1,721,023 research outputs found
A two-step optimization model for the distribution of perishable products
No full textOptimal planning for distribution networks of perishable products is addressed by means of a two-step approach pursuing both strategic and tactical goals. The distribution network is represented as a directed graph, and discrete-time dynamic equations are devised to model the storage and transportation of products. The first decision step consists of an optimization problem to account for a strategic viewpoint. In particular, this problem allows to select optimal values for replenishment cycles of products, safety stocks, and amounts of products to be transferred among the nodes of the network by considering uncertainty on long-term demand predictions. The second decision step requires the solution of rolling-horizon optimization problems at the various time buckets that exploit accurate, short-term predictions of customers' demands according to a tactical perspective. In this case, the strategic decisions on the amounts of products to be transferred are tuned according to the available short-term predictions of demands. The effectiveness of the proposed approach is showcased by simulations in a case study, in comparison with a classical lot-for-lot strategy
Optimal Control of Distribution Chains for Perishable Goods
No full textA discrete-time dynamic model of distribution chains for perishable goods is presented together with an approach for its optimal management based on model predictive control. The model is based on a directed graph, with buffers representing the amounts of goods for the various remaining lifetimes, whose time evolution is obtained via balance equations. The amounts of goods to transfer from node to node are chosen by solving a receding-horizon optimal control problem at each time step. The proposed approach allows one to trade among inventory and transportation costs, satisfaction of the customers' demand, and reduction of the amount of wasted goods, namely goods with no remaining lifetime and thus that have to be discarded from the distribution chain. Preliminary simulation results in three scenarios are reported to show the potential of the proposed approach
Optimal and predictive control of distribution chains by using integer tree-based search and mixed-integer programming
No full textThe control of multi-item multi-echelon distribution chains is addressed by using integer tree-based search and mixed-integer programming. Basing on a discrete-time model that describes the exchange of goods inside a generic distribution chain, the decisions on the flows are made by referring to a performance index that accounts for transportation, holding, and backlog costs at two levels, i.e., strategic and tactical. As to the strategic level, a worst-case stock replenishment policy is adopted to exploit the uncertain information available on long-term predictions of customers' demand. The optimal selection of policy parameters such as delivery cycle times of goods is obtained by using a top-down exploration of a tree with leaves associated with min-max subproblems. A heuristic algorithm is presented to explore the tree for finding a suboptimal solution in a reduced number of steps. Such an algorithm is well-suited to being applied to distribution chains with a dimension that prevents from an exhaustive exploration of the leaves. At the tactical level, the on-line decisions on the transportation of goods are taken by using model predictive control, which allows one to take into account recent, reliable, shortterm predictions of the demand. The tactical optimal decisions are obtained by solving mixedinteger programming problems with fewer variables as compared with the strategic setting. Simulation results are presented to assess the potential of the proposed approach in terms of both effectiveness and computational efficiency. © 2012 IFAC
Integer Tree-Based Search and Mixed-Integer Optimal Control of Distribution Chain
No full textThe use of integer tree-based search and mixed-integer programming is investigated for the purpose of control of multi-item multi-echelon distribution chains. A discrete-time model is considered to describe the dynamics of a generic distribution chain. The decisions on the amounts of goods to transfer are made by referring to a performance index that accounts for transportation, storage, and backlog costs at two levels, i.e., strategic and tactical. As to the strategic level, a worst-case stock replenishment policy is adopted to exploit the uncertain information available on long-term predictions of the customers' demand. The solution of such a problem is obtained by using a top-down tree-based algorithm to select policy parameters such as the delivery cycle times of goods. At the tactical level, the on-line decisions on the transportation of goods are taken basing on model predictive control, which allows one to take into account recent, reliable, short-term predictions of the demand. The tactical optimal decisions are obtained by solving mixed-integer programming problems with fewer variables as compared with the strategic setting. Simulation results are presented to show the effectiveness of the proposed approach
Model Predictive Control for the Scheduling of Seedings in an Adaptive Vertical Farm
No full textA model predictive control approach is presented for the scheduling of sowings in an adaptive vertical farm, i.e., an innovative vertical greenhouse in which the spacing between shelves is automatically adapted to crop growth. First, a dynamic model describing the evolution of occupancy and shelf height is developed. The model is affected by disturbances to account for possible deviations of crop growth from the nominal pattern. Then, an optimal control problem over a given timeframe is defined to determine the best time instants to perform seedings in the various shelves with the goal of maximizing production yield. The repeated solution of the optimal control problem over a shorter, moving window over time, according to the receding horizon paradigm, allows to devise robust control strategies with respect to disturbances, even in the absence of predictions about their future realizations. Preliminary simulation results are reported for different control horizons and type of disturbances to showcase the effectiveness of the proposed approach in maximizing production yield while exploiting almost all the available vertical space
Scheduling Landing and Payload Switch of Unmanned Aerial Vehicles on a Single Automatic Platform
No full textWe focus on the problem of optimally managing a set of unmanned aerial vehicles performing given missions that require to land on an automatic platform, unmount the currently-carried payload, and take off with another payload to complete mission objectives. Such a problem often arises when swarms of drones cooperate to complete monitoring applications or other tasks requiring an efficient schedule of landings and payload switches in a resource-constrained environment. First, the problem is formulated as a mixed-integer linear programming one, which, however, may be complex to be solved for a large number of drones. Thus, we also propose a heuristic algorithm able to find suboptimal solutions with a reduced computational effort. Preliminary simulation results are reported and discussed
Stirred Liquid-Liquid Systems: The Effect of the Continuous Phase Viscosity. Experimentation and Modelling
No full textMixed-Integer Linear Programming for the Scheduling of Seedings in an Industrial Adaptive Vertical Farm
No full textWe present a new concept of industrial vertical greenhouse, called adaptive vertical farm, based on the possibility of adapting the distance between the shelves to the growth of the plants cultivated therein. This is possible through a set of sensors able to measure the crop height and a set of actuators to automatically move the shelves. A scheduling approach of seedings is proposed that requires the solution of a mixed-integer linear programming problem to fully utilize all the available vertical space and maximize the production yield. Simulation results obtained when cultivating various types of crops and for different greenhouse configurations in terms of total height and number of shelves are reported. The goal is to evaluate the effectiveness of the proposed scheduling approach and of the adaptive vertical farm concept in general, as compared to a vertical farm with fixed shelves
Mixing in sistemi liquido-liquido: studio dell’effetto della viscosità della fase continua. Sperimentazione e modello fisico-matematico
No full textFast moving horizon state estimation for discrete‐time systems with linear constraints
No full textFast moving horizon state estimation for nonlinear discrete-time systems affected by disturbances is addressed by means of imperfect optimization at each time instant based on few iterations of the gradient, conjugate gradient, and Newton algorithms. Linear constraints on the state vector are taken into account through a projection on the subspace associated with such constraints. The stability of the estimation error for the resulting scheme is proved under suitable conditions. The effectiveness of the proposed approach is showcased via simulation results in comparison with moving horizon estimation based on complete optimization and extended Kalman filtering
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