Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid...
详细信息
Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid and may lower their electricity costs. This paper proposes a methodology to minimize the electricity cost of a grid-connected factory that also has onsite solar power generation and battery storage. Purchases from the grid are subject to time-of-use electricity rate schedules. The problem is formulated as a mixed-integer programming problem and GAMS is used to find the optimal manufacturing and onsite energy flow schedules that have the minimal electricity cost. A case study with one hybrid flow shop, onsite PV power generation, and a battery was used to test the proposed method. Testing results showed that the factory's electricity cost can be reduced by 54.0% under summer TOU rate on a typical day while a 0.7% electricity cost reduction can be achieved for a representative day under a winter TOU rate. An annual electricity cost savings of 28.1% can be obtained with the optimal schedules. In addition, a parametric study incorporating the optimal schedules was performed to understand the economic performances associated with different PV capacity and battery bank size for the factory.
Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is intr...
详细信息
Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study. (C) 2014 Elsevier Ltd. All rights reserved.
This paper studies beamforming techniques for energy efficiency maximization (EEmax) in multiuser multiple-input single-output (MISO) downlink system. For this challenging nonconvex problem, we first derive an optimal...
详细信息
This paper studies beamforming techniques for energy efficiency maximization (EEmax) in multiuser multiple-input single-output (MISO) downlink system. For this challenging nonconvex problem, we first derive an optimal solution using branch-and-reduce-and-bound (BRB) approach. We also propose two low-complexity approximate designs. The first one uses the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference so that the EEmax problem reduces to a concave-convex fractional program. Particularly, the problem is then efficiently solved by closed-form expressions in combination with the Dinkelbach's approach. In the second design, we aim at finding a stationary point using the sequential convex approximation (SCA) method. By proper transformations, we arrive at a fast converging iterative algorithm where a convex program is solved in each iteration. We further show that the problem in each iteration can also be approximated as a second-order cone program (SOCP), allowing for exploiting computationally efficient state-of-the-art SOCP solvers. Numerical experiments demonstrate that the second design converges quickly and achieves a near-optimal performance. To further increase the energy efficiency, we also consider the joint beamforming and antenna selection (JBAS) problem for which two designs are proposed. In the first approach, we capitalize on the perspective reformulation in combination with continuous relaxation to solve the JBAS problem. In the second one, sparsity-inducing regularization is introduced to approximate the JBAS problem, which is then solved by the SCA method. Numerical results show that joint beamforming and antenna selection offers significant energy efficiency improvement for large numbers of transmit antennas.
In this paper, we propose a method for N - 1 contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power...
详细信息
In this paper, we propose a method for N - 1 contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power systems, a single outage of a majority of lines will not usually cause overload on other lines in most loading conditions. Thus they will not affect the feasible region and the optimal answer of the TCEP optimization problem, and can be safely removed from contingency analysis if we can identify them. A contingency identification index is developed to detect these lines and create variable contingency lists (VCL) for different network loading conditions. In our proposed method, we use results of a relaxed version of the original problem as a lower bound answer in the first step, and integrate contingencies into TCEP in the next steps to solve this optimization problem faster while still satisfying N - 1 criterion. For solving TCEP with contingencies, two options are offered, i. e., option A that uses an updated system as its base case (original existing network together with selected lines by the relaxed problem) and option B that uses the original existing network as its base case (without results of the relaxed problem). Option A is faster than option B because it usually should select fewer new lines compared to B, but cannot guarantee optimality. Option B provides the optimal answer while taking more computational time. An ERCOT case study is used to show capabilities of the proposed method for solving large scale problems, and the numerical result demonstrates this method is much faster than the integrated MIP method that directly incorporates all contingencies.
This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among facto...
详细信息
This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among factories and can be delivered to the customer in batches which have limited capacity. The aim of this optimization problem is threefold: (1) determining the number of batches, (2) assigning jobs to batches,and (3) scheduling the batches production and delivery in each factory. The proposed problem formulated as a mixed-integer linear program. Then the model's performance is analyzed and evaluated through two examples. Moreover, a knowledge-based imperialistic competitive algorithm (KBICA) is also presented to find an approximate optimum solution for the problem. Computational experiments of the proposed problem investigate the efficiency of the method through different sizes of the test problems. (C) 2016 Elsevier Ltd. All rights reserved.
In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account th...
详细信息
In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account the action of the competing generation companies and the market-price formation rules and must be coordinated with long-term planning results. This paper presents a three stage optimization process with a data analysis and parameter calculation, a linearized unit commitment, and a nonlinear generation scheduling refinement. Although the procedure has been developed from the experience with the Spanish power market, with minor adaptations it is also applicable to any generation company participating in a competitive market system. (C) 2006 Elsevier Ltd. All rights reserved.
This paper considers a minimum-cost network flow problem in a bipartite graph with a single sink. The transportation costs exhibit a staircase cost structure because such types of transportation cost functions are oft...
详细信息
This paper considers a minimum-cost network flow problem in a bipartite graph with a single sink. The transportation costs exhibit a staircase cost structure because such types of transportation cost functions are often found in practice. We present a dynamic programming algorithm for solving this so-called single-sink, fixed-charge, multiple-choice transportation problem exactly. The method exploits heuristics and lower bounds to peg binary variables, improve bounds on flow variables, and reduce the state-space variable. In this way, the dynamic programming method is able to solve large instances with up to 10,000 nodes and 10 different transportation modes in a few seconds, much less time than required by a widely used mixed-integer programming solver and other methods proposed in the literature for this problem.
The modeling of time plays a key role in the formulation of mixed-integer programming (MIP) models for scheduling, production planning, and operational supply chain planning problems. It affects the size of the model,...
详细信息
The modeling of time plays a key role in the formulation of mixed-integer programming (MIP) models for scheduling, production planning, and operational supply chain planning problems. It affects the size of the model, the computational requirements, and the quality of the solution. While the development of smaller continuous-time scheduling models, based on multiple time grids, has received considerable attention, no truly different modeling methods are available for discrete-time models. In this paper, we challenge the long-standing belief that employing a discrete modeling of time requires a common uniform grid. First, we show that multiple grids can actually be employed in discrete-time models. Second, we show that not only unit-specific but also task-specific and material-specific grids can be generated. Third, we present methods to systematically formulate discrete-time multi-grid models that allow different tasks, units, or materials to have their own time grid. We present two different algorithms to find the grid. The first algorithm determines the largest grid spacing that will not eliminate the optimal solution. The second algorithm allows the user to adjust the level of approximation;more approximate grids may have worse solutions, but many fewer binary variables. Importantly, we show that the proposed models have exactly the same types of constraints as models relying on a single uniform grid, which means that the proposed models are tight and that known solution methods can be employed. The proposed methods lead to substantial reductions in the size of the formulations and thus the computational requirements. In addition, they can yield better solutions than formulations that use approximations. We show how to select the different time grids, state the formulation, and present computational results. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper, we analyse a service provider's mixed bundling problem for services such as sporting events or holiday packages. Pursuing the objective of maximising total revenue, the service provider has to deter...
详细信息
In this paper, we analyse a service provider's mixed bundling problem for services such as sporting events or holiday packages. Pursuing the objective of maximising total revenue, the service provider has to determine static prices for each single product at the beginning of the selling period. Additionally, an optimal package price has to be chosen for the bundle that comprises one unit of each single product. Because of capacity constraints, the availability of products can change over time such that consumers are forced to switch from their preferred subset of products to an alternative following dynamic substitution. We propose two mixed-integer linear programmes based on reservation prices that appropriately model the consumer choice process to address the bundling problem. It becomes evident that the determination of the optimal prices is computationally expensive even for small problem classes. Therefore, we develop metaheuristics using variable neighbourhoods. To evaluate their performance, we propose the following new approach: our extensive computational study is performed using especially generated scenarios for which the optimal product prices are known. For this purpose, we present a set of conditions for the generation of reservation prices that guarantee the optimality of the predefined prices. Based on our computational results, managerial insights are derived. (C) 2013 Elsevier B.V. All rights reserved.
We consider several variants of the two-level lot-sizing problem with one item at the upper level facing dependent demand, and multiple items or clients at the lower level, facing independent demands. We first show th...
详细信息
We consider several variants of the two-level lot-sizing problem with one item at the upper level facing dependent demand, and multiple items or clients at the lower level, facing independent demands. We first show that under a natural cost assumption, it is sufficient to optimize over a stock-dominant relaxation. We further study the polyhedral structure of a strong relaxation of this problem involving only initial inventory variables and setup variables. We consider several variants: uncapacitated at both levels with or without start-up costs, uncapacitated at the upper level and constant capacity at the lower level, constant capacity at both levels. We finally demonstrate how the strong formulations described improve our ability to solve instances with up to several dozens of periods and a few hundred products.
暂无评论