Emergency Resource Layout Planning Methodology with Multiple Constraints

This study presents a systematic review of the literature on layout planning in healthcare facilities. The review includes 81 articles from journals, conferences, books, and other documents. Articles were classified in two groups according to their main contents including (i) concepts and guidelines and (ii) techniques and tools to assist in layout planning in healthcare facilities. Results indicate that a great variety of concepts and tools have been used to solve layout problems in healthcare. However, healthcare environments such as hospitals can be complex, limiting the ability to obtain optimal layout solutions. Influential factors may include the flows of patients, staff, materials, and information; layout planning and implementation costs; staff and patients safety and well-being; and environmental contamination, among others. The articles reviewed discussed and often proposed solutions covering one or more factors. Results helped us to propose future research directions on the subject.

Detailed Dynamic Layout Planning: An Adaptive Layout Approach

Emergency Resources Management System is an important part of emergency management system. There are many classification systems of emergency resources, which lead to inaccurate description of the structure of emergency resources and negative impacts of the emergency resources sharing and configuration scheduling. In this paper, we compared the strengths and weaknesses of existed emergency resources classification systems, constructed a emergency resources classification system suitable for the whole process of emergency rescue, built an upper emergency resource and domain ontology model rested on SUMO ontology, formally described the concepts in the field of emergency resources and the relationships between these concepts, and satisfied the emergency resources demand of quickly crisis dealing.

A large number of massive repair machines are urgently necessary for a post-disaster rescue. These machines also need to be operated by professionals, and the demands require the participation of different industries in the whole society since they cannot be met via the national emergency resource storage system. Therefore, the support of extensive emergency resources from different industries across the entire society is needed in the rescue process, that is, social emergency resource sharing. To achieve this sharing, an emergency resource scheduling model should have the ability to allocate resources from the whole society. However, traditional emergency scheduling models have not considered the suppliers’ willingness to take part in the scheduling activities and their abilities to supply the resources. To solve the above issues, this paper designs a scheduling model for social emergency resource sharing based on an emergency contribution index (SSERS). The emergency contribution index (ECI) can be used to find the enterprises that not only have the ability to provide efficient emergency resources on time but also have the willingness to participate in emergency rescue. The results show that our model effectively optimizes the basic models to some extent and achieves social emergency resource sharing.

Coordinated trajectory planning of a dual-arm space robot with multiple avoidance constraints

In the global competition, it is vital for any organization to minimize the cost of manufacturing or services and at the same time maintain the quality of process. Facility layout problem (FLP) is an important consideration to reduce the material-handling cost and thus improve the productivity. Optimal design and arrangement of facilities have a significant effect on the overall production cost and manufacturing lead time. In this work, the existing process type layout is studied and its transportation cost is determined. The tool SLP (systematic layout planning) is used to propose a new improved layout. As a part of research work, the existing facility layout of IWAI electronics, which is a speaker manufacturing industry, has been studied. The important parameter considered to improve the layout is the relationship between the departments. Computerized Relationship Layout Planning (CORELAP) was used analytically to investigate the total closeness rating (TCR) for the existing layout. The TCR helps to reposition the departments according to their relationship and closeness ranking. A new layout is then proposed which reduced the material-handling cost by 11.63%.

Due to increasing demands in parcel transportation, enhancing efficiency of parcel sortation operations in logistics distribution centers becomes more and more important. Facility and layout planning in logistics distribution center play an important role in saving time and money, and they have a significant impact on the performance of parcel sortation as well. Thus, this study proposes a simulation optimization approach to solve the sortation conveyor layout problem, especially the shortcuts design of the sortation system that can increase the processing capacity. The proposed simulation optimization approach consists of two parts which are genetic algorithm and simulation. Genetic algorithm helps to find the optimal layout design of sortation system, while simulation is used to evaluate the performance of algorithm results.

An Improved Pigeon-inspired Optimization Algorithm for Solving Dynamic Facility Layout Problem with Uncertain Demand

Sublevel stoping is a commonly used underground mining method in which the profit can be increased by optimizing the layout plan. The complexity of the sublevel stope layout problem is demonstrated by showing it is a special case of the independent set problem, which is NP-hard. A novel approach based on dynamic programming is proposed to solve the sublevel stope layout problem. This approach identifies the recurring subproblems and memoizes their results. Memoizing subproblems reduces the solution time by shifting the computational burden of recalculation to the computer memory. To solve larger problem instances, a greedy heuristic is introduced to further decrease the solution time and limit the memory usage. For smaller problem sizes, the heuristic can be lifted and the approach can be used as an exact method. A large case study is presented to demonstrate the performance of the approach. The results show that that the stope layout plan is generated fast and it captures the valuable regions of the orebody well. A second, smaller case study is presented to benchmark the introduced exact and heuristic approaches. The exact approach outperforms the heuristic approach only by 1% while the solution time is reduced by more than 99%.

Construction site layout is crucial to any project and has a significant impact on the economy, quality, schedule, and other aspects of the project. Especially for infrastructure construction project, the layout planning of site temporary batch plant is one of the key factors for assessing on complexity scales and project performances. The poorer the site batch plant layout planning, the more increasing the project complexity (risks) and the greater the construction cost raised. Generally, the site batch plant layout problem has been solved through the experiences of site management team, using more or less sophisticated numerical model, but mostly the final decision made by the leader who responsible for the site management taking much more consideration of the design conditions and the site spatial constrained. Therefore, the site batch plant layout has always been considered in complying with maximization principles. That is, individual contractor is often required to build his own batch plants for servicing his own project only according to its contract clauses. This construction management strategy has caused to a large waste of resources and much low productivity of the operated plants. The purpose of this paper applies genetic algorithms and computing techniques to develop an optimal model in order to search an optimal solution to the site batch plant layout in the planning stage, and combine with the practical contracting strategies to design feasible construction management plan for enhancement of site management and improvement of concrete quality, as well as minimizing the total construction costs. The GA’s model was developed and applied to specific project located in Taiwan. The usefulness of the model was proven through by the practical operation of the project.KeywordsSite LayoutOptimizationGenetic Algorithms

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

The aim of this paper is to explore the power-law relationship between the degree centrality of countries and their citation-based performance in Management Information Systems research. We analyzed 27,662 articles that received 127,974 citations. The distribution of the citation-based performance follows a power law with exponent of −2.46 ± 0.05. The distribution of the centrality degree of countries follows a power law with exponent of −2.26 ± 0.24. The citation-based performance and degree centrality exhibited a power-law correlation with a scaling exponent of 1.22 ± 0.04. Citations to the articles of a country in MIS tend to increase 21.22 or 2.33 times each time it doubles its degree centrality in the international collaborative network. Policies that encourage a country to increase its degree centrality in a collaboration network can disproportionately increase the impact of its research.

Optimized scheduling of resource-constraints in projects for smart construction

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.