Optimization and Engineering最新文献

Countries around the world are gradually implementing the transition to a circular economy in waste management. This effort should be initiated already at the waste producers. It is necessary to plan and monitor waste management in as much detail as possible, i.e. at the level of micro-regions. At present, only indicators at the national level are analysed, as more detailed data at the micro-regional level are often not available or are burdened with significant errors and inconsistencies. The calculation of waste management indicators for micro-regions will allow to identify the potential for increasing material or energy recovery and to plan the necessary infrastructure directly to these locations instead of blanket and often ineffective legislative actions. This paper presents an approach for determining the producer-treatment linkage, i.e., provides information about each produced waste, where it was treated, and in what way. Such information is often not available based on historical waste management data as there are repeated waste transfers and often aggregated within a micro-region. The network flow approach is based on an iterative procedure combining a simulation with multi-criteria optimization. The chosen criteria replicate expert estimates in investigated issue such as minimum flow splitting, and minimum transfer micro-regions. A data reconciliation is performed where the deviation from all simulations is minimized, given that the capacity constraints of nodes and arcs resulting from the database must be satisfied. The approach is tested on a generated sample task to evaluate the precision and time complexity of the developed tool. Finally, the presented approach is applied to address a case study in the Czech Republic, within which it is possible to identify treatment location and methods for waste from individual regions.

The coupling problem between the structural field and temperature field is widely encountered in engineering applications and holds significant research importance. In the context of thermo-mechanical coupling topology optimization, the thermal stress resulting from the temperature field can cause structural deformation, consequently impacting the structural performance. Therefore, it is crucial to conduct rational optimization designs to ensure favorable mechanical behavior and heat dissipation. This study utilizes thermo-mechanical coupling theory to perform multi-objective topology optimization, aiming to minimize compliance and heat dissipation weakness concurrently, thereby obtaining a more comprehensive design scheme with enhanced overall performance. Initially, a topological optimization model for the coupled thermo-mechanical problem is established. Subsequently, the objective functions of structural compliance and heat dissipation weakness are normalized, and their sensitivities are derived. Next, a multi-load case and multi-objective optimization algorithm based on Floating Projection Topological Optimization (FPTO) is proposed to minimize both structural compliance and heat dissipation weakness. By comparing the topological configuration and objective function values obtained using the Solid Isotropic Material with Penalization (SIMP) method, it is evident that the FPTO method achieves clear and smooth boundaries in the topological configuration, while yielding smaller objective function values. Additionally, under appropriate trade-off factors, the FPTO method achieves a more balanced topological structure and optimizes material distribution without increasing the structural volume, thus enabling lightweight structures, providing novel ideas and methods for engineering applications.

A new bounds tightening algorithm for globally solving AC optimal power flow (ACOPF) problems is presented. Practical ACOPF instances are too large to be solved by conventional global optimization algorithms based on extensive search-space partitioning. However, tailored optimization-based bounds tightening (OBBT) algorithms using advanced relaxation techniques have been shown to achieve tight optimality gaps for many test cases with no partitioning at all. Unfortunately, OBBT is still costly because it requires solving two convex subproblems per decision variable in each iteration. We present a new OBBT algorithm, using a new SOCP based relaxation, that achieves tight optimality gaps while only solving subproblems for a small subset of variables. For PGLIB benchmarks up to 300 buses, the algorithm achieves the best gap on more test problems and is significantly faster on average than two existing OBBT algorithms chosen for comparison.

We address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of applications as for instance in supply chain optimization problems.