Review of conventional metaheuristic techniques for 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.

Each university has different regulations in making lecture schedule according to the conditions of respective institution. In this study, regulation on lecture scheduling used the maximum limit of credits of lecturer in a day, in which if exceeds the maximum limit, then the schedule of course will be moved on another day. This study attempts to optimize the schedule preparation in each semester by considering the maximum limit of credits of a lecturer in a day. Lecture scheduling is a combination of space, time, and resources. It is categorized into combinatorial optimization group. There are two methods for solving the combinatorial optimization problems, i.e. exact and approximation method. The approximation method consists of two types, heuristics and metaheuristics. The algorithm metaheuristics categories include genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), bee colony optimization (BCO), simulated annealing, and so on. This paper employed metaheuristics approach. Research on the preparation of lecture scheduling using ACO algorithm has been conducted and proven to be able to prepare the lecture scheduling. The ACO algorithm has numbers of parameters that in solving issues, one must manually set a number of parameters by using the Design of Experiments (DoE) tool, which takes time to solve the optimization problem. Hence, to solve the issue, an automated parameter optimization is required. PSO algorithm has fewer parameters compared to the ACO. Therefore, the purpose of this paper is a hybrid between PSO and ACO in preparing lecture scheduling.

To enrich the calculation means of resource-constrained project scheduling problem(RCPSP), a particle swarm optimization (PSO) algorithm is presented. A new permutation of priority-based encoding scheme is designed in the algorithm, and it inherits all the merits of both the permutation-based encoding scheme and the priority-based encoding scheme. The velocity-updating mechanism is formulized based on the vector similarity theory according to the characteristics of RCPSP, and in the position-updating mechanism the particles can move according to the so called vector velocity and dimension velocity. A full factorial computational experiment was set up using the well-known standard instances in PSPLIB, and the results reveal that the algorithm is effective for the RCPSP.

Metaheuristic approaches receive a great interest in the area of optimization, especially when exact methods are missing, or the cost is extremely high. Besides the possibility to report good solutions in reasonable time, metaheuristic techniques are widely applicable. There are diverse categories of techniques that differ in number of search agents (or solutions), solution representation, and movement mechanism in search space. Just mentioned ingredients are determined according to the motivation or inspiration philosophy behind the technique. Nature-inspired optimization category is very popular and has proven high efficiency in many problems. It contains famous subclasses like evolutionary algorithms, swarm intelligence, and single-based techniques. Famous and classical examples of each subclass are genetic algorithm, particle swarm, and ant colony optimization, and simulated annealing, respectively. Nature-inspired optimization family grows so fast, and many members have joined it recently, for example, emperor penguin colony (2019), seagull optimization algorithm (2019), sailfish optimizer (2019), pity beetle algorithm (2018), emperor penguin optimizer (2018), multi-objective artificial sheep algorithm (2018), salp swarm algorithm (2017), electromagnetic field optimization (2016), sine cosine algorithm (2016), moth-flame optimization (2015), grey wolf optimizer (2014), flower pollination algorithm (2012), bat algorithm (2010), cuckoo search algorithm (2009), firefly algorithm (2008), and many others. There are many proposed hybridization and cooperation methods between techniques to produce improved versions of original ones. Nature-inspired techniques have been used in many application areas like theoretical computer science, engineering and control, forecasting, medical field, finance, management, operation research, and others. Also, new scientific disciplines like renewable energy, robotics, and navigation are feasible areas to make use of nature-inspired techniques. This chapter sheds light on six so recently new techniques that belong to nature-inspired optimization class.

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.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. These problems cannot be solved in isolation because of the interaction that exists between them. Many evolutionary algorithms have been applied to this problem most commonly the Genetic Algorithm (GA). It has been common practice to improve the performance of the GA with some local search techniques. The Bi-population Genetic Algorithm (BPGA) is one of the most competitive GAs for solving the MRCPSP. In this paper, we improve the BPGA by hybridising it with an Estimation of Distribution Algorithm that focuses on improving how modes are generated. We also suggest improvement to the existing experimental methodology.

To confront the Resource Constrained Project Scheduling Problem (RCPSP), metaheuristics have been proved very good alternatives, especially for large complicated projects. In this class of algorithms, Evolutionary Computation has recently gained much attention, with most important representative the Genetic Algorithms. Following the mainstream, we stress our efforts on another evolutionary algorithm, the Evolution Strategies (ES). The application of ES takes place under two discrete solution encodings; one works on vectors of priority values and the other is based on convex combinations of priority rules. The analysis of the results, produced from tests on the PSPLIB, inspired the development of two extended algorithms. The first extension assumes that ES work on vectors of priority values but the underlying evolutionary operators are modified so as to allow a fast reordering of activities. The second extension concerns the construction of a novel solution encoding which combines the priority values and the convex combination of priority rules. Both proposals indicate a far better performance when compared with genetic algorithms, hence, open a new research direction in the domain of project scheduling with evolutionary algorithms.

Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and two types of resources for execution: storage resources representing, e.g., the project budget; and renewable resources representing, e.g., personnel or equipment. Storage resources are consumed by activities at their start or produced upon their completion, while renewable resources are allocated to activities at their start and released upon their completion. The resource-constrained project scheduling problem with consumption and production of resources (RCPSP-CPR) consists of determining a minimum-makespan schedule such that all precedence relations are respected, the demand for each renewable resource never exceeds its capacity, and the stock level of each storage resource never falls below a prescribed minimum. Due to the consideration of storage resources, the feasibility variant of this problem is NP-complete. We propose a novel compact mixed-integer linear programming (MILP) model based on a novel type of sequencing variables. These variables enable us to identify which activities are processed in parallel and whether a sequencing of activities is necessary to respect the resource capacities. Our computational results indicate that our novel model significantly outperforms state-of-the-art MILP models for all considered scarcity settings of the storage resources. Additionally, our results indicate a superior performance for instances of the well-known resource-constrained project scheduling problem (RCPSP).

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.

Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP hard problem and more intelligent optimization algorithms are developed to solve it. In this paper, genetic algorithm(GA) is employed to deal with RCPSP. A priority value encoding scheme is designed to in the algorithm. The numerical results indicate that our methods is slightly better as far as solution quality is concerned and requires smaller solution time than the GA where an activity list encoding with schedule mode is used.

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.

The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms - Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) - to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale problems.

A project can be defined as a set of activities. Each activity needs a certain amount of time (that can be variable or even stochastic) and requires different type and quantity of resources (tools, personnel, money, etc.). Also, usually there are additional constraints that affect the scheduling of the activities, such as precedence constraints (for example, an activity cannot start until another one has finished), time constraints (do not start or finish before or later than a certain date) and simultaneously constraints (activities that cannot be done at the same time). In this chapter, different tools for project scheduling will be introduced: graph representation of time and precedence constraints; Gantt chart and load curves; and heuristic and exact algorithms for project scheduling considering different evaluation criteria. Then, a simple case of project scheduling is presented and solved using the described tools, allowing useful comparison between the graph-based approaches and the exact approach of a Mixed Integer Linear Programming (MILP) model implemented in IBM ILOG CPLEX. The implementation and verification procedure followed here allows the enlargement of project applications beyond commercial applications or open literature.

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.

The resource-constrained project scheduling problem (RCPSP) consists of a set of non-preemptive activities that follow precedence relationship and consume resources. Under the limited amount of the resources, the objective of RCPSP is to find a schedule of the activities to minimize the project makespan. This article presents a new genetic algorithm (GA) by incorporating a local search strategy in GA operators. The local search strategy improves the efficiency of searching the solution space while keeping the randomness of the GA approach. Extensive numerical experiments show that the proposed GA with neighborhood search works well regarding solution quality and computational time compared with existing algorithms in the RCPSP literature, especially for the instances with a large number of activities. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011