Resource Availability Cost Problem Research Articles

Minimizing the Expected Renewable Resource Costs in a Project with Stochastic Resource Availability

In this paper, we study the well-known resource availability cost problem with stochastic resource availability. The objective is to determine the initial levels of all renewable resources and establish a schedule corresponding to each scenario such that the expected resource availability cost is minimized. We assume that resource shortfalls can be compensated externally but at a noticeable higher cost. We formulate the problem as a two-stage stochastic programming model (TSSPM). We also develop an exact decomposition-based algorithm (DBA) for the particular case of the problem with at most two resources, which also functions as a heuristic for the original problem. Since the number of scenarios influences the performance of the developed solution approaches, we utilize a fast scenario reduction method to reduce the number of scenarios. Computational results indicate that the DBA outperforms the TSSPM formulation in solution quality and CPU runtime.

A Work-content Based Resource Availability Cost Problem: Mathematical Modeling and Solving Procedure

A Work-content Based Resource Availability Cost Problem: Mathematical Modeling and Solving Procedure

The multi-mode resource investment problem: a benchmark library and a computational study of lower and upper bounds

The multi-mode resource investment problem (MRIP) is the multi-mode extension of the resource investment problem, which is also known under the name resource availability cost problem. It is a project scheduling problem with a given due date as well as precedence and resource constraints. The goal is to find a precedence feasible schedule that minimises the resource costs of the allocated resources. To compute these costs, the maximum resource peak is considered regarding renewable resource types, whereas the sum of allocated nonrenewable resource units is used in the case of nonrenewable resources. Many practical and complex project scheduling settings can be modelled with this type of problem. Especially with the usage of different processing modes, time and cost compromises can be utilised by the project manager. In the literature, some procedures for the MRIP have been investigated; however, the computational experiments in these studies have not been carried out on common benchmark instances. This makes a fair comparison of methods difficult. Therefore, we generated novel instances specifically designed for this problem and published them on the website https://riplib.hsu-hh.de. On this website, the instances as well as best-known solution values are available and researchers can also contribute their findings. We investigate these novel instances by proposing and evaluating lower bounds for the MRIP. Additionally, we analyse the proposed instances by applying heuristic as well as exact methods. These experiments suggest that most of the instances are challenging and further research is needed. Finally, we show some computational complexity properties of the NP-hard MRIP.

Evolutionary algorithms for multi-objective stochastic resource availability cost problem

This paper investigates the resource availability cost problem in a PERT-type network, where both activities duration and resource requirement are considered as stochastic parameters. The problem has two objective functions in which the first one, namely the project’s makespan, is to minimize the project’s duration. However, the second one tries to minimize the total cost of resources. Since its NP-hardness is proven in a strong sense, four well-known evolutionary algorithms including strength pareto evolution algorithm II, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and pareto envelope-based selection algorithm II are proposed to solve the problem. Furthermore, to enhance the algorithms’ performance, some efficient mutation and crossover operators, as well as two novel operators called local search and movement, are employed to solution structure for producing new generations. Also, in order to tackle uncertainty, Monte-carlo simulation is utilized. In order to tune the effective parameters, the Taguchi method is used. The performance of our proposed algorithms is evaluated by numerical test problems in different size which generated based on PSPLIB benchmark problems. Finally, to assess the relative performance of the four proposed algorithms, six well-known performance criteria are employed. Using relative percentage deviation and TOPSIS approach, the performance of algorithms is elucidated.

Multi-project Scheduling: Multicriteria Time-cost Trade-off Problem

Managers of multiple projects with overly constrained resources face difficult decisions in how to allocate resources to minimize the average delay per project or the time to complete the whole set of projects.Our offer includes two stages decomposition approach for multi-project scheduling. The first stage of the approach aims to define the projects as aggregations. Each aggregation is determined by solving a resource availability cost problem (RACP) model to complete the project by his critical path. In the second stage, aggregations are used for its resource leveling scheduling to complete the multi-project by a pre-specified project duration. Both stages use priority rule (PR) heuristics. Both stages are based on two parallel schedule generation schemes (SGS) and resource criteria. Each SGS uses two PR. The first SGS, a set of demands must be developed as initial solution. The solutions obtained by the first SGS algorithm with the best resource allocation rule are used as a baseline to compare those obtained by the latter. The second, the initial solution must be optimized. The visualized results of the offered methodology are given.

Constructive Heuristics for Project Scheduling Resource Availability Cost Problem with Tardiness

AbstractThis work proposes a deterministic and a probabilistic constructive heuristic for solving the project scheduling resource availability cost problem with tardiness (RACPT), as well as a new ...

A Taghuchi based Multi Objective Time-Cost Constrained Scheduling for Resource Availability Cost Problem: A Case Study

In this paper, a new multi-objective time-cost constrained resource availability cost problem is proposed. The mathematical model is aimed to minimize resource availability cost by considering net present value of resource prices in order to evaluate the economic aspects of project to maximize the quality of project's resources to satisfy the expectations of stakeholders and to minimize the variation of resource usage during project. Since the problem is NP-hard, to deal with the problem a simulated annealing approach is applied, also to validate our results GAMS software is used in small size test problems. Due to the dependency of SA algorithm to its initial parameters a taghuchi method is used to find the best possible SA parameters combinations to reach near optimum solutions in large size problems.

Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems

We consider the resource availability cost problem and two extensions through general temporal constraints and calendar constraints. With general temporal constraints minimum and maximum time lags between the activities can be ensured. Calendar constraints are used to model breaks in the availability of a resource, e.g., weekends or public holidays of resource types that equal staff. Especially if long-term and capital-intensive projects are under consideration, resource availability cost problems should be applied because in such projects it is more important to minimize the cost than, e.g., the project duration. We present mixed-integer linear programming (MILP) formulations as well as constraint programming (CP) models for the three problems. In a performance study we compare the results of the MILP formulations solved by cplex and the CP models solved by the lazy clause generation solver chuffed on benchmark instances from literature and also introduce new benchmarks. Our CP models close all open instances for resource availability cost problems from the literature.

Project scheduling for minimizing temporary availability cost of rental resources and tardiness penalty of activities

This paper addresses the resource availability cost problem with rental resources where each activity has a given due date to be completed. In this problem setting, the required resources are temporarily rented to accomplish the corresponding activities where the paid fee for the rental resources depends on duration of their availability. In addition, each activity would be subjected to a tardiness penalty if its finish time surpasses its given due date. A mathematical model is presented for the problem and some features of its solution space are established. Also, a best-performed version of ant colony optimization (ACO) algorithm based on Ant Colony System is developed to tackle this strongly NP-Hard problem. The proposed method consists a new compatible schedule generation scheme, a new resource based heuristic role and an efficient local search. In a comprehensive experimental effort, the proposed parameters-tuned approach is compared with the exact solutions obtained by GAMS on several small-scale instances, while results of a competitive metaheuristic based on Genetic Algorithm are employed to validate the developed ACO algorithm for the large-scale instances. Finally, effectiveness of the proposed ACO is analyzed using statistical tests and the impact of the crucial parameters on the resulting solutions is demonstrated.

An effective heuristic for project scheduling with resource availability cost

The resource constrained project scheduling problem (RCPSP) is widely studied in the literature and has a host of applications in practice. As a variant of the RCPSP, the resource availability cost problem (RACP), which has the aim of minimizing the availability costs of renewable resources in order to complete a project subject to a given deadline, is considered in this paper. We divide the RACP into two sub-problems: the sequencing problem and the resource decision problem, and propose a multi-start iterative search heuristic (MSIS) to solve it. For the sequencing problem, an iterative search framework is constructed to effectively search the activity sequences. A two stage resource adjustment procedure and a backward peak elimination procedure is developed for solving the resource decision problem. MSIS is compared with three existing algorithms on both PSPLib and RanGen data sets involving 1380 instances. A complete calibration of the different parameters and operators of MSIS by means of a design of experiments approach is given. Experimental and statistical results show that MSIS outperforms the other three algorithms in both effectiveness and efficiency by a significant margin.

Definition of Ship Outfitting Scheduling as a Resource Availability Cost Problem and Development of a Heuristic Solution Technique

The outfitting process of shipyards building complex ship types, such as offshore, passenger, and military vessels, is becoming more critical to efficient ship production as the number of components installed on these vessels continues to increase. Outfitting of such vessels is generally characterized by disorganization and rework due to a lack of coordination between the shipyard and subcontractors as well as insufficiently detailed planning. This paper presents a mathematical model for the outfitting planning process of a shipyard building complex vessels. A qualitative description is included for the constraints and objectives underlying the developed mathematical model for the Ship Outfitting Scheduling Problem (SOSP). A heuristic solution technique is also developed for solving the SOSP, and a test case of six midship sections from a recently constructed pipelaying vessel is presented to show the feasibility of both the mathematical model and heuristic. This test case shows that it is possible to find a high-quality planning for the SOSP with a reasonable computational effort. Furthermore, it was found that the greatest priority should be given to components that have the earliest deadlines or dependents with such deadlines. Components should also be scheduled in such a way to minimize the required movements of outfitting personnel between the different work sites of a shipyard.

Definition of Ship Outfitting Scheduling as a Resource Availability Cost Problem and Development of a Heuristic Solution Technique

The outfitting process of shipyards building complex ship types, such as offshore, passenger, and military vessels, is becoming more critical to efficient ship production as the number of components installed on these vessels continues to increase. Outfitting of such vessels is generally characterized by disorganization and rework due to a lack of coordination between the shipyard and subcontractors as well as insufficiently detailed planning. This paper presents a mathematical model for the outfitting planning process of a shipyard building complex vessels. A qualitative description is included for the constraints and objectives underlying the developed mathematical model for the Ship Outfitting Scheduling Problem (SOSP). A heuristic solution technique is also developed for solving the SOSP, and a test case of six midship sections from a recently constructed pipelaying vessel is presented to show the feasibility of both the mathematical model and heuristic. This test case shows that it is possible to find a high-quality planning for the SOSP with a reasonable computational effort. Furthermore, it was found that the greatest priority should be given to components that have the earliest deadlines or dependents with such deadlines. Components should also be scheduled in such a way to minimize the required movements of outfitting personnel between the different work sites of a shipyard.

A tabu search method for block erection scheduling in shipbuilding with activity resource elasticity

Traditional optimisation method for block erection scheduling in shipbuilding assumes that the activity durations are determined, and usually takes the minimisation of project makespan as the objective. We analyse the activities in the process of block erection, and address the characteristic of activity resource elasticity that the duration of some activities was elastic and could be impressed by the means of increasing the amount of resource availability. In this paper, we propose an optimisation method for block erection scheduling problem in shipbuilding with the objective of minimising the project cost and project deadline, which is an extension of Resource Availability Cost Problem RACP. The proposed method is a global research algorithm based on tabu search strategy. Experimental results for the simulation proved the effect of the characteristic of activity resource elasticity for the scheduling problem, and tested the performance of our algorithm.

Definition of Ship Outfitting Scheduling as a Resource Availability Cost Problem and Development of a Heuristic Solution Technique

Definition of Ship Outfitting Scheduling as a Resource Availability Cost Problem and Development of a Heuristic Solution Technique

Solving the Multi-Mode Resource Availability Cost Problem in Project Scheduling Based on Modified Particle Swarm Optimization

This paper proposes a model for multi-mode resource availability cost problem (MMRACP) in project scheduling which minimizes the resource availability cost required to finish all activities in a project at a given project deadline. Precedence relations exist among the activities of the project in the model. Furthermore, renewable and nonrenewable resources are both considered. MMRACP is a nondeterministic polynomial time hard (NP-hard) problem, as a result it is very difficult to use an exact method to solve it. For solving MMRACP, we developed a modified particle swarm optimization method combined with path relinking procedure and designed a heuristic algorithm to improve the fitness of the solution. At the end, a computational experiment including 180 instances was designed to test the performance of the modified particle swarm optimization. Comparative computational results show that the modified particle swarm optimization is very effective in solving MMRACP.

Multi-mode resource availability cost problem with recruitment and release dates for resources

This paper investigates the multi-mode resource availability cost problem with recruitment and release dates for resources. This problem is a more realistic model and extended case of the resource availability cost problem. The project contains activities interrelated by finish–start precedence relations with zero time lags, which require a set of renewable resources. First, a mixed integer programming formulation is proposed for the problem. Then, simulated annealing (SA) algorithm is proposed to obtain a satisfying solution for this NP-hard problem. The effectiveness of the proposed algorithm is demonstrated through comprehensive experimentation based on 300 test problems. The results are analyzed and discussed.

Schedule generation scheme for solving multi-mode resource availability cost problem by modified particle swarm optimization

The resource availability cost problem (RACP) (Mohring, Operations Research, 32:89---120, 1984) is commonly encountered in project scheduling. RACP aims to minimize the resource availability cost of a project by a given project deadline. In this study, RACP is extended from a single mode to a multi-mode called multi-mode RACP (MMRACP), which is more complicated than RACP but more convenient in practice. To solve MMRACP efficiently, forward activity list (FAL), a schedule generation scheme, is proposed. Heuristic algorithms are designed according to the characteristics of FAL to repair infeasible solutions and to improve the fitness of the solution. Modified particle swarm optimization (MPSO), which combines the advantages of particle swarm optimization and scatter search, is proposed to make the search for the best solution efficient. Computational experiments involving 180 instances are performed to validate the performance of the proposed algorithm. The results reveal that MPSO using FAL is a very effective method to solve MMRACP.

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 all 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 solving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuristic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimization (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Finally, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.

A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities

We present a tabu search-based hyper-heuristic algorithm for solving construction resource leveling problems, i.e. resource leveling under resource constraints with the prescribed maximum project duration to be equal or greater than the initial/minimum duration, as well as the related resource availability cost problem. The algorithm operates within a commercial project management software package by altering the priorities assigned to activities. The hyper-heuristic controls a set of low-level heuristics, which modify the priorities of selected activities by performing simple moves such as “replace” and “swap”. The most promising heuristics according to their efficiency are applied first, and a tabu list is used to prohibit heuristics with recently poor performance from being applied too soon. The application of the algorithm in three project cases showed that the proposed procedure is promising for handling resource optimization problems. ► Hyperheuristics recently introduced to treat construction project scheduling problems. ► THH is the first tabu hyperheuristic applied to construction scheduling problems. ► THH is a flexible procedure that easily can be applied to different problems. ► Application of the algorithm proved it as promising for treating leveling problems.

An artificial immune system algorithm for the resource availability cost problem

In this paper, an artificial immune system (AIS) algorithm for the resource availability cost problem (RACP) is presented, in which the total cost of the (unlimited) renewable resources required to complete the project by a pre-specified project deadline should be minimized. The AIS algorithm makes use of mechanisms inspired by the vertebrate immune system and includes different algorithmic components, such as a new fitness function, a probability function for the composition of the capacity lists, and a K-means density function in order to avoid premature convergence. All components are explained in detail and computational results for the RACP are presented.