective function while treating the storage capacity as constraints. The simulation model enables “what-if” analyses to be performed and has overcome the difficulties and impracticalities of analytical methods especially when the system incorporates stochastic variables exhibited in the case example. The use of efficient frontier analysis for analyzing the simulation results is a novel idea which has been proven to be effective in screening non-dominated solutions. This has provided the authors with near optimal solutions to trade-off logistics costs and service levels (availability), with minimal experimentation times.

The ongoing urbanization and decarbonization require deployment of energy storage in the urban energy system to integrate large-scale variable renewable energy (VRE) into the power grids. The cost reductions of batteries enable private entities to invest energy storage for energy management whose operating strategy may differ from traditional storage facilities. This study aims to investigate the impacts of energy storage on the power system with different operation strategies. Two strategies are modeled through a simulation-based regional economic power dispatch model. The profit-oriented strategy denotes the storage system operated by private entities for price arbitrage, and the nonprofit-oriented strategy denotes the storage system dispatched by an independent system operator (ISO) for the whole power system optimization. A case study of Jiangsu, China is conducted. The results show that the profit-oriented strategy only has a very limited impact on the cost reductions of power system and may even increase the cost for consumers. While nonprofit-oriented energy storage performs a positive effect on the system cost reduction. CO₂ emission reduction can only be achieved under a high VRE scenario for energy storage. Integrating energy storage into the power system may increase CO₂ emissions in the near term. In addition, the peak-valley spread is crucial to trigger operations of profit-oriented energy storage, and the profitability of energy storage operator is observed to be decreasing with the total storage capacity. This study provides new insights for the energy management in the smart city, and the modeling framework can be applied to regions with different resource endowments.

The authors characterize two battery storage operating strategies of profit- and nonprofit-oriented by adopting a simulation-based economic dispatch model. A simulation from 36 years of hourly weather data of wind and solar output from case study of Jiangsu, China is conducted.

The results show that the profit-oriented strategy only has a very limited impact on the cost reductions of power system and may even increase the cost for consumers. While nonprofit-oriented energy storage performs a positive effect on the system cost reduction. CO₂ emission reduction can only be achieved under high VRE scenario for energy storage. Integrating energy storage into the power system may increase CO₂ emissions in the near term. In addition, the peak-valley spread is crucial to trigger operations of profit-oriented energy storage, and the profitability of energy storage operator is observed to be decreasing with the total storage capacity.

This study provides new insights for the energy management in the smart city, and the modeling framework can be applied to regions with different resource endowments.

There is need for a new, more precise, more integrated system for logistics costing. The integration of purchasing, transportation (traffic) and storage operations throughout the vertical stream, from source of materials to the consumer is discouragingly complex. It cannot be accomplished by departmentalising logistics functions, performing average and joint commodity costing, and dealing primarily with direct costs. The volume of records may be representative, but there is a very apparent lack of costing systems to facilitate logistics planning. The volume of shipment is the key to logistics cost analysis, just as volume of production is the independent variable in conventional economic analysis. Logistics costs vary with changes in the volume of shipment. Cost per hundredweight is the unit for cost analysis under the Cellular Flow Planning System. It is measured on a specific commodity, between specific points. Purchase price, transport rate, linear increasing costs, constant and single payment decreasing costs are the five classes of costs used to compare competitive volumes of shipment. All significant logistics costs can be so classified. This article presents only the rationale behind the unit for cost measurement, the classification of costs, and the means of comparison.