An activity-list-based nested partitions algorithm for resource-constrained project scheduling

The resource-constrained project scheduling problem (RCPSP) has received wide attention. In this paper, an activity-list-based nested partitions algorithm (ALNP) is developed for solving the RCPSP and a P-ALNP is proposed to improve ALNP with local adjustment. In the algorithms, to improve the search efficiency, a partial double justification is employed as local search mechanism. The computational experiments on the PSPLIB and analysis on robustness of the algorithms show that ALNP outperforms the traditional serial scheduling scheme for solving the large-scale, complex RCPSPs, and P-ALNP can improve ALNP and obtain better results. P-ALNP is a competitive algorithm for solving the RCPSP.     

Looking for the best modes helps solving the MRCPSP/max

The multi-mode resource-constrained project scheduling problem with minimum and maximum time lags MRCPSP/max is a very general project scheduling problem with multiple execution modes per activity, renewable and non-renewable resources and minimum and maximum time lags between activities. In this paper, we describe SA-EVA, an algorithm for the problem. SA-EVA first searches for the best mode for each activity, without considering renewable resources. In this phase a simulated annealing is applied. Once a mode vector has been chosen, the problem reduces to the RCPSP/max, which SA-EVA solves with EVA, an algorithm designed in Ballestín et al. [2009. An evolutionary algorithm for the resource-constrained project scheduling problem with minimum and maximum time-lags. Journal of Scheduling, 14 (4), online]. Computational results show that SA-EVA outperforms the state-of-the-art algorithms in medium and large instances.     

An exact algorithm for project scheduling with multiple modes

We consider an extension of the classical resource-constrained project scheduling problem (RCPSP), which covers discrete resource-resource and time-resource tradeoffs. As a result a project scheduler is permitted to identify several alternatives or modes of accomplishment for each activity of the project. The solution procedure to be presented is a considerable generalization of the branch-and-bound algorithm proposed by Demeulemeester and Herroelen, which is currently the most powerful method for optimally solving the RCPSP. More precisely, we extend their concept of delay alternatives by introducing mode alternatives. The basic enumeration scheme is enhanced by dominance rules which increase the performance of the algorithm. We then report on our computational results obtained from the comparison with the most rapid procedure reported in the literature.     

Critical chain project buffer sizing based on resource constraints

Project scheduling is a complex process involving many types of resources and activities that require optimisation. The resource-constrained project scheduling problem is one of the well-known problematic issues when project activities have to be scheduled to minimise the project duration. Consequently, several methods have been proposed for adjusting the buffer size but none of these traditional methods consider buffer sizing accuracy based on resource constraints. The purpose of this paper is to develop a buffer sizing method based on a fuzzy resource-constrained project scheduling problem in order to obtain an appropriate proportionality between the activity duration and the buffer size. Specifically, a comprehensive resource-constrained method that considers both the general average resource constraints (GARC) and the highest peak of resource constraints (HPRC) is proposed in order to obtain a new buffer sizing method. This paper contributes to the research by considering several different aspects. First, this paper adopts a fuzzy method to calculate and obtain the threshold amount. Second, this paper discusses the resource levelling problem and proposes the HPRC method. Third, the proposed method uses a fuzzy quantitative model to calculate the resource requirement. The findings indicate that the project achieved higher efficiency, providing effective protection and an appropriate buffer size.     

Comments on “deadlock prevention policy based on petri nets and siphons”

Some well-known decision problems can be regarded as special cases of the resource-constrained project-scheduling problem (RCPSP) with respect to the structure of their mathematical models. These decision models include assembly line balancing, job shop scheduling and some packing problems. Here, the differences and similarities of mathematical models of the multilevel capacitated lot-sizing problem (MLCLSP) and the RCPSP are shown, and it is concluded that the RCPSP is a special case of the MLCLSP. This theoretical result is complemented by an integrated, general model formulation that allows one to coordinate customer-specific orders and make-to-stock (lot size) production at the master (production) planning level.     

Hybridization of ABC and PSO algorithms for improved solutions of RCPSP

A resource-constrained project scheduling problem (RCPSP) is one of the most famous intractable NP-hard problems in the operational research area in terms of its practical value and research significance. To effectively solve the RCPSP, we propose a hybrid approach by integrating artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Moreover, a novel structure of ABC-PSO is devised based on embedded ABC-PSO (EABC-PSO) and sequential ABC-PSO (SABC-PSO) strategies. The EABC-PSO strategy mainly applies the PSO algorithm to update the process of the ABC algorithm while the SABC-PSO strategy demonstrates an approach in which computational results obtained from the ABC algorithm are further improved based on the PSO algorithm. In both strategies, bees in the ABC process are entitled to learning capacity from the best local and global solutions in terms of the PSO concept. Subsequently, the updates of solutions are premeditated with crossover and insert operators together with double justification methods. Computational results obtained from the tests on benchmark sets show that the proposed ABC-PSO algorithm is efficient in solving RCPSP problems, demonstrating clear advantages over the pure ABC algorithm, the PSO algorithm, and a number of listed heuristics.     

Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept of deterministic project scheduling conflicts with real situations, in which in many cases, some data on the activity' s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations. Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us. We use the multi-mode resource allocation and scheduling model that takes into account the dynamicity features of all parameters, that is, the scheduling process must be flexible to dynamic environment features. In this paper, we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations, in which we apply the proposed heuristic rules (PHR) for scheduling multi-mode resource-constrained projects. Five projects are considered test problems for the PHR. The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems. The results in many cases of the proposed priority rules are very promising, where they achieve better scheduling dates in many test case problems and the same results for the others. The proposed model is based on the dynamic features for project topography.     

资源约束项目排序问题的一种修正蚁群算法

根据蚁群算法的性质与资源约束项目排序问题(CPSP:Resource-Constrained Project Schedul- ing Problem)的特征,本文给出了蚁群算法中信息素的表示及更新方案、启发信息的计算方法等,由此提出了一种求解RCPSP的修正蚁群算法。最后,通过对项目排序问题库中的标准问题集进行计算,结果表明本文提出的修正蚁群算法是可行优良的。     

Stability and resource allocation in project planning

The majority of resource-constrained project scheduling efforts assume perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.     

Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm–fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.     

Machine scheduling in the presence of sequence-dependent setup times and a rate-modifying activity

Rate modifying activity (RM) is a type of maintenance after which the processing rate of the machine increases. RM is a very new topic in academic studies. However, it is very common in real world situations. In this paper, we study the integrated problem of assigning a common due-date to all jobs, scheduling the jobs and making decisions about the position of RM in a single machine environment in which the setup times are sequence dependent. The objective is minimising the summation of earliness costs, tardiness costs and due date related costs. This problem has never been studied in the literature with any arbitrary criterion. We construct a time-dependent travelling salesman problem (TDTSP) formulation for this problem. The position of the optimal common due date and some dominance properties for the position of RM are presented. A branch and bound (B&B) procedure is developed to solve the problem optimally. Numerical results justify the effectiveness of the B&B method for small problems. For larger problems, two robust metaheuristics are proposed.     

Minimising makespan on parallel batch processing machines with non-identical ready time and arbitrary job sizes

This paper considers the parallel batch processing machine scheduling problem which involves the constraints of unequal ready times, non-identical job sizes, and batch dependent processing times in order to sequence batches on identical parallel batch processing machines with capacity restrictions. This scheduling problem is a practical generalisation of the classical parallel batch processing machine scheduling problem, which has many real-world applications, particularly, in the aging test operation of the module assembly stage in the manufacture of thin film transistor liquid crystal displays (TFT-LCD). The objective of this paper is to seek a schedule with a minimum total completion time for the parallel batch processing machine scheduling problem. A mixed integer linear programming (MILP) model is proposed to optimise the scheduling problem. In addition, to solve the MILP model more efficiently, an effective compound algorithm is proposed to determine the number of batches and to apply this number as one parameter in the MILP model in order to reduce the complexity of the problem. Finally, three efficient heuristic algorithms for solving the large-scale parallel batch processing machine scheduling problem are also provided.     

基于混沌粒子群的资源受限项目调度问题

鉴于基本粒子群算法易陷入局部最优,提出一种将混沌算法嵌入基本粒子群的混沌粒子群算法,并将其用于求解典型的资源受限项目调度问题。采用基于优先值的粒子编码方式和串行调度方案,分别用基本粒子群算法和混沌粒子群算法对实例求解。并且比较了2种算法求解多资源受限项目调度问题的性能。结果表明:混沌粒子群算法在距最优值的平均偏差和达到最优值的次数百分比等性能上要优于基本的粒子群算法,并且混沌粒子群具有更好的收敛性。但是,混沌粒子群算法在计算达到最优工期的平均时间上略比基本粒子群算法逊色。     

Flexible job-shop scheduling problems with ‘AND’/‘OR’ precedence constraints

The purpose of this research is to solve flexible job-shop scheduling problems with ‘AND’/‘OR’ precedence constraints in the operations. We first formulate the problem as a Mixed-Integer Linear Program (MILP). The MILP can be used to compute optimal solutions for small-sized problems. We also developed a heuristic algorithm that can obtain a good solution for the problem regardless of its size. Moreover, we have developed a representation and schedule builder that always produces a legal and feasible solution for the problem, and developed genetic and tabu search algorithms based on the proposed schedule builder. The results of the computational experiments show that the developed meta-heuristics are very effective.     

Project scheduling with flexible resources: formulation and inequalities

In this paper, we study a variant of the resource-constrained project scheduling problem in which resources are flexible, i.e., each resource has several skills. Each activity in the project may need several resources for each required skill. We present a mixed-integer linear programming formulation for this problem. Several sets of additional inequalities are also proposed. Due to the fact that some of the above-mentioned inequalities require a valid upper bound to the problem, a heuristic procedure is proposed. Computational experience is reported based on randomly generated data, showing that for instances of reasonable size the proposed model enlarged with the additional inequalities can be solved efficiently.     

Mathematical model and exact algorithm for the home care worker scheduling and routing problem with lunch break requirements

Home health care or home care (HHC/HC) refers to the delivery of social, medical and paramedical services to clients in their own homes. Each day, care workers start from the HHC/HC centre, visit some clients and return to the centre. During the service delivery process, there is usually a lunch break for each worker. In this paper, we address a real-life home care worker scheduling and routing problem with the consideration of lunch break requirements. A three-index mathematical model is constructed for the problem. The problem is decomposed into a master problem and several pricing sub-problems, and is optimally solved by a branch-and-price (B&P) algorithm. Specifically, a sophisticated label-correcting algorithm is designed to address lunch break constraints in pricing sub-problems; some cutting-edge acceleration strategies are applied during the column generation process. Experimental results show that the proposed B&P algorithm is able to produce satisfied solutions within an acceptable runtime and outperforms the mixed integer programming solver CPLEX.     

Daily scheduling of caregivers with stochastic times

This paper addresses a daily caregiver scheduling and routing problem arising in home health care or home care service providers. The problem is quite challenging due to its uncertainties in terms of travel and service times derived from changes in road traffic conditions and customer health status in practice. We first model the problem as a stochastic programme with recourse, where the recourse action is to skip customers without services if the caregiver arrives later than their latest starting service time (i.e. hard time window requirements). Then, we formulate the problem as a set partitioning model and solve it with a branch-and-price (B&P) algorithm. Specifically, we devise an effective discrete approximation method to calculate the arrival time distribution of caregivers, incorporate it into a problem-specific label algorithm, and use a removal-and-insertion-based heuristic and the decremental state-space relaxation technique to accelerate the pricing process. Finally, we conduct numerical experiments on randomly generated instances to validate the effectiveness of the discrete approximation method and the proposed B&P algorithm.     

R&D project scheduling when activities may fail

An R&D project typically consists of several stages. Due to technological risks, the project may have to be terminated before completion, each stage having a specific likelihood of success. In the project planning and scheduling literature, this technological uncertainty has typically been ignored and project plans are developed only for scenarios in which the project succeeds. In this paper we examine how to schedule projects in order to maximize their expected net present value when the project activities have a probability of failure and when an activity's failure leads to overall project termination. We formulate the problem, show that it is NP-hard, develop a branch-and-bound algorithm that allows us to obtain optimal solutions and provide extensive computational results. In the process, we establish a complexity result for an open problem in single-machine scheduling, namely for the discounted weighted-completion-time objective with general precedence constraints.     

MRP lot sizing with variable production/purchasing costs: formulation and solution

The research on lot sizing is extensive; however, no author in the literature reviewed to date provides an optimal solution algorithm to a prevalent problem which is found in manufacturing. A multi-level, general product-structure, variable-cost model is presented which follows the procedure of a closed-loop material requirements planning (MRP) system, and incorporates many conditions that production and material managers find in practice. A branch and bound (B&B7) algorithm is developed. The efficiency of B&B is derived from effective lower bounds and solution procedures which are determined on the basis of the space-time structure of the MRP lot-sizing problem and its non-convex total-cost function. This path-dependent lower bound is computationally efficient and guarantees an optimal solution. The B&B algorithm is tested on problems and compared to heuristics in the literature.     

Bad-scenario-set robust scheduling for a job shop to hedge against processing time uncertainty

This paper proposed two robust scheduling formulations in real manufacturing systems based on the concept of bad scenario set to hedge against processing time uncertainty, which is described by discrete scenarios. Two proposed robust scheduling formulations are applied to an uncertain job-shop scheduling problem with the makespan as the performance criterion. The united-scenario neighbourhood (UN) structure is constructed based on bad scenario set for the scenario job-shop scheduling problem. A tabu search (TS) algorithm with the UN structure is developed to solve the proposed robust scheduling problem. An extensive experiment was conducted. The computational results show that the first robust scheduling formulation could be preferred to the second one for the discussed problem. It is also verified that the obtained robust solutions could hedge against the processing time uncertainty through decreasing the number of bad scenarios and the degree of performance degradation on bad scenarios. Moreover, the computational results demonstrate that the developed TS algorithm is competitive for the proposed robust scheduling formulations.