This paper addresses the problem of the on-line scheduling of a limited communication resource in order to optimize the control performance. A multivariable linear system with communication constraints is modeled in t...
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This paper addresses the problem of the on-line scheduling of a limited communication resource in order to optimize the control performance. A multivariable linear system with communication constraints is modeled in the mixed Logical Dynamical (MLD) framework. The system is controlled using a Model Predictive Controller (MPC), which computes, at each sampling period, the appropriate control values and network allocation. The performance of the controlled system is evaluated using a Linear-Quadratic cost function. At each step, the MPC needs to solve an optimization problem, including logic constraints. The translation of this problem into the mixedinteger Quadratic programming (MIQP) formulation is described. Finally, using a numerical example, the relationship between the state variables of the plant and the resultant allocation of the communication resource is investigated.
In this research, an alternative parallel assembly line design strategy for multi products is proposed. Assigning operators to the parallel assembly stations optimally is an essential task specifically when the produc...
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In this research, an alternative parallel assembly line design strategy for multi products is proposed. Assigning operators to the parallel assembly stations optimally is an essential task specifically when the production volume is considerably high and the numbers of required operators are more than the number of assembly operations. The primary objective in the parallel assembly line problem is to balance more than one assembly line together. To deal with this problem a three phase hierarchy methodology is developed: (1) based on the assembly network for each product, operations are grouped into stations for various configuration. (2) In order to determine how many operators should be assigned to each station, a mathematical model is used and parallel stations are designed. (3) The number of assembly lines is determined with the objective of minimizing total number of operators required in the system. This approach guarantees that minimum number of workers is obtained thus maximizing overall system efficiency. This study is the extension of Suer’s (1998) study in which only one product was considered.
The scale of freight forwarding to the hinterland becomes an issue from the perspective of both – transport policy and cost efficiency of service providers. This problem is sharply visible in areas where ports, depot...
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The scale of freight forwarding to the hinterland becomes an issue from the perspective of both – transport policy and cost efficiency of service providers. This problem is sharply visible in areas where ports, depots, inland intermodal terminals, exporters and importers are located, and full and empty containers satisfying demand and supply are frequently distributed creating a lot of traffic. Therefore solutions meeting the challenges of sustainable transport, responding to climate change and regulation of CO2 emissions are in need. In this paper, a variant of a mixed Fleet Heterogeneous Dial-a-Ride Problem is proposed for optimal routing of trucks carrying full and empty 20-foot and 40-foot containers, with multiple pick-ups and deliveries. Transportation is performed by alternatively fueled vehicles (AFVs) for environmental reasons which causes a constraint of a limited driving range and a need of refueling. The main objective is minimising the total distance subject to matching the empty container demand and supply, necessary refueling of the trucks, and service time windows.
Demand response has become a topic of significant research, development, and deployment over the last few years. The energy demand management is a critical task in industrial process systems for the potential benefits...
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Demand response has become a topic of significant research, development, and deployment over the last few years. The energy demand management is a critical task in industrial process systems for the potential benefits to be realized by promoting the interaction and responsiveness of process operation. However, the dynamic behavior, especially transition trajectories, of the underlying process is seldom taken into account during this task. Furthermore, the incorporation of energy constraints related to electricity pricing and availability is one of the key challenges in this process. The purpose of this study is thus to present a novel optimization formulation for energy demand management in dynamic process systems that takes transition behavior and cost explicitly into account, while simultaneously handling time-sensitive electricity prices. This is accomplished by bringing together production scheduling and transition control through a real-time optimization framework. The dynamic formulation is cast as a mixed-integer nonlinear programming problem and demonstrated using a continuous stirred tank reactor example where the energy required is assumed to be roughly proportional to the material flow.
An electric road system (ERS) is a road in which vehicles can travel powered from the electrical grid. Deployed at scale, this technology reduces or eliminates the need for electric vehicles (EVs) to stop for rechargi...
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An electric road system (ERS) is a road in which vehicles can travel powered from the electrical grid. Deployed at scale, this technology reduces or eliminates the need for electric vehicles (EVs) to stop for recharging, and allows for equipping these vehicles with smaller batteries. In particular, it facilitates the decarbonisation of road freight transportation. In this paper, we present a routing problem for a hybrid vehicle travelling on a ERS road network, using estimations of power requirements of the vehicle. We formulate the problem as a mixedinteger linear and use it to present numerical experiments on a road network electrified in three stages, showing how the technology can help reducing fuel usage and have a global impact on how the roads in the network are used.
This paper describes mixedinteger nonlinear programming (MINLP) heuristics for solving dynamic scheduling problems in complex petroleum production systems with a network topology. We modify the Feasibility Pump heuri...
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This paper describes mixedinteger nonlinear programming (MINLP) heuristics for solving dynamic scheduling problems in complex petroleum production systems with a network topology. We modify the Feasibility Pump heuristic for convex MINLPs [Bonami and Gonçalves, 2010] by formulating a multiobjective problem, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving solution quality with respect to the objective value. We further present a simple linearization-based heuristic, only aimed at quickly generating feasible solutions. The MINLP heuristics are applied to a dynamic multi-pipeline shale well and compressor scheduling problem, targeted on application in decision-support tools for improving operations in large shale-gas systems. Developing efficient and robust heuristics are important for the applicability of these tools, in the sense that low computation times are often more important than global optima. A computational study shows that the proposed objective-oriented Feasibility Pump is competitive both in terms of solution quality and computation time compared to other heuristics and the branch-and-bound method.
We propose a mixed-integer quadratic programming (QP) solver that is suitable for use in embedded applications, for example, hybrid model predictive control (MPC). The solver is based on the branch-and-bound method, a...
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We propose a mixed-integer quadratic programming (QP) solver that is suitable for use in embedded applications, for example, hybrid model predictive control (MPC). The solver is based on the branch-and-bound method, and uses a recently proposed dual active-set solver for solving the resulting QP relaxations. Moreover, we tailor the search of the branch-and-bound tree to be suitable for embedded applications on limited hardware; we show, for example, how a node in the branch-and-bound tree can be represented by only two integers. The embeddability of the solver is shown by successfully running MPC of an inverted pendulum on a cart with contact forces on an MCU with limited memory and computing power.
This paper addresses drayage routing problems with heterogeneous fleets, compatibility con-straints, and truck load configurations. In these problems, containers can be of any size and cargo category. In addition, com...
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This paper addresses drayage routing problems with heterogeneous fleets, compatibility con-straints, and truck load configurations. In these problems, containers can be of any size and cargo category. In addition, compatibility and load configuration constraints define which and how many containers can be transported by each truck. We propose a state transition logic to model these constraints. Based on this logic, we develop two mixed-integer programming models for this family of problems. The first model is a compact formulation that can be input into a black-box solver. The second model combines partial routes in a space-time network and is solved with a tailored branch-and-cut approach. To analyze the efficiency of the proposed models, we conduct extensive computational tests on instances with different numbers of requests, geographical distributions of locations, time-window lengths, and fleet compositions. Moreover, we discuss how our modeling approach can assist decision-making in three different drayage routing applications.
In this study we consider the shortest path problem, where the arc costs are subject to distributional uncertainty. Basically, the decision-maker attempts to minimize her worst-case expected loss over an ambiguity set...
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In this study we consider the shortest path problem, where the arc costs are subject to distributional uncertainty. Basically, the decision-maker attempts to minimize her worst-case expected loss over an ambiguity set (or a family) of candidate distributions that are consistent with the decision-maker's initial information. The ambiguity set is formed by all distributions that satisfy prescribed linear first-order moment constraints with respect to subsets of arcs and individual probability constraints with respect to particular arcs. Under some additional assumptions the resulting distributionally robust shortest path problem (DRSPP) admits equivalent robust and mixed-integer programming (MIP) reformulations. The robust reformulation is shown to be NP-hard, whereas the problem without the first-order moment constraints is proved to be polynomially solvable. We perform numerical experiments to illustrate the advantages of the considered approach;we also demonstrate that the MIP reformulation of DRSPP can be solved effectively using off-the-shelf solvers. CO 2021 Elsevier Ltd. All rights reserved.
Digitalization plays a crucial role in improving the performance of chemical companies. In this context, different modeling, simulation, and optimization techniques such as MILP, discrete-event simulation (DES), and d...
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Digitalization plays a crucial role in improving the performance of chemical companies. In this context, different modeling, simulation, and optimization techniques such as MILP, discrete-event simulation (DES), and data-driven (DD) models are being used. Due to their heterogeneity, these techniques must be executed individually, and holistic optimization is manual and time-consuming. We propose Benders decomposition to combine these techniques into one rigorous optimization procedure. The main idea is that heterogeneous models can simultaneously be optimized as Benders subproblems. We illustrate this concept with the distributed permutation flow shop scheduling problem (DPFSP) and assume that a MILP, DES, and DD model exist for three flow shops. Our approach can compute bounds and report gap information on the optimal makespan for five medium-sized literature instances. The approach is promising because it enables the optimization of heterogeneous models and makes it possible to build optimization capabilities on an existing model and tool landscape in chemical companies.
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