Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem

PurposeConstruction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).Design/methodology/approachA direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.FindingsComputational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.Research limitations/implicationsIt is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.Originality/valueAn integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

This paper presents a hybrid genetic algorithm for the simple assembly line problem, SALBP-1. The chromosome representation of the problem is based on random keys. The assignment of the operations to the workstations is based on a heuristic priority rule in which the priorities of the operations are defined by the chromosomes. A local search is used to improve the solution. The approach is tested on a set of problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the algorithm.

Reinforcement Learning for Resource Constrained Project Scheduling Problem with Activity Iterations and Crashing

Intelligent and connected vehicles rely on edge computing to offload their perception and planning tasks, so the scheduling of communication and computing resources is critical to the driving safety and efficiency. However, the imbalanced distribution of road traffic and offloading demands impedes the quality of vehicular edge computing. In this paper, we propose a multi-timescale load balancing approach to improve the service quality and resource utility of vehicular edge computing. Specifically, vehicle mobility optimization is leveraged to perform long-term load balancing, and resource allocation is used to achieve real-time load balancing. As the multi-timescale optimization is confronted with the curse of dimensionality, multi-agent deep reinforcement learning is utilized to optimized vehicle mobility and resource allocation in parallel. Experimental results show that the proposed method can significantly reduce the service delay of vehicular edge computing.

Project scheduling consists of defining and assigning resources to a set of time-scattered activities. Several economic, business and engineering disciplines are considered for practical application: project management, operations research, software development, systems engineering. In Resource-Constrained Project Scheduling (RCPS), task allocation is determined by resource distribution based on a set of constraints. Poor constraint evaluation and its impact on the project schedule, may lead to project baseline disruptions, therefore to cost failover and company loss. The aim of the current research is to provide a solution for the standard Resource Constrained Project Scheduling Problem (RCPSP) through a hybrid heuristic algorithm based on priority rule scheduling (priority rules, schedule generation schemes). We present a conceptual framework for a software engineering context, where activities of concurrent projects are allocated based on employees' skills to optimize a regular objective of estimated activity duration.

Due to the own business characteristics and customer requirements, the risks such as requirement change, unexpected task or resource turnover often arise in small and medium-sized R&D organizations, therefore it is essential for the organizations to set up an organization-oriented organizational process model. The principles and approaches of the typical combined process utilization within the process model are elaborated via the examples of organizational requirement management, project management and resource equilibrium. Resource-Constrained Project Scheduling Problem(RCPSP) is a key sub-problem in partner selection of multi-projects Scheduling. Parallel Scheduling Schema(PSS) and some priority rules are designed to solve the NP-complete problem of the organization's whole projects scheduling. By using the PSS and those rules, an improved Critial Chain Method(CCM) is used to monitor and adjust organizational project plans and resources, parallel scheduling and dynamic adjusting the project plans, preventing the resource conflict condition happened, providing the technical support for the process model used on the software enterprise information platform.

Effective project management implies the use of advanced planning and scheduling methods that allow to determine feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for construction projects where spatial factor plays a critically important role. Previous attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities and motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In this paper we start with the classical RCPSP statement and then present mathematically strong formalisation of the extended generalised problem, taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalised RCPSP problem an effective scheduling method is proposed. The method tends to minimise the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilisation limits, avoiding workspace congestions and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too.

Effective project management implies the use of advanced planning and scheduling methods that allow to determine the feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for the construction projects where spatial factor plays critically important role. Previously taken attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities, motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In the paper we start with the classical RCPSP statement and then present mathematically strong formalization of the extended generalized problem taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalized RCPSP problem an effective scheduling method is proposed. The method tends to minimize the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilization limits, avoiding workspace congestion and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. In order to evaluate the method, benchmark sets are proposed and investigated. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too.

As an archipelagic country, Indonesia has high needs for its aviation development. About 270 million people are predicted to fly from and within Indonesia in 2034, which is three times larger than current market conditions. Due to the complexity of aircraft development projects, management of a well-designed Indonesian aircraft design project needs to be implemented. This research aims to analyze the scheduling of aircraft wing structure design project made in Indonesia. Critical Path Method (CPM) and Resource-Constrained Project Scheduling (RCPS) are used to get better scheduling with a shorter duration to complete the project. Results from resource allocation using RCPS can yield a shorter project duration (730 working days) compared to the company’s normal resource allocation (950 working days).

The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

This paper presents a heuristic solution procedure for a very general resource---constrained project scheduling problem. Here, multiple execution modes are available for the individual activities of the project. In addition, minimum as well as maximum time lags between different activities may be given. The objective is to determine a mode and a start time for each activity such that the temporal and resource constraints are met and the project duration is minimized. Project scheduling problems of this type occur e.g. in process industries. The heuristic is a multi---pass priority---rule method with backplanning which is based on an integration approach and embedded in random sampling. Its performance is evaluated within an experimental performance analysis for problem instances of real---life size with 100 activities and up to 5 modes per activity.

In Hong Kong, 6-day construction cycle always being used for constructing one typical floor of concrete building superstructure in a week. Work-zones are defined in the working platform for better allocation of limited resources. Once the work-zones are defined, the resources (labour, plant, materials) will be allocated to a specific zone to deliver the works on a particular day of the 6-day construction cycle. In practice, the definition of work-zone is dependent on the experience of project manager. Yet, there is no past research studied the evaluation of work-zone definition schemes. As such, in this research study, three heuristic rules are proposed for choosing better scheme of work-zone. The heuristic rules are based on (i) lengths of work-zone dividing line, (ii) fluctuations of daily duration, and (iii) utilisation rates of resource. To illustrate the steps of evaluating the scheme, an illustrative example is given. To illustrate the method application of the rules, a practical case study of constructing a 40-storey housing is given. The proposed rules were verified based on sensitivity analysis and expert validation. Conclusions are drawn by expressing the contributions and limitations of the proposed rules, followed by suggesting future research works.

A new heuristic method is presented for the resolution of multiresource constrained conflicts in project scheduling. In attempting to find a minimal makespan solution, the algorithm employs a simple procedure to generate a feasible solution with no backtracking. A postanalysis phase then applies a hill-climbing search. The solution method is different from existing heuristic methods in that it repairs resource conflicts rather than constructs detailed schedules by dispatching activities. Resource-violating sets of activities are identified which must be prevented from concurrent execution because this would violate resource constraints. Repairs are made by imposing an arc to sequence two activities in such a resource violating set. Computational results are compared with those of existing heuristics for the minimal makespan problem.

Rankings of graduate and professional programs have become commonplace since Cartter's study for the American Council on Education was published in 1966. Much of the research up to the present has focused on discovering proxy variables, such as departmental size, which correspond to departmental ratings, as well as observing how the ratings of departments change over time. This study examines the relationship between the peer ratings reported by Roose and Andersen in 1970 and resource allocation patterns, represented by average salary figures, among fifty major American universities. It also shows the relationship of these ratings and other resource allocation measures within one of the fifty institutions. Results indicate that among the fifty universities, average salaries are higher at the highly rated institutions than at the lower rated ones, with senior professors being the chief beneficiaries of the higher wage scales. Similarly, within one institution the senior professors of higher ranked departments are better paid, compared to their junior colleagues, than those of lower rated departments. The higher rated departments share commonalities as to proportion of senior professors and teaching load assignments as well. Both across and within universities, departments found to be prestigious by Roose and Andersen do tend to exhibit common characteristics concerning resource allocation procedures.

In order to fully utilize the Grid resources, implementing a good scheduling algorithm is really important. Currently, some of the well-known researches and enterprise schedulers have applied Priority Rule (PR) schedulers to manage the Grid jobs because they are simple and easy to implement. To date, there is no strong performance justification or proof given on why these specific PR algorithms are preferred compared to other PR algorithms. For example, five PR algorithms; First Come First Serve (FCFS), Longest Job First (LJF), Shortest Job First (SJF), Earliest Deadline First (EDF) and Minimum Time To Deadline (MTTD) have been proposed. However, none of these algorithms perform well in every metrics of performance. An attempt to improve their performance has been made using Combinatorial Rule (CR) which is the combination of more than one PR. Unfortunately, the current implementation of CR only focuses on two performance metrics which are flowtime and makespan, while sacrificing the others. In order to tackle the aforementioned problem, this paper introduces Minimum Time To Deadline to Fastest Resource - Shortest Job to Fastest Resource (MTTDFR-SJFR), a new CR scheduler that performs better than other CR algorithms tested within the scope. To achieve high performance, a combination of PR with Fastest Resource (FR) resource selection scheme that further improve the performance is proposed. Graphical results obtained from experimental simulation showed the superiority of the proposed CR algorithm in term of overall performance, compared to other CR as well as the original PR.