Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

Optimization of Multi-Execution Modes and Multi-Resource-Constrained Offshore Equipment Project Scheduling Based on a Hybrid Genetic Algorithm

Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.

Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem

In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.

An Evolutionary Algorithm with Multi-Local Search for the Resource-Constrained Project Scheduling Problem

This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.

Multi-objective optimization for the multi-mode finance-based project scheduling problem

The finance-based scheduling problem(FBSP)is about scheduling project activities without exceeding a credit line financing limit.The FBSP is extended to consider different execution modes that result in the multi-mode FBSP(MMFBSP).Unfortunately,researchers have abandoned the development of exact models to solve the FBSP and its extensions.Instead,researchers have heavily relied on the use of heuristics and meta-heuristics,which do not guarantee solution optimality.No exact models are available for contractors who look for optimal solutions to the multi-objective MMFBSP.CPLEX,which is an exact solver,has witnessed a significant decrease in its computation time.Moreover,its current version,CPLEX 12.9,solves multi-objective optimization problems.This study presents a mixed-integer linear programming model for the multi-objective MMFBSP.Using CPLEX 12.9,we discuss several techniques that researchers can use to optimize a multi-objective MMFBSP.We test our model by solving several problems from the literature.We also show how to solve multi-objective optimization problems by using CPLEX 12.9 and how computation time increases as problem size increases.The small increase in computation time compared with possible cost savings make exact models a must for practitioners.Moreover,the linear programming-relaxation of the model,which takes seconds,can provide an excellent lower bound.

SOLVING RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEMS WITH BI-CRITERIA HEURISTIC SEARCH TECHNIQUES

In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-processing phase, the algorithm estimates distance between a state and the goalstate and measures complexity of problem instances. In the search phase, the algorithm uses estimatesof the pre-processing phase to further estimate distances to the goal state. The search continues in astepwise generation of a series of intermediate states through search path evaluation process withbacktracking. Developments of intermediate states are exclusively based on a bi-criteria new stateselection technique where we consider resource utilization and duration estimate to the goal state. Wealso propose a variable weighting technique based on initial problem complexity measures.Introducing this technique allows the algorithm to efficiently solve complex project schedulingproblems. A numerical example illustra

Hybrid genetic algorithm for bi-objective resourceconstrained project scheduling

In this study,we considered a bi-objective,multi-project,multi-mode resource-constrained project scheduling problem.We adopted three objective pairs as combinations of the net present value(NPV)as a financial performance measure with one of the time-based performance measures,namely,makespan(Cmax),mean completion time(MCT),and mean flow time(MFT)(i.e.,minCmax/maxA^PF,minA/Cr/max7VPF,and min MFTI mdixNPV).We developed a hybrid non-dominated sorting genetic algorithm Ⅱ(hybrid-NSGA-Ⅱ)as a solution method by introducing a backward-forward pass(BFP)procedure and an injection procedure into NSGA-Ⅱ.The BFP was proposed for new population generation and post-processing.Then,an injection procedure was introduced to increase diversity.The BFP and injection procedures led to improved objective functional values.The injection procedure generated a significantly high number of non-dominated solutions,thereby resulting in great diversity.An extensive computational study was performed.Results showed that hybrid-NSGA-Ⅱ surpassed NSGA-Ⅱ in terms of the performance metrics hypervolume,maximum spread,and the number of nondominated solutions.Solutions were obtained for the objective pairs using hybrid-NSGA-Ⅱ and three different test problem sets with specific properties.Further analysis was performed by employing cash balance,which was another financial performance measure of practical importance.Several managerial insights and extensions for further research were presented.

Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment

In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.

Heuristic algorithm for RCPSP with the objective of minimizing activities' cost

Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.

Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology–Part I Model and method

Purpose–This is the first part of a two-part paper.The purpose of this paper is to report on methods that use the Response Surface Methodology(RSM)to investigate an Evolutionary Algorithm(EA)and memory-based approach referred to as McBAR–the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants.Some of the methods are useful for investigating the performance(solution-search abilities)of techniques(comprised of McBAR and other selected EAbased techniques)for solving some multi-objective dynamic resource-constrained project scheduling problems with time-varying number of tasks.Design/methodology/approach–The RSM is applied to:determine some EA parameters of the techniques,develop models of the performance of each technique,legitimize some algorithmic components of McBAR,manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment.Findings–The results of applying the methods are explored in the second part of this work.Originality/value–The models are composite and characterize an EA memory-based technique.Further,the resiliency of techniques is determined by applying Lagrange optimization that involves the models.

Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology–PartⅡExperiments and analysis

Purpose–This is the second part of a two-part paper.The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm(EA)and memory-based approach referred to as McBAR–the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants.Design/methodology/approach–The methods applied in this paper are fully explained in the first part.They are utilized to investigate the performances(ability to determine solutions to problems)of techniques composed of McBAR and some EA-based techniques for solving some multi-objective dynamic resource-constrained project scheduling problems with a variable number of tasks.Findings–The main results include the following:first,some algorithmic components of McBAR are legitimate;second,the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above-mentioned problems;and third,McBAR has the most resilient performance among the techniques against changes in the environment that set the problems.Originality/value–This paper is novel for investigating the enumerated results.