Management and Service-aware Networking Architectures (MANA) for Future Internet Position Paper: System Functions, Capabilities and Requirements

Future Internet (FI) research and development threads have recently been gaining momentum all over the world and as such the international race to create a new generation Internet is in full swing: GENI, Asia Future Internet, Future Internet Forum Korea, European Union Future Internet Assembly (FIA). This is a position paper identifying the research orientation with a time horizon of 10 years, together with the key challenges for the capabilities in the Management and Service-aware Networking Architectures (MANA) part of the Future Internet (FI) allowing for parallel and federated Internet(s)

Online fulfillment: f-warehouse order consolidation and bops store picking problems

Fulfillment of online retail orders is a critical challenge for retailers since the legacy infrastructure and control methods are ill suited for online retail. The primary performance goal of online fulfillment is speed or fast fulfillment, requiring received orders to be shipped or ready for pickup within a few hours. Several novel numerical problems characterize fast fulfillment operations and this research solves two such problems. Order fulfillment warehouses (F-Warehouses) are a critical component of the physical internet behind online retail supply chains. Two key distinguishing features of an F-Warehouse are (i) Explosive Storage Policy – A unique item can be stored simultaneously in multiple bin locations dispersed through the warehouse, and (ii) Commingled Bins – A bin can stock several different items simultaneously. The inventory dispersion profile of an item is therefore temporal and non-repetitive. The order arrival process is continuous, and each order consists of one or more items. From the set of pending orders, efficient picking lists of 10-15 items are generated. A picklist of items is collected in a tote, which is then transported to a packaging station, where items belonging to the same order are consolidated into a shipment package. There are multiple such stations. This research formulates and solves the order consolidation problem. At any time, a batch of totes are to be processed through several available order packaging stations. Tote assignment to a station will determine whether an order will be shipped in a single package or multiple packages. Reduced shipping costs are a key operational goal of an online retailer, and the number of packages is a determining factor. The decision variable is which station a tote should be assigned to, and the performance objective is to minimize the number of packages and balance the packaging station workload. This research first formulates the order consolidation problem as a mixed integer programming model, and then develops two fast heuristics (#1 and #2) plus two clustering algorithm derived solutions. For small problems, the heuristic #2 is on average within 4.1% of the optimal solution. For larger problems heuristic #2 outperforms all other algorithms. Performance behavior of heuristic #2 is further studied as a function of several characteristics. S-Strategy fulfillment is a store-based solution for fulfilling online customer orders. The S-Strategy is driven by two key motivations, first, retailers have a network of stores where the inventory is already dispersed, and second, the expectation is that forward positioned inventory could be faster and more economical than a warehouse based F-Strategy. Orders are picked from store inventory and then the customer picks up from the store (BOPS). A BOPS store has two distinguishing features (i) In addition to shelf stock, the layout includes a space constrained back stock of selected items, and (ii) a set of dedicated pickers who are scheduled to fulfill orders. This research solves two BOFS related problems: (i) Back stock strategy: Assignment of items located in the back stock and (ii) Picker scheduling: Effect of numbers of picker and work hours. A continuous flow of incoming orders is assumed for both problems and the objective is fulfillment time and labor cost minimization. For the back-stock problem an assignment rule based on order frequency, forward location and order basket correlations achieves a 17.6% improvement over a no back-stock store, while a rule based only on order frequency achieves a 12.4 % improvement. Additional experiments across a range of order baskets are reported

Optimization Techniques for Modern Power Systems Planning, Operation and Control

Recent developments in computing, communication and improvements in optimization techniques have piqued interest in improving the current operational practices and in addressing the challenges of future power grids. This dissertation leverages these new developments for improved quasi-static analysis of power systems for applications in power system planning, operation and control. The premise of much of the work presented in this dissertation centers around development of better mathematical modeling for optimization problems which are then used to solve current and future challenges of power grid. To this end, the models developed in this research work contributes to the area of renewable integration, demand response, power grid resilience and constrained contiguous and non-contiguous partitioning of power networks. The emphasis of this dissertation is on finding solutions to system operator level problems in real-time. For instance, multi-period mixed integer linear programming problem for applications in demand response schemes involving more than million variables are solved to optimality in less than 20 seconds of computation time through tighter formulation. A balanced, constrained, contiguous partitioning scheme capable of partitioning 20,000 bus power system in under one minute is developed for use in time sensitive application area such as controlled islanding

A Two-Stage Model for Optimal Operation of Multi-energy Hub System for Resilience Enhancement Against Natural Disasters

The climate change leads to more natural disasters which can lead to two results, one is that some generation and transmission infrastructures of energy will endure serious damages, and another is that cities and districts will probably be exposed to potentially large-scale blackouts. The pressures of energy and environment problems have prompted people to reflect on existing energy consumption patterns and begin to study the comprehensive utilization of various types of energy such as electricity, gas and heat. The concept of energy hub (EH) has emerged. It is a key hub within multi-energy network. A Two-stage model for the operation of multi-energy hub system for resilience enhancement in natural disasters was established in this thesis. The system includes three different energy hub systems, each EH consists of electric transformer, Combined Cooling, Heating and Power (CCHP), Energy Storage System (ESS) and chiller which are responsible for energy conversion and transfer. Each EH is connected to the main electric network and natural gas network. There are also transmission lines and pipelines connected between them for energy communication. The purpose of this model is to reduce the load shedding as much as possible while ensuring the maximum economic benefits including operation costs and load curtailment punishing fees of both two stages, so that each EH system can make a reasonable energy supply externally and maintain stable operation internally. When disaster happens, the system will go through two stages, first stage is the one before disaster and second stage is the one when disaster occurs. The choices made by the system will be different at these two stages, including selling and purchasing value from the main network, storing and releasing energy value of ESS, conversion ratio for different energies within EH and the load shedding value of demand side because each stage has different transmission rate and load demand. Three case studies have been done. YALMIP toolbox of MATLAB has been used to solve these problems. In case study one, the result shows that the total cost of two-stage model reduced by about 25% compared to the separate stage model, and load curtailment, especially electricity, was reduced sharply. In case study two, after load priority setting, load curtailment fee has been reduced obviously by 8.2%, shedding value of significant load has been reduced up to 26.9%. In case study three, the total cost of coordinated 3-EH model has been reduced by 57.59% compared to the model without coordination, and each EH has saved cost by 32.92%, 69.38% and 53.21% respectively. The result shows great advantages of this model, by using the two stage the total cost and load curtailment value reduced significantly for both whole system and each EH

A Two-Stage Model for Optimal Operation of Multi-Energy Hub System for Resilience Enhancement Against Natural Disasters

The climate change leads to more natural disasters which can lead to two results, one is that some generation and transmission infrastructures of energy will endure serious damages, and another is that cities and districts will probably be exposed to potentially large-scale blackouts. The pressures of energy and environment problems have prompted people to reflect on existing energy consumption patterns and begin to study the comprehensive utilization of various types of energy such as electricity, gas and heat. The concept of energy hub (EH) has emerged. It is a key hub within multi-energy network. A Two-stage model for the operation of multi-energy hub system for resilience enhancement in natural disasters was established in this thesis. The system includes three different energy hub systems, each EH consists of electric transformer, Combined Cooling, Heating and Power (CCHP), Energy Storage System (ESS) and chiller which are responsible for energy conversion and transfer. Each EH is connected to the main electric network and natural gas network. There are also transmission lines and pipelines connected between them for energy communication. The purpose of this model is to reduce the load shedding as much as possible while ensuring the maximum economic benefits including operation costs and load curtailment punishing fees of both two stages, so that each EH system can make a reasonable energy supply externally and maintain stable operation internally. When disaster happens, the system will go through two stages, first stage is the one before disaster and second stage is the one when disaster occurs. The choices made by the system will be different at these two stages, including selling and purchasing value from the main network, storing and releasing energy value of ESS, conversion ratio for different energies within EH and the load shedding value of demand side because each stage has different transmission rate and load demand. Three case studies have been done. YALMIP toolbox of MATLAB has been used to solve these problems. In case study one, the result shows that the total cost of two-stage model reduced by about 25% compared to the separate stage model, and load curtailment, especially electricity, was reduced sharply. In case study two, after load priority setting, load curtailment fee has been reduced obviously by 8.2%, shedding value of significant load has been reduced up to 26.9%. In case study three, the total cost of coordinated 3-EH model has been reduced by 57.59% compared to the model without coordination, and each EH has saved cost by 32.92%, 69.38% and 53.21% respectively. The result shows great advantages of this model, by using the two stage the total cost and load curtailment value reduced significantly for both whole system and each EH

Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the base of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution route with and without carbon emissions cost are constructed, and fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, the path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results