Mutation particle swarm optimization algorithm for solving the optimal operation model of thermal power plants

Consumer phase identification in low-voltage distribution network considering vacant users

Determining [formula omitted]- and [formula omitted]-robustness of digraphs using mixed integer linear programming

By modifying the objective function and adding new constraints to a Mixed Integer Programming (MIP) method proposed by Park and Reveliotis, this chapter presents a Revised MIP (RMIP) method to directly solve siphons, called smart siphons, with the minimal cardinality as well as the minimal number of resource places. Accordingly, a proper Control Place (CP) is added for each smart siphon in order to achieve the desired control. Both efficiency and practicality of this method are proved through a theoretical proof and several examples.

Convex Model for Global Optimization of Water Distribution System

It has been shown that mixed integer programming methods can effectively support minimal model, stable model and well-founded model semantics for ground deductive databases. Recently, a novel approach called partial instantiation has been developed which, when integrated with mixed integer programming methods, can handle non-ground logic programs. The goal of this paper is to explore how this integrated framework based on partial instantiation can be optimized. In particular, we develop an incremental algorithm that minimizes repetitive computations. We also develop optimization techniques to further enhance the efficiency of our incremental algorithm. Experimental results indicate that our algorithm and optimization techniques can bring about very significant improvement in run-time performance.

In this paper, we formulate the problem of finding an optimal generation dispatch and transmission topology to meet a specific inflexible load as a mixed integer program. Our model is a mixed-integer linear program because it employs binary variables to represent the state of the equipment and linear relationships to describe the physical system. We find that on the standard 118-bus IEEE test case a savings of 25% in system dispatch cost can be achieved.

ABSTRACTRailway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions.Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first

Optimal controlled-islanding with AC power flow constraints: A benders-decomposition approach

The authors propose an algorithm for solving reactive power planning problems. The optimization approach is based on a recursive mixed-integer programming technique using an approximation method. A fundamental feature of this algorithm is that the number of capacitor or reactor units can be treated as a discrete variable in solving large-scale VAr (volt-ampere reactive) planning problems. Numerical results have verified the validity and efficiency of the algorithm. >

A current-based model of the static synchronous series compensator (SSSC) for Newton–Raphson power flow

The frequency may change significantly as a result of the erratic behavior of power system components, such as generators and transmission lines, reflecting the generation and demand balance in power systems (Ahmadi and Ghasemi in IEEE Trans. Power Syst. 29:1536–1545, Jul. 2014). This can result in large load shedding and significant financial loss. For instance, a storm-related transmission line failure in 2009 (Conti in Eng. Technol. 5:46–47, 2010) resulted in a significant frequency reduction and 40% load shedding in the Brazil system. As a result, when planning the schedule for power systems, frequency security under contingencies must be taken into account.

A resilient power system should be able to withstand extreme occurrences and recover quickly (Berkeley, and Wallace. A Framework for Establishing Critical Infrastructure Resilience Goals: Final Goals and Recommendations [Online].; Trakas and Hatziargyriou in IEEE Trans. Power Syst. 33:2260–2271, 2018). One of the main goals of smart grid development has been to increase grid resilience to extreme weather events (Dan and Wang in IEEE Power Energy Mag. 13:26–34, 2015).

Traditionally, duct sizing in ventilation systems is based on balancing pressure losses across all branches, with fan selection performed subsequently. However, this sequential approach is inadequate for systems with distributed fans in the central duct network, where pressure losses can vary significantly. Consequently, when designing the system topology, fan placement and duct sizing must be considered together. Recent research has demonstrated that discrete optimisation methods can account for multiple load cases and produce ventilation layouts that are both cost- and energy-efficient. However, existing approaches usually concentrate on component placement and assume that duct sizing has already been finalised. While this is sufficient for later design stages, it is unsuitable for the early stages of planning, when numerous system configurations must be evaluated quickly. In this work, we present a novel methodology that simultaneously optimises duct sizing, fan placement, and volume flow controller configuration to minimise life-cycle costs. To achieve this, we exploit the structure of the problem and formulate a mixed-integer linear program (MILP), which, unlike existing non-linear models, significantly reduces computation time while introducing only minor approximation errors. The resulting model enables fast and robust early-stage planning, providing optimal solutions in a matter of seconds to minutes, as demonstrated by a case study. The methodology is demonstrated on a case study, yielding an optimal configuration with distributed fans in the central fan station and achieving a 5 reduction in life-cycle costs compared to conventional central designs. The MILP formulation achieves these results within seconds, with linearisation errors in electrical power consumption below 1.4%, confirming the approach’s accuracy and suitability for early-stage planning.

The surgery department is required to improve its performance by efficient usage of its available resources. Surgery department interdependencies with other hospital functions and departments are numerous and complex, which causes some restrictions on surgery ward performance and consequently hospital performance. Operating room (OR) schedule determines day to day operations that take a place at the surgery department. Creating such a schedule is a complex task, due to a multitude of constraints, preferences and objectives that planners need to take into account. Meanwhile, the schedule has major effects on the performance of surgery department and the hospital. This paper provides a review of recent operational research on operating room planning and scheduling with focus on multi-objective simulation optimization with use meta-heuristic method: in operating room, as a practical suggestion for hospitals. Recent researches were collected based on some keywords including simulation optimization, and operating room by google scholar, Scopus, and Pubmed. The findings showed that, there are not much practical suggestion in this case and still there is a gap and needs to be discovered.

Civil structures are often overdesigned to meet safety and functionality criteria under rare, strong events. Adaptive structures, however, can modify their response through sensing and actuation to satisfy design criteria more efficiently with better material utilization, which results in lower resource consumption and associated environmental impacts. Adaptation is performed through actuators integrated into the structural layout. Several methods exist for optimal actuator placement to control displacements and internal force flow. In discrete systems like trusses and frames, actuator placement is typically a binary assignment. Most existing methods use bilevel and heuristic formulations, leading to suboptimal solutions without proving global optimality. This paper introduces a Mixed Integer Programming (MIP) method that produces global optimum solutions by optimizing both actuator placement and commands. Two objective functions are used: minimizing the number of actuators and minimizing control energy. The optimization considers structural and serviceability limits and control feasibility. An extensive benchmark compares the new formulation’s global optima with solutions from greedy, stochastic, and heuristic methods. Results show that the new method consistently produces higher-quality solutions than all other methods benchmarked in this study.