Multi-skilled Resources Research Articles

Energy-efficient resource-constrained multi-project scheduling problem with generalized precedence relations and multi-skilled resources

Energy-efficient resource-constrained multi-project scheduling problem with generalized precedence relations and multi-skilled resources

A chance-constrained programming method with credibility measure for solving the multi-skill multi-mode resource-constrained project scheduling problem

This paper addresses a multi-skilled extension of the multi-mode resource-constrained project scheduling problem (MRCPSP) with preemption of activities and generalized precedence relations under uncertainty. The problem is formulated mathematically as a single objective optimization model to minimize the project makespan. Concerning real-world circumstances, amount of non-renewable resources and availability are considered as interval type-2 fuzzy numbers. Therefore, a type-2 fuzzy chance-constrained programming (TFCP) method is developed to tackle the problem. Four unique characteristics are taken into account in this paper concurrently: (i) multi-skilled resources; (ii) renewable and non-renewable resources; (iii) preemptable activities; and (iv) interval type-2 fuzzy parameters. The existence of these practical contributions causes projects schedule, especially construction projects, to be closer to the real-world conditions. The proposed mathematical model is validated and tested on instances from PSPLIB (j12, j16 and j18) and MMLIB (MM50 and MM100) datasets. In this study, the computational results for the first time demonstrate that the number of preemption points depend heavily on fluctuations of non-renewable resources and number of skills. It is also shown that considering multi-skill renewable resources is an effective factor in project makespan. In J20, there were both a four-unit reduction and increase in the number of preemption points achieving by increasing the average amount of available non-renewable resources by 23 units and decreasing it by 22 units, respectively. Additionally, when seven skills of renewable resources were reduced, the number of preemption points increased by seven units. In this problem instance, project is completed within 28 days considering multi-skill renewable resources, whereas the project makespan is reported 33 days without considering them. Various sensitivity analyses are presented to evaluate the proposed mathematical model and the overall solution approach. Comparative analysis is conducted to compare the schedule in crisp and uncertain conditions. Eventually, the importance of considering multi-skill resources in project scheduling is illustrated compared to not considering them.

Dynamic Optimization of the Multi-Skilled Resource-Constrained Project Scheduling Problem with Uncertainty in Resource Availability

Multi-skilled resources have brought more flexibility to resource scheduling and have been a key factor in the research of resource-constrained project scheduling problems. However, existing studies are mainly limited to deterministic problems and neglect some uncertainties such as resource breakdowns, while resource availability may change over time due to unexpected risks such as the COVID-19 pandemic. Therefore, this paper focuses on the multi-skilled project scheduling problem with uncertainty in resource availability. Different from previous assumptions, multi-skilled resources are allowed a switch in their skills, which we call dynamic skill assignment. For this complex problem, a nested dynamic scheduling algorithm called GA-PR is proposed, which includes three new priority rules to improve the solving efficiency. Moreover, the algorithm’s effectiveness is verified by an example, and the modified Project Scheduling Problem Library (PSPLIB) is used for numerical experimental analysis. Numerical experiments show that when the uncertainty in resource availability is considered, the more skills the resource has and the more resources are supplied, the better the dynamic scheduling method performs; on the other hand, the higher the probability of resource unavailability and the more skills are required, the worse the dynamic scheduling method performs.The results are helpful for improved decision making.

A new algorithm for resource-constrained project scheduling with breadth and depth of skills

This paper addresses a multi-skilled extension of the resource-constrained project scheduling problem (RCPSP). Although a handful of papers dealt with the multi-skilled RCPSP (MSRCPSP), little to no attention is given to the ideal levels of skills for multi-skilled resources. In this paper, skills are measured along two dimensions known as breadth and depth. In a project environment, the breadth of a resource is perceived as the amount of skills an employee masters. The depth of a skill is the efficiency level at which work can be performed by a resource that masters that skill. The MSRCPSP with breadth and depth consists of scheduling activities with skill requirements and assigning multi-skilled resources to those activities. To be able to efficiently solve the MSRCPSP, a genetic algorithm is developed. Using the created activity schedules and resources assignments, the best workforce characteristics are analysed. Key aspects in this analysis are the breadth and depth. The problem-specific procedure combines a new representation, a new crossover and tailor-made local searches. Computational experiments measure the impact of different multi-skilled resources and their efficiency levels on the makespan of the project.

An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the Multi-Mode Multi-Skilled Resource-constrained Project Scheduling Problem

This paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. Hence, in this paper, a mixed-integer formulation called the MMSRCPSP is proposed to minimize the completion time of project. Since the MMSRCPSP is strongly NP-hard, a new genetic algorithm is developed to find optimal or near-optimal solutions in a reasonable computation time. The proposed genetic algorithm (PGA) employs two new strategies to explore the solution space in order to find diverse and high-quality individuals. Furthermore, the PGA uses a hybrid multi-attribute decision making (MADM) approach consisting of the Shannon’s entropy method and the VIKOR method to select the candidate individuals for reproduction. The effectiveness of the PGA is evaluated by conducting numerical experiments on several test instances. The outputs of the proposed algorithm is compared to the results obtained by the classical genetic algorithm, harmony search algorithm, and Neurogenetic algorithm. The results show the superiority of the PGA over the other three methods. To test the efficiency of the PGA in finding optimal solutions, the make-span of small size benchmark problems are compared to the optimal solutions obtained by the GAMS software. The outputs show that the proposed genetic algorithm has obtained optimal solutions for 70% of test problems.

Optimal Work Assignment to Multiskilled Resources in Prefabricated Construction

AbstractMultiskilling allows for dynamic reallocation of workers from one stage of production to another in response to bottleneck configurations and, therefore, has been advocated as a potential s...

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

Abstract In advanced manufacturing of building elements, process integration and utilization of multi-skilled resources enhance the flexibility of production networks against variations in demand and resource availability. This research study aims to incorporate the required cost and time for cross-training multi-skilled resources into resource planning computations. Towards this aim, minimizing the cost of utilizing multi-skilled resources in off-site construction is formulated using integer and probabilistic optimization models. Production data of two prefabrication networks in Brisbane and Melbourne, Australia are used to derive computational results and validate models. The main contribution of this research study is to analyze the costeffectiveness of deploying multi-skilled resources with the aim of improving production flexibility. The modeling methodology and findings are of practical use to off-site manufacturers that experience variations in production demand and resource availability.

Flexible resource management and its effect on project cost and duration

In practice, most projects result in cost overruns and schedule slippage due to poor resource management. This paper presents an approach that aims at reducing project duration and costs by empowering project managers to assess different scenarios. The proposed approach addresses combinatorial modes for tasks, multi-skilled resources, and multiple calendars for resources. A case study reported in the literature is presented to demonstrate the capabilities of this method. As for practical implications, this approach enhances the decision-making process which results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this paper adds empirical evidence to enrich the existing literature, as it highlights relevant issues to model properly the complexity of real-life projects.

IMPROVING PRODUCTIVITY, WORKFLOW MANAGEMENT, AND RESOURCE UTILIZATION IN PRECAST CONSTRUCTION

Construction industry has long dealt with problems such as low productivity, unstable workflow in projects and low utilization rate of resources. Off-site construction, which is a hybrid of mass manufacturing and construction, has significant potential to address aforementioned problems. In order to realize this potential, off-site construction needs to avoid replicating the traditional subcontracting approach and therefore fragmented practice in the construction industry. The current research focuses on restructuring the interaction of resources by using cross-training. Findings show that cross-training resources results in transferring excess capacity from underutilized to over utilized resources in direct or indirect pathways. Such initiatives can significantly improve productivity, workflow management and resource utilization in precast construction that highly specialized resources are not required. The study contributes to the construction management literature by providing insight into dynamics of using multiskilled resources in off-site construction.

Optimal process integration architectures in off-site construction: Theorizing the use of multi-skilled resources

ABSTRACTThe architecture, engineering and construction industry has long dealt with problems such as schedule and budget overruns, quality and safety issues, and low productivity. Off-site construction, which is a hybrid of manufacturing and construction, has significant potential to address industry’s endemic problems. However, off-site construction has been criticized for replicating the traditional subcontracting approach and therefore fragmented practice in the construction industry. The current research focuses on process integration and cross-training of multi-skilled resources as a solution to the aforementioned problem. To identify optimal process integration architectures for off-site construction, production data of three off-site manufacturers were analyzed by using a hybrid of fuzzy theory and the technique for order of preference by similarity to ideal solution. Findings reveal that process integration architectures transferring excess capacity from under-utilized to over-utilized resources in direct or indirect pathways are preferable in terms of satisfying decision criteria such as time and cost of cross-training, skill transferability, compliance with network logic, and safety considerations. The study contributes to the off-site construction literature by providing insight into dynamics of using multi-skilled resources. It also contributes to practice by developing a customizable and user-friendly framework for off-site production managers in order to identify the optimal process integration architecture in their own production scenarios.

Off-site construction optimization: Sequencing multiple job classes with time constraints

Off-site construction is a unique hybrid of manufacturing operations and on-site construction activities. Maximizing the production output has long been the main challenge for off-site manufacturers. Among other responses to this challenge, the use of multi-task shared resources has proved its effectiveness in improving tangible performance measures of production. However, multi-skilled resources often become bottlenecks (overloaded) when producing multiple classes of products and prevent the production network from meeting due dates. This paper analytically models the problem of defining the optimal product sequencing using optimization-based metaheuristics with the aim of minimizing changeover time, which is wasted switching from a product class to another. Production data of two Australian off-site manufacturers are used in the subsequent empirical analysis resulting in advancement of five research propositions. This research contributes to the scheduling theory by expanding the insight into dynamics of resource sharing and job sequencing. The developed models and propositions are of practical value for off-site manufacturers of building elements to maximize their production output.

Optimization of process integration and multi-skilled resource utilization in off-site construction

Traditional approaches in construction project management assign each process to a trade contractor with an individual specialisation, and trades with the greatest work content (bottlenecks) have a significant influence on the progress rate of projects. A system with integrated processes, however, is able to function dynamically in response to variability in product demand and labour resources. This investigation aims to compare and contrast cross-training strategies that are applicable to off-site construction in order to create multi-skilled resources. To this end, the optimal number of additional skills was formulated as a constrained optimization problem. Then, production data from two prefabricated production facilities in Melbourne and Brisbane, Australia were used to construct a total of 1080 simulation experiments. Tangible performance metrics of systems were used to compare process integration strategies and use of multi-skilled resources. Findings show that choosing optimal process integration architecture depends on the level of capacity imbalance and processing time variability. This investigation optimises the decision making on process integration in off-site construction networks.

The impact of fixed and variable costs in a multi-skill project scheduling problem: An empirical study

In this paper, we address a cost-oriented multi-skill project scheduling problem. The project consists on a set of activities such that, for some pairs, a start-to-start time dependency exists. The execution of each activity requires several skills. More than one resource of each skill may be required for processing an activity. A pull of multi-skilled resources is assumed. Costs are associated with resource usage and include fixed and variable costs. The former are incurred simply by using the resources; the latter depend on the final makespan of the project. For this problem, a mathematical programming modeling framework is proposed. The ‘natural’ model contains a non-linear objective function which, nonetheless, can be linearized at the expense of one additional set of continuous variables. The linearized model is enhanced using several sets of additional inequalities. The results of an extensive set of computational tests performed with the final model are reported. One major goal is to evaluate the possibility of using an off-the-shelf solver for tackling the problem. Another relevant goal is to understand the extent to which a cost-oriented objective influences the solutions obtained. Accordingly, we compare the solutions obtained using such objective with the solutions obtained using the traditional makespan minimization objective, often considered in project scheduling problems.

Universal access: the benefits and challenges in bringing integrated HIV care to isolated and conflict affected populations in the Republic of Congo

The Pool region of the Republic of Congo is an isolated, conflict-affected area with under-resourced and poorly functioning health care services. Despite significant AIDS-related mortality and morbidity in this area, and a national level commitment to universal HIV care, HIV has been largely neglected. In 2005 Médecins Sans Frontières decided to introduce HIV care activities. However, in this setting of high basic health care needs, limited medical resources and competing medical priorities, a vertical HIV programme was not suitable. This paper describes the process of integrating HIV care and treatment into basic health services, the clinical outcomes of 222 patients started on antiretroviral treatment (ART), and the benefits to communities and health care systems. Key lessons learned include the use of multi-skilled human resources, the step-wise implementation of HIV activities, the initial engagement of an HIV experienced staff member, the use of simplified and adapted testing, clinical and monitoring protocols and drug regimens, the introduction of more complex monitoring tools to simplify clinical management decisions and intensive staff education regarding the benefits of HIV integration. This project in a rural and remote conflict-affected setting demonstrates that integrated HIV programs can save lives and play a key role in helping to achieve universal access to ART in Africa.

Algorithm for Scheduling with Multiskilled Constrained Resources

Scheduling with constrained resources, particularly skilled labor, is a major challenge for almost all construction projects. In the literature, various techniques have been developed to reduce consequent project delay of constrained resources. Most of these techniques assume single-skilled resources and use heuristic rules to decide which activity will receive the resource first and which ones to delay. To improve existing solutions, this paper introduces some modifications to heuristic resource-scheduling solutions, considering multiskilled resources. The proposed approach stores and utilizes information about the resource(s) that can be substituted when there is a shortage in another one. Using this information, less utilized resources can be combined to substitute the shortages in constrained resources during the shortage period, taking into consideration the loss in work productivity. To automate the proposed algorithm, a macroprogram has been written on a commercial scheduling software. An example application is presented to show the improved results of the proposed approach over existing heuristics. The use of the proposed approach as a better resource management tool within the construction industry is then discussed.