Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To compensate for a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution. A Nondominated Sorting Genetic Algorithm (NSGA-II) is used to solve the large-sized instances of the modeled problem. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

The implementation of major projects is complicated by the multiplicity of beneficiaries, owners, and all participants in the project as well as the technical overlap between the various engineering, financial and administrative works, while the specific features of the construction activity have a clear influence in shaping the nature of construction projects because the implementation processes were associated with a deep and long-term intervention in the natural environment, where construction is a burden on the environment, both in the construction phase and during the maintenance and liquidation phases: it requires depreciation of a large number of material resources. Through that, this study focused on clarifying the most important concepts of risk management and modern strategies in risk analysis and how to respond to them and monitor projects. The study then presented a questionnaire for the risks facing major projects in Egypt. Through analyzing the results of the questionnaire, a qualitative risk analysis was conducted that can be used to prioritize response to risks, in addition to conducting a Monte Carlo simulation based on theoretical foundations and providing a new process for prioritizing project risks related to sustainability, where the (Primavera Risk Analysis) program was used to clarify the impact of risks on project time and cost. All analyses are based on the theoretical background regarding risk, risk management process, and project life cycle approach in the sustainable construction sector. with the help of this study, it is possible to address ways of mitigating the harmful effects on the environment through the implementation of sustainable management in the planning of future projects and better management of current projects.

Delay is one of the most common causes of construction projects failure in Iran. The larger sizes of the projects, the more risks and costs of delays. In this paper, the causes of delays in EPC contracts of nonindustrial buildings were excavated from the related previous research and several interviews with experts of this subject, and adjusted by the brainstorming technique, Delphi and reconciling the nature of these projects. Then, a questionnaire was distributed among 52 experts working in the South Pars project, and the data were analyzed by descriptive and factor analysis methods. Descriptive analysis revealed that “Inflation and escalation of material prices and human resources salaries”, “Unrealistic contract duration and requirements imposed” and “Political situation” were the most significant delay factors. Meanwhile, factor analysis indicates that “Improper construction methods”, “Shortage of experienced and skilled labor” and “Long acceptance process (shop drawings, permits, tests and samples)” were the most important causes of delay.

In reality, the machines may interrupt because of the nature of deterioration of the machines. Thus, it is inevitable to perform maintenance alongside production planning. The preventive maintenance is a schedule of strategic operations that are performed prior to the failure occurring, to retain the system operating at the preferred level of consistency. Thus, preventive maintenance plays a significant role in flow shop scheduling models. With its practical significance, this study addresses a practical three-machine n jobs flow shop-scheduling problem (FSSP) in which machine specific preventive maintenance, where each machine is given with a maintenance schedule is considered. In addition, a practical ordered precedence constraint in which some set of jobs has to process in the specified order irrespective of their processing times is also considered. The problem’s goal is to establish the optimal job sequence and preventive maintenance such that the overall cost of tardiness and preventive maintenance is as minimum as possible. An efficient heuristic approach is designed to tackle the present model, resulting in total cost savings. A comparative analysis is not conducted due to absence of studies on the current problem in the literature. However, Computational experiments are carried out on some test instances and results are reported. The reported results may be useful for future studies.

Disruptive innovation has attracted much interest, discussion and argument in academia and industry since it was defined. Based on a review of the related research publications, this paper aims to clarify the concept of disruptive innovation on controversies, summarize ex-ante studies on project management for disruptive product innovation, and propose potential research areas of the disruptive product innovation. The ex-ante studies are classified into three aspects: timing and conditions of disruptive innovation, factors for the implementation of disruptive innovation, forecasting and implementation of disruptive innovation. After a review of the existing methods of disruptive innovation, the potential research of disruptive innovation is presented.

The purpose of this paper is to explore the influence of ICT on construction supply chain management (CSCM). Furthermore, this research proposes a model for ICT that can be used to test and improve CSCM within the Nigerian construction industry. The relationship between ICT and CSCM was highlighted in an integrated model. Structural equation modelling (SEM) was used to validate the hypothesised relationship by evaluating the responses of practitioners working in construction project management related firms using a sample of 214 respondents. The findings of this study show a link between ICT and CSCM on five (5) of the constructs on targeted construction firms engaged in supply chain (SC)-related construction activities in Port-Harcourt, Rivers State, Nigeria. This research is supported by the Technology Organisation and Environment (TOE) Model as a key theory of technology adoption. With an average R2 value, the SEM model is 76%, which is commendable (high). The current study offers an empirical and theoretical explanation of various aspects of ICT and CSCM, particularly in the Nigerian construction industry. As a result, this study adds to the body of knowledge by providing crucial insight into the influence of ICT on CSCM throughout the entire construction supply chain. The knowledge gained will aid industry stakeholders and the government in developing policies that will increase ICT adoption in current practice.

Organizing function is the significant component of management guaranteeing the success of construction works. Many studies have been emphasizing on the topic of critical success factors (CSFs) within construction works, yet the results have rarely emphasized examining organizing behaviors within public construction work management; these less researched topics were the aims of this study. To fulfil this research aim, a regression analysis design was employed. Data were collected using questionnaires conducted from 139 professionals involved in public construction works management in Vietnam. The structural equation modelling (SEM) technique with partial least-squares estimation (PLS) was utilized to analyze the data. The results revealed 6 behavioral dimensions (i.e., structure organizing (OR1), authorization organizing (OR2), coordination organizing (OR4), human resource organizing (OR3), job organizing (OR5), responsibility organizing (OR6) to assess organizing function in terms of public construction work management. The study also reveals that structure organizing (OR1), authorization organizing (OR2), coordination organizing (OR4) have significant effects on management effectiveness (ME). In addition, coordination organizing (OR4) acts as the mediator of Human resource organizing (OR3), job organizing (OR5); while responsibility organizing (OR6) indicates an indirect influence through the mediator of OR5. The success of this approach is expected to reinforce the contribution of organizing function and suggest a useful tool for supporting the professionals in enhancing public construction work management.

This study contributes to the hybrid flow shop due to a lack of consideration of characteristics existing in real-world problems. Prior studies are neglecting identical machines, explicit and sequence-dependent setup times, and machine blocking. We propose a hybrid genetic algorithm to solve the problem. Furthermore, we also propose a mixed-integer linear programming formulation. We note a predominance of the mathematical model for small instances, with five jobs and three machines because of how fast there is convergence. The objective function adopted is to minimize the makespan, and relative deviation is used as a performance criterion. Our proposal incorporates two metaheuristics in this process: a genetic algorithm to generate sequences (the flow shop subproblem) and a GRASP to allocate the jobs in the machines (the parallel machines subproblem). The extensive computational experience carried out shows that the proposed hybrid genetic algorithm is a promising procedure to solve large-sized instances.

This study aims to identify and evaluate the critical success factors (CSFs) for international development projects (IDPs) from the perspective of key IDP stakeholders in Afghanistan. The study adopts a quantitative cross-sectional survey research design. Thirty-one success factors were identified and shortlisted through literature reviews and validated by experts and IDP management practitioners. The study's target population is the IPD senior management, IDP team members, and the general public. Amongst 500 questionnaires distributed, a total of 217 were returned and considered for analysis. The result of Exploratory Factor Analysis (EFA) revealed five key CSFs, namely: project cycle management, effective recruitment, continuous learning and adapting, project management method, and clear project goals and objectives. Besides, one-way ANOVA results revealed no statistically significant differences in the ranking of CSFs by the three groups of respondents. However, the post hoc test result indicated that the CSF 'continuous learning and adapting' was relatively rated lower by the general public. The findings of the study would assist the international community, their implementing partners, and IDP management practitioners in better management and successful implementation of IDPs in developing countries. It will also contribute to the CSFs theories and IDPM body of knowledge. The research is the first of its kind to examine the CSFs for IDPs in Afghanistan.

Job precedence can often be seen in various manufacturing process scenarios. For instance, in the context of flow shop scheduling, certain jobs must be processed before a specific job may be executed. Formally, this scenario is known as precedence constraint, which influences the optimal job sequence. Because of this practical significance, in this study, a two-machine flow shop scheduling problem in which transportation times, breakdown time, and weighted jobs are considered. In addition to that, an ordered precedence constraint is considered that ensures a successor job cannot start on any machine before its predecessor job has been done on all machines. This is the first study that deals with flow shop scheduling problems with transportation times, breakdown time, job weights, and precedence constraints altogether, to the best of the author’s knowledge. To solve this problem, a simple and efficient solution methodology is developed that assures optimal or near-optimal solutions effectively. The developed algorithm is tested on various test instances and results are reported, which will be useful for future comparative studies.