Artificial Bee colony for 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).

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.

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.

This work integrates artificial bee colony (ABC) and bees algorithm (BA) to develop a two bees (TB) algorithm. Agents of TB are stochastically assigned to ABC and BA sub-swarms in each iteration according to their fitness values. Consequently, the current healthier sub-swarm gains more agents to carry out its actions. Sub-swarm populations therefore vary ceaselessly during iterations, while either the ABC or BA sub-swarms may be superior to the other. Experiments are conducted on 23 benchmark functions. Results demonstrate that the TB performs better than or close to the ABC or BA winner. TB overcomes the poor performance of ABC and BA in handling particular problems.

This paper provides a fast near‐optimum solution to the Multi‐Mode Resource Constrained Project Scheduling Problem (MRCPSP), for projects with activities that have known deterministic renewable and non‐renewable resource requirements. Multi‐modes allows any activity to execute one of many alternative combinations of resource usages and durations, while each activity needs to pick the most feasible one. This type of setting is more prevalent for real‐life project schedules, which necessitates systematic methodologies and advanced solution approaches in harmony with real‐world implementations. This study proposes a Modified Variable Neighbourhood Search Heuristic (MVNSH) algorithm as an advanced optimization technique for scheduling projects. To validate our approach, exhaustive computational tests were carried out by using relevant benchmark instances. The MVNSH algorithm has been shown to be faster and more accurate than many existing heuristic methods. This research also contributes to developing a practical decision support system for resolving real‐life constraints in multi‐mode resource‐constrained projects.

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 resource constrained project scheduling problem is considered as a complex scheduling problem. In order to solve this NP-hard problem, an efficient differential evolution DE algorithm is proposed in this paper. In the algorithm, improved mutation and crossover operators are introduced with an aim to maintain feasibility for generated individuals and hence being able to converge quickly to the optimal solutions. The algorithm is tested on a set of well-known project scheduling problem library PSPLIB, with instances of 30, 60, 90 and 120 activities. The proposed DE is shown to have superior performance in terms of lower average deviations from the optimal solutions compared to some of the state-of-the-art algorithms.

In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing Bees Algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.

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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.

Projects represent the principal means of materialization of products. The inherent complexity of product projects is treated through the techniques and approaches project management. Throughout products life cycle the techniques and approaches project management are mainly involved in the planning, programming and control of project activities conducted in context of resource constrained under uncertainties. In addition of scenarios the complexity of the projects, there are some classes of products, typical of industries of the defense, aerospace, telecommunication, software, and biomedicine, which are problematic for current methods of resource constrained project planning and scheduling under uncertainty. The existing methods fail because they suffer from one or more of the following limitations: focused mainly on the basic RCPSP (Resource Constrained Project Scheduling Problem) model; dealing with only one source of uncertainty, mostly in duration of activities; and do not model uncertainties.

In this study, a cluster-based heuristic algorithm to solve multi-mode resource constrained project scheduling problem (MMRCPSP) is presented. The MMRCPSPs are usually challenging to schedule because each activity has multiple execution modes with renewable and non-renewable resources that are categorized as an NP-hard problem. In this paper, we propose and apply three ideas to solve an MMRCPSP. Firstly, an MMRCPSP with two objectives namely, makespan and executive cost is converted into a single mode resource-constrained project scheduling problem (RCPSP) with three objectives by relaxing the non-renewable resource constraints and then defining a corresponding penalty value. In the second phase, in the light of precedence and renewable resource constraints, the new RCPSP are scheduled by calling the multi-directional scheduling schemes (multi-dss). In the end, on the basis of three objectives’ value (namely makespan, cost, and penalty value), we defined a density-based fitness function for an evolutionary approach regarding the clustering methods on the new solution space. Our numerical results showed that the multi-dss with higher dimension regarding the automatic cluster-based fitness functions affected the performance of the evolutionary approach. Furthermore, the evolutionary approach based on multi-directional scheduling schemes prevents to trap into the local optimums by increasing the solutions’ diversity.

The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem class of scheduling problems. It even contains the well-known job shop scheduling problem as a special case. In these problems, jobs demand different amounts from multiple resources at once. The goal is to minimize completion time while satisfying resource capacity constraints throughout the schedule. The RCPSP has several variations and generalizations, one of which is the multi-mode problem. Multiple operation modes are given for jobs, differing in resource usages and execution times. Another generalization is where resources have time-varying capacities instead of a constant limit. The S-graph framework was developed for batch process scheduling and used successfully in various applications. The advantages of this approach motivate research to extend its capabilities to more general problem classes and apply it in various specialized case studies. The authors have previously presented an extension of the S-graph framework for RCPSP and its multi-mode generalization. In this work, a further extension is proposed for handling time-varying capacities. The method relies on a model transformation technique and recent algorithmic developments of the framework.

In this paper, a Pre-emptive Resource Constrained Project Scheduling Problem (PRCPSP) mathematical model is proposed to schedule a production project by considering uncertainty of time parameter for each activity. At last to evaluate the validity of the model, the mathematical model is implemented in a production factory and the results have been evaluated. In addition, some analyses are implemented to assess the behavior of the model and extract managerial insights.

In this paper, a permutation chromosome encoding scheme is proposed for obtaining solution to resource constrained project scheduling problem. The proposed chromosome coding method is applied to Genetic algorithm procedure and implemented through object oriented programming. The method is applied to a numerical example with two different stopping criteria. The results obtained are encouraging as they are in agreement with published solutions in literature. The method lends itself easily to programming and can be used to obtain results to large scale problems encountered in practice.http://dx.doi.org/10.4314/njt.v34i1.16