An effective heuristic for project scheduling with resource availability cost

[EN] 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. (C) 2016 Published by Elsevier B.V.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Key Research & Development program in Jiangsu Province (No. BE2015728) and the Collaborative Innovation Center of Wireless Communications Technology. Rubén Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project SCHEYARD - Optimization of Scheduling Problems in Container Yards with reference DPI2015-65895-R co-financed with FEDER funds.Zhu, X.; Ruiz García, R.; Li, S.; Li, X. (2017). An effective heuristic for project scheduling with resource availability cost. European Journal of Operational Research. 257(3):746-762. https://doi.org/10.1016/j.ejor.2016.08.049S746762257

Railway scheduling reduces the expected project makespan.

The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (1997), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling.Railway scheduling; Roadrunner scheduling; Feeding buffer; Priority list; Resource availability;

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

Optimization Algorithms in Project Scheduling

Scheduling, or planning in a general perspective, is the backbone of project management; thus, the successful implementation of project scheduling is a key factor to projects’ success. Due to its complexity and challenging nature, scheduling has become one of the most famous research topics within the operational research context, and it has been widely researched in practical applications within various industries, especially manufacturing, construction, and computer engineering. Accordingly, the literature is rich with many implementations of different optimization algorithms and their extensions within the project scheduling problem (PSP) analysis field. This study is intended to exhibit the general modelling of the PSP, and to survey the implementations of various optimization algorithms adopted for solving the different types of the PSP

ROBUST RESOURCE INVESTMENT PROBLEM WITH TIME-DEPENDENT RESOURCE COST AND TARDINESS PENALTY

The Resource Investment Problem (RIP) is a variant of the well-known Resource Constraint Project Scheduling Problem (RCPSP) that requires finding the optimal resource allocation, given a preset completion date, with the objective of minimizing the total cost. The practical relevance of RIP is very obvious; since the decision maker (the project manager for example) wants to know what resources are required to achieve the targeted project completion date. RIP helps to decide the amount of investment in resources that yield the optimal solution, in addition to the optimal tradeoff between completion time and resource investment. In practice, most of the projects are associated with due dates beyond which a tardiness penalty may be applied. To avoid the tardiness penalty, project managers sometimes decide to add more resources, thereby increasing resource investment cost, to the project to finish earlier. In this thesis the (RIP) has been extended to consider time-depended resource cost instead of time-independent resource cost in the classical RIP. The problem was named Resource Investment Problem with Time-Dependent Resource Cost and Tardiness Penalty, abbreviated as (RIP-TDRC). A mathematical model was introduced to simultaneously find the optimal resource assignment and activity staring times. The objective is to minimize the sum of the resources and tardiness cost. Two versions of this problem are addressed in this thesis: the deterministic version of RIP-TDRC and the stochastic version. For the latter, it is assumed that the activity durations are subject to many uncertainties such as (bad weather conditions, material shortage, employee’s absences …etc.). To solve this problem, a simulation-optimization based algorithm is proposed. This algorithm solves the deterministic problem version iteratively through all possible project completion times and simulates the project considering the uncertainties to find the optimal solution. The performance of the proposed algorithm and the effect of some problem parameters on the solution are assessed through computational experiments. The experiments revealed the usefulness of the algorithm in finding relatively robust solution for small problem sizes

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

An examination of heuristics for the shelf space allocation problem.

Wong, Mei Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 115-120).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Our Contributions --- p.4Chapter 1.3 --- Framework of Shelf Space Allocation Problem --- p.4Chapter 1.4 --- Organization --- p.6Chapter 2. --- Literature Review --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Commercial Approaches --- p.7Chapter 2.3 --- Experimental Approaches --- p.8Chapter 2.4 --- Optimization Approaches --- p.11Chapter 2.4.1 --- Exact Approaches --- p.11Chapter 2.4.2 --- Heuristics Approaches --- p.16Chapter 2.5 --- Summary --- p.19Chapter 3. --- Overview of Shelf Space Allocation Problem --- p.21Chapter 3.1 --- Introduction --- p.21Chapter 3.2 --- Problem description --- p.22Chapter 3.2.1 --- Mathematical Model --- p.24Chapter 3.2.1.1 --- Notations --- p.25Chapter 3.2.1.2 --- Model --- p.25Chapter 3.2.1.3 --- Assumption --- p.26Chapter 3.2.1.4 --- Notations of final model --- p.27Chapter 3.2.1.5 --- Final model --- p.27Chapter 3.3 --- Original Heuristic --- p.28Chapter 3.3.1 --- Yang (2001) Method --- p.28Chapter 3.3.2 --- Remarks on Original Heuristic --- p.29Chapter 3.4 --- Original Heuristic with Yang's Adjustment --- p.30Chapter 3.4.1 --- Remarks on Yang's Adjustment --- p.32Chapter 3.5 --- New Neighborhood Movements --- p.33Chapter 3.5.1 --- New Adjustment Phase --- p.33Chapter 3.6 --- Network Flow Model --- p.35Chapter 3.6.1 --- ULSSAP --- p.35Chapter 3.6.2 --- Transforming shelf space allocation problem (SSAP) --- p.38Chapter 3.7 --- Tabu Search --- p.41Chapter 3.7.1 --- Tabu Search Algorithm --- p.42Chapter 3.7.1.1 --- Neighborhood search moves --- p.42Chapter 3.7.1.2 --- Candidate list strategy --- p.45Chapter 3.7.1.3 --- Tabu list --- p.46Chapter 3.7.1.4 --- Aspiration criteria.........................................: --- p.47Chapter 3.7.1.5 --- Intensification and Diversification --- p.48Chapter 3.7.1.6 --- Stopping criterion --- p.49Chapter 3.7.1.7 --- Probabilistic choice --- p.50Chapter 3.7.2 --- General Process of Tabu Search --- p.51Chapter 3.7.3 --- Application of Tabu Search to SSAP --- p.54Chapter 3.7.4 --- Analysis of Tabu Search --- p.58Chapter 4. --- Tabu Search with Path Relinking --- p.60Chapter 4.1 --- Introduction --- p.60Chapter 4.2 --- Foundations of path relinking --- p.62Chapter 4.3 --- Path Relinking Template --- p.65Chapter 4.4 --- Identification of Reference set --- p.69Chapter 4.5 --- Choosing initial and guiding solution --- p.73Chapter 4.6 --- Neighborhood structure --- p.74Chapter 4.7 --- Moving along paths --- p.81Chapter 4.8 --- Application of Tabu Search with Path Relinking --- p.87Chapter 4.9 --- Conclusion --- p.90Chapter 5. --- Computational Studies --- p.92Chapter 5.1 --- Introduction --- p.92Chapter 5.2 --- General Parameter Setting --- p.92Chapter 5.3 --- Parameter values for Tabu search --- p.94Chapter 5.4 --- Sensitivity test for Tabu search with Path Relinking --- p.95Chapter 5.4.1 --- Reference Set Strategies and Initial and Guiding Solution Criteria --- p.96Chapter 5.4.2 --- Frequency of Path Relinking --- p.99Chapter 5.4.3 --- Size of reference set --- p.101Chapter 5.4.4 --- Comparison with Tabu Search --- p.102Chapter 5.5 --- Comparison with other heuristics --- p.105Chapter 5.6 --- Conclusion --- p.109Chapter 6. --- Conclusion --- p.111Chapter 6.1 --- Summary of achievements --- p.112Chapter 6.2 --- Future Works --- p.113Bibliography --- p.11

A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags

[EN] The efficient use of resources is a key factor to minimize the cost while meeting time deadlines and quality requirements; this is especially important in construction projects where field operations take fluctuations of resources unproductive and costly. Resource Leveling Problems (RLP) aim to sequence the construction activities that maximize the resource consumption efficiency over time, minimizing the variability. Exact algorithms for the RLP have been proposed throughout the years to offer optimal solutions; however, these problems require a vast computational capability ( combinatorial explosion ) that makes them unpractical. Therefore, alternative heuristic and metaheuristic algorithms have been suggested in the literature to find local optimal solutions, using different libraries to benchmark optimal values; for example, the Project Scheduling Problem LIBrary for minimal lags is still open to be solved to optimality for RLP. 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