Effective Project Scheduling Under Workspace Congestion and Workflow Disturbance Factors

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.

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).

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.

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.

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).

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.

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.

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.

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.

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.

ABSTRACTThis paper proposes an improved critical chain method with fuzzy approach to schedule projects under uncertainty. Utilizing fuzzy numbers can be wise because of the natural uncertainty in estimation of required work to accomplish the project tasks. To minimize the project duration as an objective taking into account resource limitations and other constrains, a resource-constrained project scheduling problem (RCPSP) model is adopted. The output of this model determines the project schedule. Then, a new buffer sizing approach based on the square root of the sum of the squares (SSQ) method is introduced which is modified by coefficients to overcome the schedule risk more efficiently. As a result, a project buffer is added to the end of the project schedule and is used if necessary. The methodology can be used as a control procedure.

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

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