This paper highlights the subject of integrated projects planning (IPP) in contemporary IS departments, and presents a multi-period, multi-project selection and assignment approach (MPPA) to assist the departments in ...
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This paper highlights the subject of integrated projects planning (IPP) in contemporary IS departments, and presents a multi-period, multi-project selection and assignment approach (MPPA) to assist the departments in handling continuous project-based IS requests. The MPPA features a model to optimize the selection and assignment of IS projects. In the scope of multi-project, multi-period planning, the model innovatively considers the losses due to (1) the accumulated postponement of a previously unselected IS request and (2) the expected delay of ongoing projects when inserting a new project request. The MPPA also features an event-based decisional process for cumulative selection and assignment on a multi-period basis. Due to the complex and contextual nature of data in this paper, a computerized system is implemented for aiding the execution of the model and the process. The paper reports on an industrial case for a demonstration of the proposed work. Finally the paper compares the MPPA with related work to summarize the value and role it may play in the IPP context. (C) 2013 Elsevier B.V. All rights reserved.
In automated electroplating lines, computer-controlled hoists are used to transfer parts from a processing resource to another one. Products are mounted into carriers and immersed sequentially in a series of tanks fol...
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In automated electroplating lines, computer-controlled hoists are used to transfer parts from a processing resource to another one. Products are mounted into carriers and immersed sequentially in a series of tanks following a given sequence. The aim of this study is to propose an algorithm to solve the two-hoist cyclic scheduling problem. This problem consists in finding a repetitive sequence of hoists' moves, while avoiding collision between the hoists which share a common track. The objective is to minimize the period of this repetitive cycle. First, a set of sequences is generated. After that, for each sequence, an algorithm is applied to assign moves to hoists. Finally, a mixedinteger linear programming model is formulated by taking into account the two sequences of hoists moves. Then it is solved to give the starting times of moves. (C) 2013 Elsevier Ltd. All rights reserved.
Data centers consume a phenomenal amount of energy, which can be significantly reduced by appropriately allocating resources using technologies such as virtualization, speed scaling, and powering off servers. This art...
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Data centers consume a phenomenal amount of energy, which can be significantly reduced by appropriately allocating resources using technologies such as virtualization, speed scaling, and powering off servers. This article proposes a unified methodology that combines these technologies under a single framework to efficiently operate data centers. In particular, a large-scale mixedinteger Program (MIP) is formulated that prescribes optimal allocation of resources while incorporating inherent variability and uncertainty of workload experienced by the data center. However, only for small to medium-sized clients it is possible to solve the MIP using commercial optimization software packages in a reasonable time. Thus, for large-sized clients a heuristic method is developed that is effective and fast. An extensive set of numerical experiments is performed to illustrate the methodology, obtain insights on the allocation policies, evaluate the quality of the proposed heuristic, and test the validity of the assumptions made in the literature. The results show that gains of up to 40% can be obtained by using the integrated approach rather than the traditional approach where virtualization, dynamic voltage/frequency scaling, and powering off servers are done separately.
This paper proposes an optimal bidding strategy for independent power producers (IPPs) in the deregulated electricity market. The IPPs are assumed to be price takers, whose objectives are to maximize their profits con...
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This paper proposes an optimal bidding strategy for independent power producers (IPPs) in the deregulated electricity market. The IPPs are assumed to be price takers, whose objectives are to maximize their profits considering price and wind power output uncertainties, while ensuring high wind power utilization. The problem is formulated as a two-stage stochastic price-based unit commitment problem with chance constraints to ensure wind power utilization. In our model, the first stage decision includes unit commitment and quantity of electricity submitted to the day-ahead market. The second stage decision includes generation dispatch, actual usage of wind power, and amount of energy imbalance between the day-ahead and real-time markets. The chance constraint is applied to ensure a certain percentage of wind power utilization so as to comply with renewable energy utilization regulations. Finally, a sample average approximation (SAA) approach is applied to solve the problem, and the computational results are reported for the proposed SAA algorithm showing the sensitivity of the total profit as the requirement of wind power utilization changes.
In this paper, we study flexible mixed model assembly lines with serially connected stages where stages may have one or more parallel stations. Station parallelism in flexible mixed model assembly lines improves syste...
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In this paper, we study flexible mixed model assembly lines with serially connected stages where stages may have one or more parallel stations. Station parallelism in flexible mixed model assembly lines improves system reliability and enables rapid adaptability to customer demand fluctuations. However, in order to effectively manage such lines under real-world conditions, there is a need to extend traditional assembly line management methods. This study extends the current research by simultaneously considering the problems of task assignment and model scheduling on parallel stations. Furthermore, scheduling of assigned tasks on each station is considered. We formulate the problem context first as a mixed integer programming model and then develop a decomposition scheme for large scale applications. Finally, we compare the performance of the proposed model and decomposition scheme using various size test instances.
mixed model assembly line literature involves two problems: balancing and model sequencing. The general tendency in current studies is to deal with these problems in different time frames. However, in today's comp...
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mixed model assembly line literature involves two problems: balancing and model sequencing. The general tendency in current studies is to deal with these problems in different time frames. However, in today's competitive market, the mixed model assembly line balancing problem has been turned into an operational problem. In this paper, we propose mixed integer programming (MIP) and constraint programming (CP) models which consider both balancing and model sequencing within the same formulation along with the optimal schedule of tasks at a station. Furthermore, we also compare the proposed exact models with decomposition schemes developed for solving different instances of varying sizes. This is the first paper in the literature which takes into account the network type precedence diagrams and limited buffer capacities between stations. Besides, it is the first study that CP method is applied to balancing and scheduling of mixed model assembly lines. Our empirical study shows that the CP approach outperforms the MIP approach as well as the decomposition schemes.
In selecting sites for conservation purposes connectivity of habitat is important for allowing species to move freely within a protected area. The aim of the Reserve Network Design Problem is to choose a network of co...
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In selecting sites for conservation purposes connectivity of habitat is important for allowing species to move freely within a protected area. The aim of the Reserve Network Design Problem is to choose a network of contiguous sites which maximises some conservation objective subject to various constraints. The problem has been solved using both heuristic and exact methods. Heuristic methods can handle much larger problems than exact methods but cannot guarantee an optimal solution. Improvements in both computer power and optimisation algorithms have increased the attractiveness of exact methods. The aim of this work is to formulate an improved algorithm for solving the Reserve Network Design Problem. Based on the concept of the transshipment problem a mixed integer programming model is formulated that achieves contiguity of the selected sites. The model is simpler in concept and to implement than previous exact models and does not require any assumptions about the regular shape of candidate sites. The method easily handles the case where more than one reserve system is required. We illustrate this with an example obtaining the trade-off between the number of contiguous areas and utility. We also illustrate that the important property of compactness can be achieved while maintaining contiguity of selected sites. (C) 2013 Elsevier B.V. All rights reserved.
This paper compares the efficacy of a newly developed network-based mixed-integerprogramming (MIP) formulation with three existing formulations for the sequence dependent setup scheduling problem with earliness/tardi...
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This paper compares the efficacy of a newly developed network-based mixed-integerprogramming (MIP) formulation with three existing formulations for the sequence dependent setup scheduling problem with earliness/tardiness penalties. This research shows that the new model is more efficient in terms of computation time for larger multi-machine problems than the existing formulations of these problems. The mixed-integer nature of the formulation allows companies to solve this class of problems with any one of many commonly available integerprogramming software packages. The presented MIP formulation provides a unique and useful method of conceptualizing and modeling a practical, yet difficult, problem within industry. Published by Elsevier Ltd
In this paper,we consider approximation algorithms for optimizing a generic multivariate polynomial function in discrete(typically binary)*** models have natural applications in graph theory,neural networks,error-corr...
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In this paper,we consider approximation algorithms for optimizing a generic multivariate polynomial function in discrete(typically binary)*** models have natural applications in graph theory,neural networks,error-correcting codes,among many *** particular,we focus on three types of optimization models:(1)maximizing a homogeneous polynomial function in binary variables;(2)maximizing a homogeneous polynomial function in binary variables,mixed with variables under spherical constraints;(3)maximizing an inhomogeneous polynomial function in binary *** propose polynomial-time randomized approximation algorithms for such polynomial optimizationmodels,and establish the approximation ratios(or relative approximation ratios whenever appropriate)for the proposed *** examples of applications for these models and algorithms are discussed as well.
This paper considers the block relocation problem (BRP), in which a set of identically-sized items is to be retrieved from a set of last-in-first-out (LIFO) stacks in a specific order using the fewest number of moves....
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This paper considers the block relocation problem (BRP), in which a set of identically-sized items is to be retrieved from a set of last-in-first-out (LIFO) stacks in a specific order using the fewest number of moves. The problem is encountered in the maritime container shipping industry and other industries where inventory is stored in stacks. After surveying the work done on the BRP, we introduce "BRP-III"-a new mathematical formulation for the BRP and show that it has considerably fewer decision variables and better runtime performance than the other formulation in the literature. We then introduce a new look-ahead algorithm (LA-N) that is an extension of the algorithms from the literature and show that the new algorithm generally obtains better solutions than the other algorithms and has minimal CPU runtime. (C) 2013 Elsevier B.V. All rights reserved.
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