A math programming model is formulated for selecting assembly stations and assigning operations to these stations so as to satisfy a production volume requirement at minimum system cost. A branch-and-bound algorithm, ...
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A math programming model is formulated for selecting assembly stations and assigning operations to these stations so as to satisfy a production volume requirement at minimum system cost. A branch-and-bound algorithm, coupled wiht a subgradient optimization procedure, is proposed. The model and algorithm are demonstrated by example on a system design problem for assembling automobile alternators. The model is applicable to many kinds of manufacturing systems.
We give a short review of existing mathematical programming based bounds for kissing numbers. The kissing number in K dimensions is the maximum number of unit balls arranged around a central unit ball in such a way th...
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This paper deals with mathematical programming model of city and urban traffic. The aim of our research is to acquire ideal images of city and urban traffic. To achieve the aim, we propose optimization models which co...
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This paper considers some theoretical and computational problems that arise when trying to find optimal taxes for environmental pollution control. The paper takes cognizance of the reality of mixed-economy difficultie...
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To increase revenues, pharmaceutical companies rely heavily on their sales forces to promote new and existing drugs to physicians. Compensation for sales representatives is largely commission based. However, individua...
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To increase revenues, pharmaceutical companies rely heavily on their sales forces to promote new and existing drugs to physicians. Compensation for sales representatives is largely commission based. However, individual effort may not be the driving force behind compensation. Sales representatives might be unfairly rewarded only because they are assigned to physicians with high sales potential. Given a set of physicians and representatives, a mathematical program is developed to maximize profit for the pharmaceutical company, while balancing both workload and sales opportunities for representatives. The model is valuable for daily operational decisions. The methodology applies to any multi-product sales-force based industry.
Dynamic airspace configuration research intends to organize and reconstruct the airspace to better accommodate the uneven traffic distribution, alleviate the demand-capacity imbalances, and thereby increase the throug...
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The main problem of conformance testing is the time consuming measurements, the enormous number of the possible test suites and the long time it takes to run them all. Therefore, test selection is required. In this pa...
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The main problem of conformance testing is the time consuming measurements, the enormous number of the possible test suites and the long time it takes to run them all. Therefore, test selection is required. In this paper we describe a new method of selecting an optimal test suite which can detect the errors with better probability and maximize the number of detected errors. We will utilize mathematical programming based on a simplified model of the test-error connection. Application for the protocol ISDN DSS1 Layer 2 (LAPD) is included.
Heat integration is important for energy-saving in the process *** is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and co...
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Heat integration is important for energy-saving in the process *** is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale *** reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such ***,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both *** insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy *** results are obtained from three literature cases of different scales.
This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete even...
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This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete event systems due to the difficulty of formulating models capable to correctly represent the system dynamics. This work connects the two fruitful research fields, i.e., mathematical programming and Timed Petri Nets. In the MP formalism, the decision variables of the model correspond to the transition firing times and the markings of the TPN, whereas the constraints represent the state transition logic and temporal sequences among events. The MP model and a simulation run of the TPN are then totally equivalent, and this equivalence has been validated through an application in the queuing network field. Using a TPN model as input, the MP model can be routinely generated and used as a white box for further tasks such as sensitivity analysis, cut generation in optimization procedures, and proof of formal properties.
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