In a stacking system abatement measures with simultaneous effects on climate and water targets, such as wetland construction and cultivation of energy crops, are credited for the abatement of multiple pollutants. In t...
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In a stacking system abatement measures with simultaneous effects on climate and water targets, such as wetland construction and cultivation of energy crops, are credited for the abatement of multiple pollutants. In this study we calculated and compared the abatement costs of achieving multiple environmental targets with and without stacking under different policy regimes (emissions trading, charge, standards). To this end, a static mixed integer programming model was constructed. Theoretical analysis showed that, irrespective of policy regime, the cost of achieving predetermined emission targets is always lower when stacking is allowed. Another result was the excess abatement of pollutant under a no stacking system. Application to the Baltic Sea region showed that cost savings from stacking of pollutant abatement vary between policy regimes, being smallest for price regulation and highest for emission standards. However, the gains from stacking were unevenly distributed among the nine countries surrounding the Baltic Sea under all policy regimes, with Poland making the largest gain and Estonia, Russia and Latvia facing losses. Excess abatement without stacking in relation to the target was highest for nitrogen under all policy regimes, comprising up to 52% of the target abatement.
IBM ILOG CPLEX Optimization Studio delivers advanced and complex optimization libraries that solve linear programming (LP) and related problems, e.g., mixedinteger. Moreover, the optimization tool provides users with...
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IBM ILOG CPLEX Optimization Studio delivers advanced and complex optimization libraries that solve linear programming (LP) and related problems, e.g., mixedinteger. Moreover, the optimization tool provides users with its Academic Research Edition, which is available for teaching and noncommercial research at no-charge. This paper describes the usage of CPLEX C++ API for solving linear problems and, as an exhaustive example, optimization of network flows in overlay multicast is taken into account. Applying continuous and integral variables and implementing various constraints, including equations and inequalities, as well as setting some global parameters of the solver are presented and widely explained.
Non-renewable resource extraction contributes greatly to degradation of wildlife habitats in boreal landscapes. In western Canada, oil and gas exploration and extraction have left a dense network of linear disturbance...
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Non-renewable resource extraction contributes greatly to degradation of wildlife habitats in boreal landscapes. In western Canada, oil and gas exploration and extraction have left a dense network of linear disturbances (seismic lines) and abandoned well pads that have fragmented boreal forest. Among multiple ecological effects, these disturbances have increased predator access to the preferred habitat of some wildlife taxa, most notably boreal woodland caribou, resulting in population declines. Restoration of seismic lines and abandoned well pads is a critical activity to improve the recovery of woodland caribou populations. We present a linear programming model that optimally allocates restoration efforts to maximize the access of caribou to nearby undisturbed habitat in a fragmented landscape. We applied the model to examine restoration scenarios in the Cold Lake First Nations area in northeastern Alberta, Canada, which includes caribou habitat but also areas of active oil and gas extraction. The model depicts the landscape as a network of interconnected habitat patches and combines three network flow sub-problems. The first sub-problem enforces the spatial connectivity of the remaining network of unrestored sites. The second sub-problem maximizes access to suitable habitat from the restored locations and the third sub-problem ensures the allocation of restoration activities in as few spatially contiguous restoration projects as possible. The approach is generalizable and applicable to assist restoration planning in other resource extraction regions and for other taxa.
We focus on modelling and solving a batch sizing problem under finite capacity and additional constraints. Algorithms presented are integrated into Advanced Planning System software called SKEP, which is widely used i...
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We focus on modelling and solving a batch sizing problem under finite capacity and additional constraints. Algorithms presented are integrated into Advanced Planning System software called SKEP, which is widely used in an industrial context. The aim is to calculate a production plan on a set of products on a planning horizon. This production is made under a finite capacity. We will present the mixed integer programming (MIP) model, taking into account extensions induced by industrial constraints, as demand uncertainty, inventory and warehouse capacity, setup times and material requirement constraints.
In cooperative cognitive radio networks (CCRNs), there has been growing demand of transmitting secondary user (SU) source information secretly to the corresponding SU destination with the aid of cooperative SU relays....
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In cooperative cognitive radio networks (CCRNs), there has been growing demand of transmitting secondary user (SU) source information secretly to the corresponding SU destination with the aid of cooperative SU relays. Efficient power allocation (PA) among SU relays and multi-relay selection (MRS) are a critical problem for operating such networks whereas the interference to the primary user receiver is being kept below a tolerable level and the transmission power requirements of the secondary users are being satisfied. Subsequently, in the paper, we develop the problem to solve the optimal solution for PA and MRS in a collaborative amplify-and-forward-based CCRNs, in terms of maximizing the secrecy rate (SR) of the networks. It is found that the problem is a mixed integer programming problem and difficult to be solved. To cope with this difficulty, we propose a meta-heuristic genetic algorithm-based MRS and PA scheme to maximize the SR of the networks while satisfying transmission power and the interference requirements of the networks. Our simulation results reveal that the proposed scheme achieves near-optimal SR performance, compared to the exhaustive search scheme, and provides a significant SR improvement when compared with some conventional relay selection schemes with equal power allocation.
Log transportation accounts for a significant portion of the total delivered cost of logs due to the considerable number of truckloads between origins and destinations. Hence, an efficient transportation plan at the o...
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Log transportation accounts for a significant portion of the total delivered cost of logs due to the considerable number of truckloads between origins and destinations. Hence, an efficient transportation plan at the operational level can generate cost savings for forest companies. In this paper, a mixedinteger linear programming model is developed for daily routing and scheduling of heterogeneous trucks considering synchronization constraints. Continuous time representation is used for modeling the problem to generate accurate schedules and to synchronize the trucks and loaders at cut blocks and sort yards. Compatibility requirements, overtime, and decisions related to the trucking contractors are incorporated in the model. The outputs of the model include the number of trucks utilized by each contractor, the arrival times and waiting times of trucks at each location, the detailed schedule of loaders, and the amount of overtime assigned to the drivers. To validate the model, it is applied to test problems from a case of a Canadian forest company, where transportation activities are contracted out. Results show it is more economical to pay overtime than dispatching additional trucks to carry logs. Additionally, variable costs and maximum driving time have the most impact on the total transportation cost.
A well-maintained catenary system is crucial to efficient and safe railway operations. Schedule catenary maintenance tasks with consideration of reliability and cost is vital for annual maintenance planning. Since pas...
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A well-maintained catenary system is crucial to efficient and safe railway operations. Schedule catenary maintenance tasks with consideration of reliability and cost is vital for annual maintenance planning. Since past research on catenary maintenance planning mainly adopt the preventive maintenance (PM) policy with fixed maintenance intervals, this study considers practical concerns of railway operators and applies predictive maintenance (PdM) policy in the annual catenary maintenance planning. A decision support model is proposed by using both mixed integer programming and heuristic methods to identify and assign catenary maintenance tasks with the objective of minimizing maintenance cost and labor cost. The numerical results show that the cost can be improved by 25% compared to the current PM-only practice. The proposed model assists planners to determine and schedule maintenance tasks effectively and ensures the required reliability.
This paper proposes a convex model for the optimal service restoration of power distribution networks after a permanent fault. The proposed method minimizes the out of service area providing the minimum switching acti...
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ISBN:
(纸本)9781509042371
This paper proposes a convex model for the optimal service restoration of power distribution networks after a permanent fault. The proposed method minimizes the out of service area providing the minimum switching actions and the tap position of the on load tap changer (OLTC) placed at the HV/MV substations subject to voltage and capacity limits of the distribution network. The nonlinear constraints of the restoration problem are transformed into second-order cone programming constraints providing a convex formulation for the service restoration problem that can be efficiently solved by commercial branch and bound solvers. The proposed method is applied to an 83-bus power distribution network to verify its effectiveness and robustness.
Lot-sizing is a very important problem in production and inventory planning under variable demand. In this work, a general model is proposed for a single-item lot-sizing problem with multiple suppliers, quantity disco...
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Lot-sizing is a very important problem in production and inventory planning under variable demand. In this work, a general model is proposed for a single-item lot-sizing problem with multiple suppliers, quantity discounts, and backordering of shortages. mixed integer programming (MIP) is used to formulate the problem and obtain the optimum solution for small problems. Due to the large number of variables and constraints in practical problems, the model is too difficult for an optimal solution. Therefore, an effective heuristic method is developed by modifying the well-known silver-meal heuristic. This heuristic method is shown to be effective for this problem, producing near-optimal solutions much faster than MIP.
Thanks to the advance of cloud computing technologies, users can access the data stored at cloud data centers at any time and from any where. However, the data centers are usually sparsely distributed over the Interne...
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Thanks to the advance of cloud computing technologies, users can access the data stored at cloud data centers at any time and from any where. However, the data centers are usually sparsely distributed over the Internet and are far away from end users. In this paper, we consider to construct a cache network by a large number of cache nodes close to the end users in order to minimize the data access delay. We firstly formulate the problem of placing the replicas of data items to cache nodes as a mixed integer programming (MIP) problem. Then, we proposed an efficient heuristic algorithm that allocates at least one replica of each data item in the cache network and attempt to allocate more data items so as to minimize the total data access cost. The simulation results show that our proposed algorithm behaves much better than a well-known LRU algorithm and the computation complexity is limited.
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