Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

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.

Minimal feasible sets in variable resource constrained projects

To determine the reasonable resource dependent relations between activities for the purpose of exactly computing the total floats and the free floats of activities, correctly identifying critical activities and critical sequences in a project schedule with variable resource constraints, the concept of the minimal feasible set （MFS） is proposed and the properties of MFS are discussed. The methods to identify optimal MFSs and resource links are then studied. Furthermore, MFS is generalized to the situation that the preconditions of MFS are not satisfied. Contrastive results show that in establishing resource links and resolving floats, MFS is at least not inferior to other methods in all cases and is superior in most situations.

Hybrid ant colony optimization for the resource-constrained project scheduling problem

To solve the resource-constrained project scheduling problem （RCPSP）, a hybrid ant colony optimization （HACO） approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved. The HACO is tested on the set of large benchmark problems from the project scheduling problem library （PSPLIB）. The computational result shows that the proposed algo- rithm can improve the quality of the schedules efficiently.

Solving resource availability cost problem in project scheduling by pseudo particle swarm optimization

This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem （RACP） by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion （PPSO） combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.

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.

Hybrid algorithm for project scheduling with capacity constraint

Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.

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.

Network Analysis for Projects with High Risk Levels in Uncertain Environments

The critical path method is one of the oldest and most important techniques used for planning and scheduling projects.The main objective of project management science is to determine the critical path through a network representation of projects.The critical path through a network can be determined by many algorithms and is useful for managing,monitoring,and controlling the time and cost of an entire project.The essential problem in this case is that activity durations are uncertain;time presents considerable uncertainty because the time of an activity is not always easily or accurately estimated.This issue increases the need to use neutrosophic theory to solve the critical path problem.Real-world problems are characterized by a lack of precision,consistency,and completeness.The concept of neutrosophic sets has been introduced as a generalization of fuzzy,intuitionistic fuzzy,and crisp sets to overcome the ambiguity surrounding real-world problems.Truth-,falsity-,and indeterminacy-membership functions are used to express neutrosophic elements.This study was performed to examine a neutrosophic event-oriented algorithm for determining the critical path in activity-on-arc networks.The activity time estimates are presented as trapezoidal neutrosophic numbers,and score and accuracy functions are used to obtain a crisp model of the problem.An appropriate numerical example is then used to explain the proposed method.

Managing project risks and uncertainties

This article considers threats to a project slipping on budget,schedule and fit-for-purpose.Threat is used here as the collective for risks（quantifiable bad things that can happen）and uncertainties（poorly or not quantifiable bad possible events）.Based on experience with projects in developing countries this review considers that（a）project slippage is due to uncertainties rather than risks,（b）while eventuation of some bad things is beyond control,managed execution and oversight are stil the primary means to keeping within budget,on time and fit-for-purpose,（c）improving project delivery is less about bigger and more complex and more about coordinated focus,effectiveness and developing thought-out heuristics,and（d）projects take longer and cost more partly because threat identification is inaccurate,the scope of identified threats is too narrow,and the threat assessment product is not integrated into overall project decision-making and execution.Almost by definition,what is poorly known is likely to cause problems.Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage,but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns.Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals.This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.

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.

A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.

Scheduling Multi-Mode Projects under Uncertainty to Optimize Cash Flows: A Monte Carlo Ant Colony System Approach

Project scheduling under uncertainty is a challenging field of research that has attracted increasing attention. While most existing studies only consider the single-mode project scheduling problem under uncertainty, this paper aims to deal with a more realistic model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows （S-MRCPSPDCF）. In the model, activity durations and costs are given by random variables. The objective is to find an optimal baseline schedule so that the expected net present value （NPV） of cash flows is maximized. To solve the problem, an ant colony system （ACS） based approach is designed. The algorithm dispatches a group of ants to build baseline schedules iteratively using pheromones and an expected discounted cost （EDC） heuristic. Since it is impossible to evaluate the expected NPV directly due to the presence of random variables, the algorithm adopts the Monte Carlo （MC） simulation technique. As the ACS algorithm only uses the best-so-far solution to update pheromone values, it is found that a rough simulation with a small number of random scenarios is enough for evaluation. Thus the computational cost is reduced. Experimental results on 33 instances demonstrate the effectiveness of the proposed model and the ACS approach.

Combinatorial auction algorithm for project portfolio selection and scheduling to maximize the net present value

Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model is formulated for the project portfolio selection and scheduling problem. An iterative multi-unit combinatorial auction algorithm is proposed to select and schedule project portfolios through a distributed bidding mechanism. Two price update schemes are designed to adopt either a standard or an adaptive Walrasian tatonnement process. Computational tests show that the proposed auction algorithm with the adaptive price update scheme selects and schedules project portfolios effectively and maximizes the total net present value. The price profile generated by the algorithm also provides managerial insights for project managers and helps to manage the scarce resources efficiently.

Developing Project Duration Models in Software Engineering

Based on the empirical analysis of data contained in the International Software Benchmarking Standards Group （ISBSG） repository, this paper presents software engineering project duration models based on project effort. Duration models are built for the entire dataset and for subsets of projects developed for personal computer, mid-range and mainframe platforms. Duration models are also constructed for projects requiring fewer than 400 person-hours of effort and for projects requiring more than 400 person-hours of effort. The usefulness of adding the maximum number of assigned resources as a second independent variable to explain duration is also analyzed. The opportunity to build duration models directly from project functional size in function points is investigated as well.

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.

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.