Resource-loaded piping spool fabrication scheduling: material-supply-driven optimization

As an important component of railway intelligent construction, the intelligent management platform for material supply chain is an integrated platform for material management in intelligent construction and an inevitable trend for the continuous development of the full-process information management of railway engineering material supply chain. This paper analyzes and proposes the architecture of the full-process information management system for the material supply chain, and develops an intelligent management platform for the material supply chain. The main functions include the coding and identification system, intelligent supply and demand matching of materials, procurement management, in-transit tracking management, intelligent warehouse management, collaborative dispatching management, and analysis and decision-making management. The research results show that the full-process information management of the railway engineering materials supply chain is oriented towards the main process of project implementation, conducting real-time control over the life cycle of engineering materials, achieving full-process coordinated dynamic management and dispatching command among all participating construction units and supply units, and improving the multi-level and multi-dimensional intelligent analysis capabilities of discrete business data. Support the continuous development of dynamic control throughout the entire process of railway engineering construction materials.

Resource-constrained project scheduling is often a complex task due to the presence of dependencies between activities and the limited availability of renewable resources. The previous two chapters gave an extensive overview of different techniques to construct such a schedule, taking various scheduling objectives into account. This chapter extends this resource-constrained scheduling approach to a more flexible baseline schedule in order to be protected against unexpected events. The Critical Chain/Buffer Management approach assumes the construction of a resource feasible schedule as discussed in the previous chapters, but incorporates a certain degree of flexibility in the activity start times in order to easily monitor schedule deviations and quickly respond by taking corrective actions to keep the whole project on schedule.

Rethinking Chinese supply resilience of critical metals in lithium-ion batteries

Optimization modeling of multi-skilled resources in prefabrication: Theorizing cost analysis of process integration in off-site construction

Calculation of safe float use in risk-analysis-oriented network scheduling

This paper presents a comparative analysis of the use of two methods, Risk Adjusted Discount Rate (RADR) and Monte Carlo Simulation, in evaluating the risks and uncertainties in an oil and gas investment proposal. Basically, RADR method is the same as the usual discounted cash flow. But the discount rate already considers any risk/uncertainty that a project will face. Thus, some percentage, based on trusted publisher, will be added to the discount rate. While using monte carlo simulation, an economic model, with base discount rate, will be evaluated by creating hundreds of possible iterations that continually change the major economic assumption based on historical data such as production, capital expenditure, operating expenditure, oil and gas price. The purpose of this paper is to compare the use of two methods, RADR and Historical-Based Monte Carlo Simulation in evaluating risk/uncertainty in oil and gas investment proposal. There are four real oil and gas projects which will be evaluated: Project 1 (Gas Development Project), Project 2 (Shallow Water Development Project), Project 3 (Offshore Development Project), and Project 4 (EOR Development Project). The Net Present Value (NPV) of each project with those two methods will be evaluated and analyzed. The comparison study shows that NPV Calculation with Historical-Based Monte Carlo Simulation tend to have higher NPV. This is important to maintain the level of project attractiveness. Historical Based Monte Carlo Simulation method also shows the real risks and uncertainties because it is based on the historical data. Besides, this method gives real picture of what the project might face in the future instead of allowing static variables to be introduced into potential dynamic model. However, to make Historical-Based Monte Carlo Simulation robust, complete historical database is needed. While, Risk Adjusted Discount Rate method can simply be used by trusted publication.

The inherent uncertainty of a project causes a difference between the project plan and execution, and efforts are needed to reduce the project loss caused by this difference. Accordingly, in research on the project scheduling problem, research on the deterministic model that finds the optimal combination of fixed elements, such as the Resource Contained Project Scheduling Problem (RCPSP), which considers resource constraints, and the Stochastic RCPSP (SRCPSP), which applies uncertainty probabilistically, are being actively studied. This study deals with the case where there are resources required for project execution that must be procured through external reservation (rental), and there is uncertainty in the availability of the corresponding resources at the time of schedule rewriting due to a change in the preceding schedule. To this end, a mathematical model was developed that probabilistically added uncertainty about the availability of resources that must be procured through external reservations (rentals) at the time of schedule rewriting that occurs in the SRCPSP model with uncertainty in the period, and the distribution of the usage points of specific activities that use resources that must be procured through reservations (rentals) was obtained using simulation techniques. Afterwards, the distribution of the usage points of specific activities was appropriately applied to the initial schedule, and an algorithm for improving the average objective function value was developed. According to the simulation results, under the same conditions, the average objective function value of the simulation using the initial schedule after improvement was improved compared to before improvement, proving the validity of the model presented in this study. This study differs from previous studies in that it additionally considered uncertainty about the availability of external rental resources as a probabilistic factor based on the existing SRCPSP model. In addition, it is significant in that the uncertainty about changes in external resources was applied to the schedule problem at the time of project reevaluation.

On the whole, appropriate scheduling is an extremely paramount factor in project planning. It makes planning items such as allocating resource, determining the number of contractors, maintaining, and delivering order easy. Due to its convoluted nature, scheduling new product development projects is very difficult; managers of these projects deal with complicated factors like uncertain activity duration, information flow and complex relationships. Dependency structure matrix (DSM) has been suggested to cope with these complexities. In the present study, uncertainty in estimating project duration is highlighted; therefore, input information like activity duration, information flow among activities, and the time factors are considered stochastic. Firstly, random numbers are generated based on probability distribution of inputs. Then, project duration and minimised project duration are simulated using DSM method and Monte-Carlo simulation; when simulation turns stable, the mean and variance of durations are obtained. A numerical example is provided and findings are discussed.

The performance of construction workers is a critical determinant of project success, influencing both productivity and quality outcomes. This study investigates how project characteristics, specifically project value and project duration, affect the performance of construction workers in high-rise building projects in Nairobi, Kenya. A quantitative research strategy and a survey research design were adopted, targeting accredited site supervisors overseeing projects valued above KShs 100 million. Using Cochran’s formula and Bartlett’s correction, a sample of 125 projects was selected, yielding 113 valid responses (90.4% response rate). Worker performance was operationalized through two dimensions: labour productivity (efficiency in task delivery) and labour effectiveness (quality of output and skillset). Data were analyzed using descriptive statistics, ANOVA, and Pearson’s correlation. Results revealed that project value significantly influences both productivity and effectiveness, with stronger effects observed on quality-related outcomes. Project duration also showed significant effects, though correlation analysis indicated a negative relationship with productivity and a weak, non-significant link with effectiveness. These findings suggest that larger and longer projects offer better conditions for training, supervision, and mechanization, enhancing worker effectiveness. However, prolonged durations may erode productivity due to fatigue and resource dilution. The study contributes empirical evidence to Kenya’s construction literature and aligns with regional findings from Nigeria and South Africa. Policy implications include the need for mechanization incentives, regulatory standards, and structured training programs. Practically, contractors should embed supervision and mechanization into project planning, while balancing labour and technology to sustain performance across varying project scales.

Uncertainty regarding the duration of activities is a major factor affecting the reliability of construction project schedules. Variability in resources, field conditions, material supplies, and other technical conditions often cause deviations between planned and actual durations. Therefore, this study aims to analyze project duration using the Program Evaluation and Review Technique (PERT) based on three-point estimates, namely optimistic (a), most likely (m), and pessimistic (b), and to visualize the duration range through Triangular Fuzzy Number representation to clarify the variation in estimates for each activity. The methods used in this study include collecting duration estimates (a, m, b) through structured interviews, calculating the Expected Time (te) for each activity, and compiling a work network using the Precedence Diagram Method (PDM) with Microsoft Project to determine the critical path and total project duration. The results of the study show that the total project duration is 170 days, with several activities on the critical path determining the project completion time, while probabilistic analysis results in a 70% chance of project completion within the target duration. The uncertainty of scheduling analysis can be resolved with a triangular fuzzy number visualization approach to extend the optimistic, most likely, and pessimistic time limits for each activity. Based on these results, this study is expected to encourage the application of PERT analysis based on three-point estimates visualized in the form of triangular distributions for each activity as a basis for setting target durations and making more realistic project acceleration decisions.

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.

This paper analyses the means by which major innovations in the operations management (OM) practice develop. It describes the process as having a life cycle pattern that starts with a big idea. The big idea concept promises breakthrough operating results, ultimately providing lower price, better quality, faster delivery, or combinations of these and other attributes that add value for the customer. A successful big idea goes through the phases of provenance, experimentation, penetration, consolidation, and absorption. At that point, it is being incorporated into business operations throughout the industry. If the original idea reaches the end of its useful life or proves unsuccessful during development, it is supplanted. Materials requirements planning (MRP) and supply chain management (SCM) are used to illustrate the general life cycle model. The analysis of these two successful major OM innovations discloses a similar development pattern but different final absorption outcomes. While MRP became an everyday tool for tactical level operations, SCM is an important strategic tool for integrated company management. The similarity of their development patterns, however, supports the general nature of the life cycle model introduced.

Having Digital Product Passport (DPPs) as a disruptive technology to enhance transparency, traceability, and circularity in the garment and clothing industry has become a trend. Combining the data on material composition, supply chain, certifications, and end of life pathways, DPPs can enable brands, regulators and consumers to make prudent sustainability decisions. Nevertheless, that which makes DPPs powerful also creates significant problems of privacy, confidentiality and data security. The secrets of suppliers, the trade secrets in the sourcing business and the relations with customers require intense efforts to ensure that the transparency will not disrupt the trust and the competitiveness. The privacy conserving DPPs discussed in this chapter are applicable to the garment industry.

Significance This builds on current work upgrading the Lobito Corridor railway, which runs through Angola, Zambia and the Democratic Republic of the Congo (DRC) and is designed to better integrate Europe and the United States into critical raw material (CRM) supply chains. Impacts The small number of committed users of the Lobito route thus far may affect its long-term profitability. Stalled government anti-corruption efforts and judicial capture may deter broader foreign investment in Angola. The project will fail to undermine significantly Chinese influence in Angola and across the region.

This paper surveys end-of-life strategies currently used in the electronics and appliances industries and identifies product characteristics that determine feasible strategies. The survey includes consumer products with different end-of-life strategies: an inkjet printer, digital copier, washing machine, vacuum cleaner, television set and single use cameras. Products have distinct end-of-life strategies, composed of end-of-life paths determined by product characteristics. These characteristics not only include product related factors such as product life and functional complexity, but also inverse supply chain factors such as available recycling technologies and demand for recycled materials and parts. This study categorizes product characteristics under following factors: external, material, disassembly, and inverse supply chain. Ongoing work links the identified product characteristics with information available to design engineers during the early stages of product development. Our plan for a web-based design tool will provide a methodology for determining appropriate end-of-life strategies for a broad range of products based on key product characteristics. The survey contributes to the development of recycling technologies such as novel separation, recovery and disassembly techniques.