Emergency evacuation in high-rise buildings is a crucial problem. The evacuation strategy of using stairs and evacuation elevators should be optimized. In this paper, simulation-based optimization method is used to optimize the evacuation strategy of using stairs and elevators in high-rise buildings. The stair simulation is based on a cellular automata model, and several typical pedestrians’ walk preferences are considered in this model. In the simulation, evacuation elevators can arrive at the refuge floors, and the scheduling of the elevators is optimized based on the GA algorithm. The simulation-based optimization is designed as a two-level problem: The upper level is a strategy level; the lower level is an operation level. In the study case, the evacuation strategy of a 100-floor ultra-high-rise office building is optimized. We find that if evacuees follow the traditional stair evacuation strategy, the evacuation time is 42.6 min. After optimization, the evacuation time of optimal strategy by using both stairs and elevators is 25.1 min. Compared with the traditional stair evacuation strategy, the efficiency of evacuation is improved by 41.1%. It is also found that the merging behavior in stairwells will decrease the velocity of the pedestrian flow. Stairs are still the main egress, and evacuation elevators are an assistant egress during high-rise building evacuation.

Simulation-based optimization of emergency evacuation strategy in ultra-high-rise buildings

Energy-saving scheduling optimization under up-peak traffic for group elevator system in building

High-rise buildings with a considerable number of elevators represent a major logistic problem concerning saving space and time due to economic reasons. For this reason, complex Elevator Group Control Systems are developed in order to manage the elevators properly. Furthermore, the subject of energy is acquiring more and more industrial relevance every day as far as sustainable development is concerned. In this paper, the first entirely dynamic Fuzzy Logic Elevator Group Control System to dispatch landing calls so as to minimize energy consumption, especially during interfloor traffic, is proposed. The fuzzy logic design described here constitutes not only an innovative solution that outperforms usual dispatchers but also an easy, cheap, feasible and reliable solution, which is possible to be implemented in real industry controllers.

/pdf/dynamic-fuzzy-logic-elevator-group-control-system-for-energy-32iokbkx30.pdf

Dynamic fuzzy logic elevator group control system for energy optimization

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods.

/pdf/immunity-based-optimal-estimation-approach-for-a-new-real-4zifato7eq.pdf

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

This paper addresses the integrated multiobjective production planning and decomposing (P&D) problem for mineral processing. A novel multiobjective 0-1 mixed integer nonlinear programming model is presented for the simultaneous P&D problem (O-model). In order to reduce the computational cost for solving O-model, a rolling horizon-based two-level decomposition approach is proposed to separate O-model into an upper level model (H-model) and a lower level model (L-model). An interactive partition (IP) and multiobjective gradient (MO-G)-based hybrid evolutionary multiobjective (EMO) algorithm named as IG-NSGA-II/IG-SPEA2, which takes the popular NSGA-II/SPEA2 as the basic EA, is proposed to solve both H-model and L-model, where an IP technique is designed to generate the efficient feasible combinational nodes, an ideal solution technique is provided for fathoming the infeasible nodes, an improved multiobjective gradient-based operator is developed to accelerate the evolution process in each selected node, and a cut with all continuous variables is constructed to exclude the previous feasible combination if it is not desired for the decision makers (DMs). The experimental results demonstrate that the presented two-level decomposition strategy can effectively integrate both levels. Moreover, the proposed hybrid method can effectively reduce the combinatorial space so as to concentrate the computing resource on the subspace of most interest, and can generate better feasible solutions than the pure EA in the full-space under computation time limits.

Two-Level Production Plan Decomposition Based on a Hybrid MOEA for Mineral Processing

Group elevator scheduling has received considerable attention due to its importance to transportation efficiency for mid-rise and high-rise buildings. One important trend to improve elevator systems is to collect advance traffic information. Nevertheless, it remains a challenge to develop new scheduling methods which can effectively utilize such information. This paper is to solve the group elevator scheduling problem with advance traffic information. This problem is difficult due to various traffic patterns, complicated car dynamics, and combinatorial explosion of the search space. A two-level formulation is developed with passenger-to-car assignment at the high-level and single car dispatching that is innovatively formulated as passenger-to-trip assignment at the low-level. Detailed car dynamics are embedded in simulation models for performance evaluation. Taking advantage of advance information, a new door action control method is suggested to increase the flexibility of elevators. In view of the hierarchical problem structure, a two-level optimization framework is established. Key problem characteristics are exploited to develop an effective trip-based heuristic for single car dispatching, and a hybrid nested partitions and genetic algorithm method for passenger-to-car assignment which can be extended to solve a generic class of sequential decision problems. Numerical results demonstrate solution quality, computational efficiency, benefit of advance information and the new door action control method, and values of new features in our hybrid method.

Optimization of Group Elevator Scheduling With Advance Information

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Optimization of Group Elevator Scheduling With Advance Information