Optimal scheduling of pumps operation in fluid distribution networks (e.g., oil or water) is an important optimization problem. This is due to the fact that the dollar cost and also global carbon footprints of such a ...
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Optimal scheduling of pumps operation in fluid distribution networks (e.g., oil or water) is an important optimization problem. This is due to the fact that the dollar cost and also global carbon footprints of such a major transportation are in mega scales. For example, one of our industrial partners, a Canadian oil pipeline operator, spent more than $18.11 million dollars in 2008 for pumping costs. According to our calculations, this would lead to up to 182,460 tons of CO2 emissions annually. Therefore, even slight improvements in operation of a pipeline system can lead to considerable savings in costs and also reducing carbon footprints emitted to the environment ( by introducing air pollutions needed to generate those huge amounts of electricity). In this paper, a methodology for determining optimal pump operation schedule for a fluid distribution pipeline system with multi-tariff electricity supply is presented. The optimization problem at hand is a complex task as it includes the extended period hydraulic model represented by algebraic equations as well as mixed-integer decision variables. Obtaining a strictly optimal solution involves excessive computational effort;however, a near optimal solution can be found at significantly reduced effort using heuristic simplifications. The problem is efficiently formulated in this paper based on mixed-integer linear programming. The proposed model is evaluated on a typical oil pipeline network. The numerical results indicate the effectiveness and computationally efficient performance of the proposed formulation.
In the fast moving consumer goods industry there is an ongoing trend towards an increased product variety and shorter replenishment cycle times. Hence, manufacturers seek a better coordination of production and distri...
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In the fast moving consumer goods industry there is an ongoing trend towards an increased product variety and shorter replenishment cycle times. Hence, manufacturers seek a better coordination of production and distribution activities. In this paper, a so-called block planning approach is presented which establishes cyclical production patterns based on the definition of setup families. For the delivery of final goods from the plants to distribution centres two transportation modes are considered, full truckload and less than truckload. The proposed mixed-integerlinear optimization model minimizes total production and transportation costs. Numerical results demonstrate the practical applicability of the proposed block planning approach. In particular, a rigid and a flexible block planning approach are compared which differ by their degree of flexibility in the scheduling of the production lots.
After a number of food safety crises, the design and implementation of traceability systems became an important tool for managing safety risks in the food industry. In the literature, numerous studies deal with tracea...
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After a number of food safety crises, the design and implementation of traceability systems became an important tool for managing safety risks in the food industry. In the literature, numerous studies deal with traceability from the viewpoint of information system and technology development. However, traceability and its implications for food safety receive less attention in literature on production and distribution planning. From the viewpoint of operations management, an efficient management of food safety risks requires the consideration of the amounts of potentially recalled products, affected regions/customers, and logistics efforts connected to solving the safety problem. In this paper we are developing a production and distribution planning model for food supply chains to address these issues. We also present heuristics for solving the resulting mixed-integer linear programming model and demonstrate the effectiveness of the developed methodology in a numerical investigation.
This paper addresses the problem of optimizing production schedules of multiproduct continuous manufacturing facilities where a wide range of products are produced in small quantities, resulting in frequent changeover...
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ISBN:
(纸本)9780878492497
This paper addresses the problem of optimizing production schedules of multiproduct continuous manufacturing facilities where a wide range of products are produced in small quantities, resulting in frequent changeovers. A real-world scheduling problem from the polyamide fiber plant is investigated. The problem involves a sequencing of products. The problem is formulated as a mixed-integer linear programming (MILP) model. The mathematical model has a linear objective function to be maximized. A simulated annealing (SA) algorithm is proposed for this scheduling problem. The computational results show that a satisfactory solution can be obtained in reasonable computation time. Case study demonstrates the effectiveness and the applicability of the model and the proposed methods.
In this paper, we address the problem of medium-term planning of single-stage continuous multiproduct plants with multiple processing units in parallel. Sequence-dependent changeover times and costs occur when switchi...
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In this paper, we address the problem of medium-term planning of single-stage continuous multiproduct plants with multiple processing units in parallel. Sequence-dependent changeover times and costs occur when switching from one type of product to another. A traveling salesman problem (TSP)-based mixed-integer linear programming (MILP) model is proposed based on a hybrid discrete/continuous time representation. We develop additional constraints and variables to ensure that subtours do not occur in the solution. The model is successfully applied to an example of a polymer processing plant to illustrate its applicability. In order to solve larger model instances and planning horizons, a rolling horizon approach is developed to reduce the computational expense. Finally, the proposed model is compared to a recently published approach through literature examples, and the results show that the computational performance of the proposed model is superior.
The point coverage, sink location, and data routing problems are considered in an integrated way and two new mixed-integerprogramming formulations are proposed. As these models are difficult to solve, a nested soluti...
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ISBN:
(纸本)9783642121388
The point coverage, sink location, and data routing problems are considered in an integrated way and two new mixed-integerprogramming formulations are proposed. As these models are difficult to solve, a nested solution procedure is proposed. The best sensor locations are sought by tabu search in the upper level. For the fixed sensor locations, the remaining problem of determining sink locations and data routes are solved efficiently in the lower level. According to the experimental results performed on a number of test instances, the performance of the nested solution approach is quite satisfactory, and the proposed heuristic method brings considerable improvements over a two-stage solution approach.
A personalized driver assisting system that makes use of the driver's behavior model is developed. As a model of driving behavior, the Probability-weighted ARX (PrARX) model, a type of hybrid dynamical system mode...
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ISBN:
(纸本)9781424453634
A personalized driver assisting system that makes use of the driver's behavior model is developed. As a model of driving behavior, the Probability-weighted ARX (PrARX) model, a type of hybrid dynamical system models, is introduced. A PrARX model that describes the driver's vehicle-following skill on expressways is identified using a simple gradient descent algorithm from actual driving data collected on a driving simulator. The obtained PrARX model describes the driver's logical decision making as well as continuous maneuver in a uniform manner. Finally, the optimization of the braking assist is formulated as a mixed-integer linear programming (MILP) problem using the identified driver model, and computed online in the model predictive control framework.
Hybrid electric vehicles are regarded as a possible solution for the reduction of pollutant emissions and for improving the fuel economy. Besides the conventional cooling circuit for the engine, hybrid vehicles need c...
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Hybrid electric vehicles are regarded as a possible solution for the reduction of pollutant emissions and for improving the fuel economy. Besides the conventional cooling circuit for the engine, hybrid vehicles need cooling for the electrical drives and for the energy storage systems as well. The development of appropriate cooling systems has the consequence that the number of auxiliary components involved, the weight and above all the energy consumption is increased. Therefore in order to minimize the energy consumption an optimal strategy for the operation of the cooling aggregates is required. In this paper an approach for finding the optimal control strategy of the electric auxiliaries over an apriori defined driving cycle is introduced. An energy minimization problem with constraints given by the maximum allowed temperature of the components is stated. This problem is based on a nonlinear mathematical model of the cooling system. It is shown how the nonlinear continuous time model can be equivalently replaced by a suitable linear discrete time model where some of the variables are confined to take integer values. This allows us to cast the optimization problem as a mixedintegerlinear program. The proposed approach is demonstrated by an example. For this purpose a cooling system is considered where an electrically driven water pump and an electric cooling fan are involved. As a result the optimal interaction of the water pump and the fan is computed such that the energy consumption of these components is minimized subject to given temperature constraints.
Planning of distributed energy systems is a challenging task,involving a lot of technical,economic,environmental and political factors. In this study,an optimization model based decision support system and the relevan...
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ISBN:
(纸本)9781457700668
Planning of distributed energy systems is a challenging task,involving a lot of technical,economic,environmental and political factors. In this study,an optimization model based decision support system and the relevant software package have been developed to provide comprehensive analysis of economic,energetic and environmental issues within a distributed energy system framework. The optimization problem is formulated as a mixedintegerlinearprogramming (MILP) model where the objective is to minimize the overall cost of the distributed energy system including both investing and running costs. By using a user friendly interface,the system can be used without the necessary to have special expertise and knowledge on energy system planning and decision analysis. In addition,besides the distributed energy system planning,it can be also used for examining and visualizing impacts of local energy and environmental policies,regional development policies,and climate change within a local framework.
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