A Permutation-Based Bees Algorithm for Solving Resource-Constrained Project Scheduling Problem

Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling.Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan.This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one.Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP.Bee algorithms are among most recently introduced meta-heuristics.This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP.To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library.Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem.

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

00/01553 Thermodynamics of TiO x in blast furnace-type slags

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.

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.

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

PurposeConstruction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.Design/methodology/approachDue to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.FindingsAccording to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.Practical implicationsICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.Originality/valueIn this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

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.

Many scheduling problems in project management, manufacturing, and elsewhere require the generation of activity networks to test proposed solution methods. Single-network generators have been used for the resource-constrained project scheduling problem (RCPSP). Since the first single-network generator was proposed in 1993, several advances have been reported in the literature. However, these generators create only one network or project at a time; they cannot generate multi-project problems to desired specifications. This paper presents the first multi-network problem generator. It is especially useful for investigating the resource-constrained multi-project scheduling problem (RCMPSP), where a controlled set of multi-project test problems is crucial for analyzing the performance of solution methods. In addition to the single-project characteristics handled by existing network generators—such as activity duration, resource types and usage, and network size, shape, and complexity—the proposed generator produces multi-project portfolios with controlled resource distributions and amounts of resource contention. To enable the generation of projects with desired levels of network complexity, we also develop several theoretical insights on the effects of network topology on the probability of successful network generation. Finally, we generate 12,320 test problems for a full-factorial experiment and use analysis of means to conclude that the generator produces “near-strongly random” problems. Fully “strongly random” problems require much greater computational expense.

The production of manufactured parts that every day is taken place into enterprices that produce parts and services, has it purpose in the pursuit of satisfying specific needs of customers, the set of resources that were transformed and finally made a finished product, have greater value when they have been combined than when they are separated, without interrelation and unprocessed, therefore the customer pays for the good or service that is given to them. So, the production system as a source of wealth is in constant search of value added that customer is willing to pay, this value allows the company to make new resources to generate the most possible quantity of products ready for sale, good quality to the minimum price that allows the management of production is always focused to greater efficiency of the production system, it means, getting optimal results compared with costs. Strategic planning sets the goals to be achieved through the use of strategies and policies; all of this is long term, giving origin to the tactical planning, including the added plan and finally; planned short-term objectives of the operational areas. The result would be reflected in operational production plans, resulting of an advanced production planning.

This paper describes a project conducted in a company that provides chemical water treatment services for industrial cooling systems across Mexico. The project addresses the challenge of rising transportation costs and inefficient production planning in a dynamic service environment. Key stakeholders, including the company’s General Director and Plant and Logistics Manager, identified transportation expenses as the fastest-growing operational cost, with increases more than double the inflation rate in Mexico over the past five years. Compounding this issue, the company currently operates with reactive production planning, relying on monthly client estimates and lacking effective control over shipment management, resulting in demand imbalances. The project utilizes a transdisciplinary approach, combining strategic planning and statistical analysis to determine the priorities of the company and establish a framework for demand planning and transportation management. The proposed approach is founded in methodologies such as Hoshin Kanri, project management, time series analysis and control charts. The main results of the project include: (1) a cost-reduction strategy for transportation, achieving a 20-25% decrease in associated expenses; and (2) a set of rules for proactive production planning and shipment management, which enable earlier procurement and help identify demand fluctuations. By integrating quantitative analysis with business management practices, this work proposes a sustainable solution to logistical challenges, aligning production planning with demand patterns and reducing costs. This research aligns with the transdisciplinary engineering methodology by bridging statistical analysis with operational strategies to create value across business and logistics processes.

This paper introduces the results whether humans are able to develop project plans with a high quality for the well-established Multi-criteria Resource-Constrained Project Scheduling Problem (RCPSP). To analyse this, an empirical study was conducted in which activities had to be serialized or parallelized in the plan, process steps had to be inserted or removed and durations as well as resource requirements had to be modified dynamically during planning. In contrast to this human based planning, a specific multicriteria evolutionary metaheuristic is presented that identifies human compatible plans to relatively large project management problems within a reasonable period of time. To evaluate the level of human competitiveness, a metric for measuring plan quality and the results of the empirical study are presented. The results derived from data of 100 participants and the metaheuristic show that only very few people were able to identify optimal solutions. Furthermore, humans are focusing on one target criteria when solving conflicting planning objectives.

The purpose of this research was to plan an approach to a project framework that integrated a model for sustainability and CSR, with the process groups of the Project Management Body of Knowledge (PMBOK®) standard, in its application to the training of a group of students in Project Design, Management, and Evaluation. The integration was justified by the scarce explicit references to sustainability and CSR found in traditional project management guidelines, norms, and standards. The new framework was used to structure a Sustainability Management Plan, which made it possible to incorporate sustainability criteria throughout the life cycle of the training project. The training proposal in Project Design, Management, and Evaluation was chosen, among several alternatives, by a multi-criteria selection process (fuzzy AHP) in the context of project scope management. The results reveal a great heterogeneity among the models and the lack of a base of key indicators in sustainability and CSR measurement tools as well as of explicit references to sustainability in project management standards. It is therefore necessary to develop a Sustainability Management Plan that can be introduced in the Project Management Plan and thus influence the strategic and operational guidelines of the Institution.

Recently, there has been a heightened interest in using triply periodic minimal surfaces (TPMSs) in the design of compact process engineering components. The benefits of high surface area per unit volume, modular form, and inherent periodicity provide a holistic self-supporting network and flow-conducive features. Applications of importance include thermal power management, biomimetic scaffolds and structures, and feasibility of advanced manufacturing. This study presents a novel approach to the manipulation of the characteristic Schwarz-G, or gyroid TPMS, for thermal design in the context of advanced manufacturing. The study presents relationships between design parameters and resulting surface area as a target response using the characteristic equation of a gyroid. Through parametric control, the characteristic equation is manipulated to produce a 20-fold increase in achievable area over a baseline design characteristic of 25.4 mm through controlled combinations of design parameters. A second relationship is presented as a function of the maximum area achieved and manipulated design parameters. Through the analysis, the study presents a framework to identify and maximize the achievable area of TPMSs for advanced manufacturing and thermal management applications.