Search results

Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles.

The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method.

The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models.

The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase.

Beginning in the early 1970s Trinidad and Tobago adopted a policy of putting all of its junior secondary schools “on shift”. The method allows for two schools conducted in the same facility at different times of the day, and has the advantage of accessing a large number of students to secondary school. The method, however, has generated a number of problems and, as a result, has been earmarked for alteration. Current plans in Trinidad and Tobago include dismantling the shift system through construction of new, single shift, schools in new locations. It is argued that proposals to build new facilities in new locations as a means of resolving problems associated with the shift system may be more effectively accomplished through application of the method of location‐allocation modelling.

Interactive‐graphic systems and operations research methodologies are increasingly being combined to produce efficient, versatile and powerful tools that enhance the decision‐making process. The possible contribution of such tools to the planning of distribution systems for industrial firms and freight carrier service networks and operations is examined and compared.

Interactive‐graphic systems and operations research methodologies are increasingly being combined to produce efficient, versatile and powerful tools that enhance the decision‐making process. The possible contribution of such tools to the planning of distribution systems for industrial firms and freight carrier service networks and operations is examined and compared. The similarities and differences between the two systems are derived and an examination is undertaken to determine whether a unique planning system may adequately respond to the requirements of the two problems.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This paper aims to study a dynamic post-disaster emergency planning (PDEP) problem in an integrated network through the investigation of the selection of shelters, medical centers and distribution centers (DCs), and the allocation of evacuees and injured people. The resource and people assignment in multiple periods are considered.

A mathematical formulation is provided for the PDEP problem. The authors decompose the model into two sub-models as follows: the primary model is an integer programming model and the subproblem is a nonlinear programming model with continuous variables. The simulated annealing is used to solve the primary problem, and particle swarm optimization (PSO) mixed with beetle antennae search (BAS) is used to solve the subproblem.

The paper finds that BAS can increase the stability of PSO and keep the advantages of PSO’s rapid convergence. By implementing these algorithms on emergency planning after the Wenchuan earthquake that happened in China in 2008, this paper finds that the priority of different levels of injured people is influenced by several factors. Even within the same disaster, the priority of different levels of injured can be inconsistent because of the differences in resource levels.

The authors integrate the shelters, medical centers and DCs as a system, and simultaneously, consider evacuees and injured people and different resource assignments. The authors divide the injured people into three levels and use survival rate function to simulate the survival conditions of different people. The authors provide an improved PSO algorithm to solve the problem.

Despite some significant success stories, there has been only a slow diffusion of awareness of the benefits and application of site evaluation techniques in general, and the application of geographical information systems (GISs) in particular. Four types of factors that have hindered the development of the use of GISs as spatial decision support systems (SDSSs) are outlined: the balance between costs and benefits; the historical development of location and locational marketing strategy within retail businesses; establishment of the role of GISs as SDSSs; and an appreciation of the competitive advantage to be gained by appropriate use of GISs. Argues the latest generation of GISs boasts a number of features that are likely to make them more user friendly and easier to integrate into the total information systems environment within an organization. However, effective use of GISs as SDSSs also depends on full integration of GISs into the armoury of decision‐support tools used by retail managers.

This study focuses on a unique retail outlet location decision‐making problem found in business format franchising industries. The problem is derived from the latent conflict in the relationship between franchisor and franchisees. An empirical study was carried out to compare benefits of two different outlet allocation decision modes: centralised planning mode (locations allocated by the franchisor) and decentralised planning mode (franchisees given autonomy to select the location of their own outlets). Research findings revealed that the decentralised location decision mode is more beneficial to the franchise system with a lower level of customer loyalty.