A reconfigurable manufacturing system can evolve its configuration to offer exactly the capacity and functionality needed for every demand period. For the reconfigurable manufacturing system with multi-part flow-line ...
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A reconfigurable manufacturing system can evolve its configuration to offer exactly the capacity and functionality needed for every demand period. For the reconfigurable manufacturing system with multi-part flow-line configuration simultaneously producing multiple parts within the same family, the production cost and the delivery time are closely related to its configuration and corresponding scheduling for certain demand period. Although studies on multi-part flow-line configuration design are abundant, studies on concurrent optimization of configuration design and scheduling for reconfigurable manufacturing system are scarce. First, a generic mixedinteger nonlinear programming model for concurrent configuration design and scheduling is established to relax the limitation of the existing model, and then a mixedinteger linear programming model is derived. The decisions of the two generalized models are to decide the amount of stations, the amount of identical machines and machines' configuration for every station, and assign parts to machines along the multi-part flow line together with sequencing assigned parts for each machine. Based on the mixedinteger linear programming model, an exact epsilon-constraint method is developed to obtain the Pareto optimal solutions with tradeoffs between cost and tardiness. The validation of two models and the epsilon-constraint method is verified against two cases adapted from the literature.
The cutting operation in the high fashion clothing industry essentially involves putting several layers of cloth on a long cutting table and fixing templates of the parts of several articles on top of the stack before...
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The cutting operation in the high fashion clothing industry essentially involves putting several layers of cloth on a long cutting table and fixing templates of the parts of several articles on top of the stack before the actual cutting can be initiated. This is a very time-consuming task giving raise to high setup costs in addition to waste production resulting from the cutting process. Total production costs can then be optimized by minimizing the number of these setups while at the same time producing little or no waste. In this paper a mixed integer programming model is proposed that searches for an optimal set of cutting patterns, each giving a combination of articles to be cut in one operation, and corresponding stack heights.
The effectiveness of radiation therapy for cancer depends on the patient remaining still during treatment. It is thus important to minimize the total treatment time (TTT). When such treatment is delivered using multil...
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The effectiveness of radiation therapy for cancer depends on the patient remaining still during treatment. It is thus important to minimize the total treatment time (TTT). When such treatment is delivered using multileaf collimators in "step-and-shoot" mode, it consists of a sequence of collimator configurations. or patterns for each, the patient is exposed to radiation for a specified time. or beam-on-time. The TTT can thus he divided into the total beam-on time and the time spent reconfiguring the collimators. The latter can reasonably be approximated by the number of patterns, multiplied by a constant overhead time per pattern. Previous approaches to this problem have all been heuristic;in particular none of them actually use the pattern overhead time to ascertain the best trade-off between beam-on time and number of patterns. In this paper, we develop exact Solution approaches, based on mixed integer programming (MIP) formulations. which minimize the TTT. We consider direct Solution of MIP formulations, and then exploit the bicriteria structure of the objective to derive an algorithm that "steps up" through the number of patterns used, leading to substantial computational savings. (C) 2007 Elsevier Ltd. All rights reserved.
This article proposes a mixedinteger linear programming (MILP)-based algorithm to estimate faults locations, types, and fault current magnitudes in unbalanced three-phase distribution networks. The proposed method re...
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This article proposes a mixedinteger linear programming (MILP)-based algorithm to estimate faults locations, types, and fault current magnitudes in unbalanced three-phase distribution networks. The proposed method requires voltage phasors prior to and during fault conditions. The measurements are collected by microPMUs at the end of the branches along with the bus impedance matrix. To assess the proposed technique's performance in fault location and current identification, different types of faults scenarios are considered. Balanced and unbalanced faults are examined on the IEEE 37-bus, the IEEE 123-bus, and 134-node real-life feeders. Efficiency of the proposed method is investigated on ungrounded systems, reduced microPMU number, different fault resistances, inaccurate bus impedance matrix, distributed generation penetration, and noisy measurement data. High accuracy rate is achieved by the proposed method in identifying fault locations, types, and current magnitudes.
In this paper, the computational performance of four different mixed integer programming (MIP) formulations for various single machine scheduling problems is studied. Based on the computational results, we discuss whi...
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In this paper, the computational performance of four different mixed integer programming (MIP) formulations for various single machine scheduling problems is studied. Based on the computational results, we discuss which MIP formulation might work best for these problems. The results also reveal that for certain problems a less frequently used MIP formulation is computationally more efficient in practice than commonly used MIP formulations. We further present two sets of inequalities that can be used to improve the formulation with assignment and positional date variables. (C) 2008 Elsevier Ltd. All rights reserved.
Placement is a critical step in the physical design for digital application specific integrated circuits (ASICs), as it can directly affect the design qualities such as wirelength and timing. For many domain specific ...
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Placement is a critical step in the physical design for digital application specific integrated circuits (ASICs), as it can directly affect the design qualities such as wirelength and timing. For many domain specific designs, the demands for high performance parallel computing result in repetitive hardware instances, such as the processing elements in the neural network accelerators. As these instances can dominate the area of the designs, the runtime of the complete design's placement can be traded for optimizing and reusing one instance's placement to achieve higher quality. Therefore, this work proposes a mixed integer programming (MIP)-based placement refinement algorithm for the repetitive instances. By efficiently modeling the rectilinear steiner tree wirelength, the placement can be precisely refined for better quality. Besides, the MIP formulations for timing-driven placement are proposed. A theoretical proof is then provided to show the correctness of the proposed wirelength model. For the instances in various popular fields, the experiments show that given the placement from the commercial placers, the proposed algorithm can perform further placement refinement to reduce 3.76%/3.64% detailed routing wirelength and 1.68%/2.42% critical path delay under wirelength/timing-driven mode, respectively, and also outperforms the state-of-the-art previous work.
A two-machine flow shop scheduling scenario with an availability constraint on one of the two machines is considered. Seven mixed integer programming formulations (MIPFs) are proposed for the problem where the availab...
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A two-machine flow shop scheduling scenario with an availability constraint on one of the two machines is considered. Seven mixed integer programming formulations (MIPFs) are proposed for the problem where the availability constraint is imposed on the first machine. Seven analogs are proposed for its counterpart. Size complexity analysis of these MIPFs is provided. Numerical results indicate that, for either one of the two problems, each of the corresponding first three MIPFs can solve instances of size up to 100 jobs in reasonable times.
Nowadays flexibility is a strategic concept for firms. Indeed workload has to follow, as close as possible, the development of demand throughout the year. However, firms cannot engage and dismiss employees according t...
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Nowadays flexibility is a strategic concept for firms. Indeed workload has to follow, as close as possible, the development of demand throughout the year. However, firms cannot engage and dismiss employees according to production requirements. Thus, workforce scheduling becomes a delicate task. In this paper, four mixed integer programming models are developed to solve the workforce schedule problem for a single- shift. The annualized hour scenario is considered with respect to a set of Swiss legal constrains. Furthermore, the minimal required workforce is guaranteed and it is assumed that each employee is able to perform each task within the team. All employees are full- time workers.
This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these ...
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This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0 - 1 integerprogramming models, Model I, Model II and Model III are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model I and Model II, as pure 0-1 integerprogramming problems with simple constraint sets and present a lower bound for the optimal value of Model I. Model III is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model III is the best among these three models and the greedy algorithm is capable of solving large scale problems.
In this paper, mixed integer programming (MIP) formulations are proposed to obtain the optimal capacity of the battery energy storage system (BESS) in a power system. Two optimization problems will be investigated: (1...
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In this paper, mixed integer programming (MIP) formulations are proposed to obtain the optimal capacity of the battery energy storage system (BESS) in a power system. Two optimization problems will be investigated: (1) When the BESS is owned by a utility, the operation cost of generators and cost of battery will be minimized. Generator on/off states, dispatch level and battery power dispatch level will be determined for a 24-h period. (2) When the BESS is owned by a community for peak shaving, the objective function will have a penalty component for the deviation of the imported power from the scheduled imported power. The battery sizing parameters, power limit and energy limit, are treated as decision variables in the optimization problems. In both cases, switchable loads are considered. Further, constrains of switchable loads are included in the optimization problem to show their impact on battery sizing. MIP problems are solved by CPLEX. The simulation results present the effect of switchable load penetration level on battery sizing parameters.
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