Solving Project Scheduling Problems Research Articles

A heuristic rule adaptive selection approach for multi-work package project scheduling problem

Effectively scheduling a project is crucial for its success, especially after generating work packages from the work breakdown structure during the planning phase. Nevertheless, solving project scheduling problems with multiple work packages is challenging due to the inefficient utilization of work package information in existing scheduling approaches. To address this issue, this paper proposes the Heuristic Rule Adaptive Selection (HAS) approach for the Multi-Work Package Project Scheduling Problem (MWPSP). This approach involves work package information and employs reinforcement learning (RL) for intelligent decision-making in scheduling. First, the MWPSP with the optimization objective of minimizing the Portfolio Delay (PDEL) and the Average Percent Delay (APD) is defined, and a scheduling environment is established that integrates information from both work packages and tasks. Second, a Double Deep Q-network (DDQN) is employed to train agents for adaptively selecting heuristic rules of tasks and work packages. The performance of the HAS approach is then evaluated using a case project and the newly created MWPSP dataset. The experimental results demonstrate that the HAS approach exhibits superior solution quality and computational efficiency in optimizing PDEL and APD compared to heuristics approaches, e.g., single-priority rule-based heuristics and genetic algorithms. This achievement sets the stage for the development of next-generation adaptive scheduling for construction projects.

Modified multi-objective evolutionary programming algorithm for solving project scheduling problems

Though the Genetic Algorithm (GA) has received considerable attention recently in solving multi-objective optimization problems, inefficiency regarding performance has been reported in applications related to project scheduling. The degradation in efficiency was magnificent in applications of highly epistatic objective functions, including scheduling problems wherein the parameters being optimized are highly correlated. Furthermore, the crossover, being the dominant operator in GA, added significantly to the observed inefficiency for causing violations in the dependency between activities. Unlike GA, the Evolutionary Programming (EP) algorithm employs only a mutation operator which makes it less vulnerable to the dependency violation issue. This study proposes a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to model and solve scheduling problems of multi-mode activities, including time–cost trade-off and finance-based scheduling with resource levelling. The modification involves the implementation of a new mutation operator to accommodate the scheduling problems in hand. Furthermore, the modified MOEP algorithm is benchmarked against the two multi-objective algorithms of SPEA-II and NSGA-II which have been used extensively in the literature to solve project scheduling problems. The results indicated that the modified MOEP algorithm outperformed SPEA-II and NSGA-II in terms of the diversity and quality of the Pareto optimal set.

A New Priority Rule for Solving Project Scheduling Problems

Priority rule-based scheduling technique is a scheduling method for constructing minimum feasible schedules when solving project scheduling problems. This approach is made up of two parts: a priority rule for determining the activity list and a schedule generation scheme which constructs the feasible schedule of the constructed activity list. Quite a number of priority rules are available, selecting the best one for a particular input problem is extremely difficult. Hence, we present a new priority rule which assembles a set of priority rules and uses machine learning to form a hybrid strategy out of the assembled strategies. The hybrid strategy operates by choosing the strategy with the best performance at every point in time to construct an activity list of a project. The one with better performance is used most frequently. This removes the problem of manually searching for the best priority rule amongst the dozens of rules that are available. Experimentally, we solved a fictitious single-mode resource-constrained project scheduling problem (single-mode RCPSP) which was solved with 13 different priority rules in Pm Knowledge Center. Our result showed that the total completion time of the project obtained with our approach competes favorably with the completion times gotten with the 13 priority rules. Additionally, we computed initial population for Genetic Algorithm in solving some multi-mode RCPSP. We compared our results with the initial solutions the authors started with, and our results competes favorably with their initial solutions, making our algorithm a good entry point for metaheuristic procedures.

Closure to “Ant Colony Optimization for Multimode Resource-Constrained Project Scheduling” by Hong Zhang

Many thanks for the comments of the discussers regarding the paper. The discussers primarily remarked that the examples used for comparison among the methods of ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) are very small because the metaheuristic methods are inefficient (e.g., time consuming) when handling large-scale problems. The discussers also suggested that future work focus on introducing efficient methods to handle practical sized problems. The discussers finally concluded that introducing new metaheuristics to achieve minor performance improvement to small textbook-type problems has no practical value. In consideration of the comments of the discussers, some points are provided in the following. 1. The paper focused on introducing an alternative metaheuristic method, i.e., an ACOmethod, for solving multimode resourceconstrained project scheduling problems in comparison with the GA and PSO methods. Although the examples used for comparison are not large, the performances of the methods under comparison are meaningful because they are compared under the same conditions, such as the same computer platform. 2. It is difficult and time consuming for the ACO method to handle practical sized problems, so the ACO method needs to be developed further with regard to computing efficiency by developing new computing techniques, such as the parallel computing framework described by Kandil and El-Rayes (2005) or other alternative computing frameworks, in consideration of the inconvenience or unavailability of multiple processors for general users. 3. The number of activities in a project may differ according to various levels of decomposing and different management focuses or periods for different contractors, so small or medium sized projects also represent practical problems. Meanwhile, when considering multiple modes of activities and resource constraints, optimal scheduling of these small or medium size projects is also complicated, in contrast to the large sized projects that do not consider multiple modes of activities and resource constraints. The discussers’ comments are meaningful and constructive for further studies into ACO and other metaheuristic methods for solving project scheduling problems of practical sizes.

Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm

There are various scheduling problems with resource limitations and constraints in the literature that can be modeled as variations of the Resource Constrained Project Scheduling Problem (RCPSP). This paper proposes a new solution representation and an evolutionary algorithm for solving the RCPSP. The representation scheme is based on an ordered list of events, that are sets of activities that start (or finish) at the same time. The proposed solution methodology, namely SAILS, operates on the event list and relies on a scatter search framework. The latter incorporates an Adaptive Iterated Local Search (AILS), as an improvement method, and integrates an event-list based solution combination method. AILS utilizes new enriched neighborhoods, guides the search via a long term memory and applies an efficient perturbation strategy. Computational results on benchmark instances of the literature indicate that both AILS and SAILS produce consistently high quality solutions, while the best results are derived for most problem data sets.

Solving Project Scheduling Problems using Estimation of Distribution Algorithm with Local Simplex Search

Solving Project Scheduling Problems using Estimation of Distribution Algorithm with Local Simplex Search

Lagrangian relaxation-based lower bound for resource-constrained modulo scheduling

Abstract In this work we propose a Lagrangian relaxation of a time indexed integer programming formulation to compute a lower bound for the resource-constrained modulo scheduling problem (RCMSP). Solving the RCMSP consists in finding a 1-periodic schedule minimizing the period subject to both temporal and resource constraints. This work is inspired by Mohring et al results [Mohring, R.H., A. S. Schulz, F. Stork and M. Uetz Solving Project Scheduling Problems by Minimum Cut Computations , Management Science. 49 (2003), 330–350] for the (non cyclic) resource constrained project scheduling problem, where each subproblem solved within the subgradient optimization is equivalent to a minimum cut problem. Experimental results, presented on instruction scheduling instances from the STMicroelectronics ST200 VLIW processor family, underline the interest of the proposed method.

A GENERALIZED HEURISTIC LEARNING APPROACH TO PROJECT SCHEDULING PROBLEMS WITH RESOURCE CONSTRAINTS

ABSTRACT We present a generalized heuristic learning algorithm and a solution approach for its implementation in solving project scheduling problems with resource constraints. The search process of the algorithm is characterised by the complete heuristic learning process: state selection, heuristic learning, and search path review. The heuristic learning process enables the algorithm to continue to improve the state selection decision. The heuristic learning threshold of the algorithm allows users to specify solution quality, optimal or near-optimal solutions, with efficient computation. The implementation approach is based on the dynamic nature of activity status and resource availability of a project. It consists of states, state transition operator, heuristic estimate, and the cost of transition between states. The performance analysis of this algorithm with Patterson's 110 problem is presented.

Exact Algorithm for Solving Project Scheduling Problems under Multiple Resource Constraints

This paper presents a new algorithm, called the enumerative branch-and-cut procedure (EBAC), for minimizing the total project duration of a construction project under multiple resource constraints based on an enumeration tree. The EBAC generates new branches to the tree corresponding to “better” feasible alternatives. It starts with all of the feasible schedule alternatives as the trial schedule alternatives at any node. The trial schedule alternatives are then evaluated to determine whether they are “worse” than any existing partial schedules in the tree by using the presented cut rules, and a worse alternative will be eliminated from the enumeration tree. In other words, the tree will contain only better feasible schedules. The presented algorithm has been coded in the VB6.0 language on a personal computer. It has been tested with the 110 scheduling problems, which have been widely used for validating a variety of schedule algorithms over the last 20 years . The EBAC can obtain the shortest project dura...

Solving Project Scheduling Problems by Minimum Cut Computations

In project scheduling, a set of precedence-constrained jobs has to be scheduled so as to minimize a given objective. In resource-constrained project scheduling, the jobs additionally compete for scarce resources. Due to its universality, the latter problem has a variety of applications in manufacturing, production planning, project management, and elsewhere. It is one of the most intractable problems in operations research, and has therefore become a popular playground for the latest optimization techniques, including virtually all local search paradigms. We show that a somewhat more classical mathematical programming approach leads to both competitive feasible solutions and strong lower bounds, within reasonable computation times. The basic ingredients of our approach are the Lagrangian relaxation of a time-indexed integer programming formulation and relaxation-based list scheduling, enriched with a useful idea from recent approximation algorithms for machine scheduling problems. The efficiency of the algorithm results from the insight that the relaxed problem can be solved by computing a minimum cut in an appropriately defined directed graph. Our computational study covers different types of resource-constrained project scheduling problems, based on several notoriously hard test sets, including practical problem instances from chemical production planning.

Solving Project Scheduling Problems by Minimum Cut Computations

In project scheduling, a set of precedence-constrained jobs has to be scheduled so as to minimize a given objective. In resource-constrained project scheduling, the jobs additionally compete for scarce resources. Due to its universality, the latter problem has a variety of applications in manufacturing, production planning, project management, and elsewhere. It is one of the most intractable problems in operations research, and has, therefore, become a popular playground for the latest optimization techniques, including virtually all local search paradigms. We show that a somewhat more classical mathematical programming approach leads to both competitive feasible solutions and strong lower bounds, within quite reasonable computation times. The basic ingredients of our approach are the Lagrangian relaxation of a time-indexed integer programming formulation and relaxation-based list scheduling, enriched with a useful idea from recent approximation algorithms for machine scheduling problems. The efficiency of the algorithm results from the insight that the relaxed problem can be solved by computing a minimum cut in an appropriately defined directed graph. Our computational study covers different types of resource-constrained project scheduling problems, based on several, notoriously hard test sets, including practical problem instances from chemical production planning.

An intelligent search method for project scheduling problems

We develop an approach for implementing a real time admissible heuristic search algorithm for solving project scheduling problems with resource constraints. This algorithm is characterized by the complete heuristic learning process: state selection, heuristic learning, and search path review. The implementation approach is based on the network structure and the activity status of a project; which consists of definition of states, state transition operator, heuristic estimation, and state transition cost. The performance analysis with a benchmark problem shows that, the accumulation of heuristic learning during the search process leads to the re-scheduling of more promising activities, and finds an optimal schedule efficiently.

Understanding Simulation Solutions to Resource Constrained Project Scheduling Problems with Stochastic Task Durations

The project scheduling problem domain is an important research and applications area of engineering management. Recently introduced project scheduling software such as Risk+, @Risk for Project, SCRAM and Risk Master have facilitated the use of simulation to solve project scheduling problems with stochastic task durations. Practitioners, however, should be made aware that the solution algorithm used in these software systems is based on the implicit assumption of perfect information, an assumption that jeopardizes the feasibility of solution results. This paper discusses the impact of assuming perfect information, introduces a multi-period stochastic programming based model of the project scheduling problem with stochastic task durations, and presents an alternative simulation algorithm that does not assume the availability of perfect information. A simple case study is used to illustrate the practical implications of applying simulation to address project scheduling problems with stochastic task durations.

Solving project scheduling problems with a heuristic learning algorithm

Solving project scheduling problems with a heuristic learning algorithm

Solving project scheduling problems with a heuristic learning algorithm

We have developed an approach to implement a real time admissible heuristic search algorithm to solve project scheduling problems. This algorithm is characterised by the complete heuristic learning process: state selection, heuristic learning, and search path review. This implementation approach is based on the dynamic nature of the activity status and the resource availability of a project. It consists of states, state transition operator, heuristic estimate, and the cost of transition between states. The performance analysis shows that the accumulation of heuristic learning during the search process has led to the re-scheduling of resource dominating activities, which is a major factor in controlling the overall project completion time.

Solving Project Scheduling Problems with a Heuristic Learning Algorithm

Solving Project Scheduling Problems with a Heuristic Learning Algorithm