Differential evolution for solving multi-mode resource-constrained project scheduling problems

Damak, N.; Jarboui, Bassem; Siarry, Patrick; Loukil, Taïcir

doi:10.1016/j.cor.2008.11.010

Cited by 137 publications

(65 citation statements)

References 25 publications

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“…For instance, Pan et al [40] presented a discrete DE algorithm to solve the permutation flow-shop scheduling problem with total flow-time criterion. Damak et al [22] recommended a DE algorithm to solve the resource-constrained project scheduling problem with multiple execution modes for each activity and minimisation of the makespan. Wang et al [53] presented a new hybrid discrete DE algorithm to solve the blocking flow-shop scheduling problems in order to minimise the maximum completion time.…”

Section: Meta-heuristic Solution Methodsmentioning

confidence: 99%

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A Differential Evolution Algorithm Developed for a Nurse Scheduling Problem

Shahnazari-Shahrezaei

Tavakkoli–Moghaddam

Azarkish

et al. 2012

sajie

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Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers' objectives and nurses' preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics -a differential evolution algorithm (DE) and a greedy randomised adaptive search procedure (GRASP) -to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP. OPSOMMINGVerpleegsterskedulering is 'n mannekragtoedelingsprobleem wat deur regverdige skofskedules beide hospitaalbestuurders se teikens en verpleegpersoneel se voorkeure bevredig. Dié artikel handel oor 'n verpleegsterskeduleringprobleem wat gebaseer is op 'n werklike gevallestudie en stel twee metaheuristieke voor om die probleem op te los -'n differensiaal evolusionêre metode en 'n gulsige, ewekansige, aanpasbare soekprosedure. Om die doeltreffendheid van die voorgestelde algoritmes te ondersoek word twee probleme opgelos. Verder word vergelykende maatstawwe gebruik om die betroubaarheid van die voorgestelde algoritmes te ondersoek. Die berekende resultate in die artikel toon dat die differensiaal evolusionêre algoritme beter vaar as die gulsige, ewekansige, aanpasbare soekprosedure.

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Section: Meta-heuristic Solution Methodsmentioning

confidence: 99%

“…The constraints (19) to (22) ensure that: = 1, if nurse i is assigned to work three successive night shifts on days k-2, k-1, and k; otherwise 0.…”

Section: Hard Constraintsmentioning

confidence: 99%

A Differential Evolution Algorithm Developed for a Nurse Scheduling Problem

Shahnazari-Shahrezaei

Tavakkoli–Moghaddam

Azarkish

et al. 2012

sajie

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“…Jarboui et al [18] also presented a PSO approach but combinatorial, in which he adopted a unique local search procedure with a unique solution encoding and representation. Damak et al [87] proposed a DE approach to solve the MRCPSP, and analyzed the effect of population size on the solution quality. The Van Peteghem and Vanhoucke [88] approach used Artificial Immune System (proposed by De Castro and Timmis [89]) to solve the MRCPSP.…”

Section: Meta-heuristicsmentioning

confidence: 99%

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

Chen¹,

Liang

Padilla

2014

Entropy

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Abstract:Projects are an important part of our activities and regardless of their magnitude, scheduling is at the very core of every project. In an ideal world makespan minimization, which is the most commonly sought objective, would give us an advantage. However, every time we execute a project we have to deal with uncertainty; part of it coming from known sources and part remaining unknown until it affects us. For this reason, it is much more practical to focus on making our schedules robust, capable of handling uncertainty, and even to determine a range in which the project could be completed. In this paper we focus on an approach to determine such a range for the Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP), a widely researched, NP-complete problem, but without adding any subjective considerations to its estimation. We do this by using a concept well known in the domain of thermodynamics, entropy and a three-stage approach. First we use Artificial Bee Colony (ABC)-an effective and powerful meta-heuristic-to determine a schedule with minimized makespan which serves as a lower bound. The second stage defines buffer times and creates an upper bound makespan using an entropy function, with the advantage over other methods that it only considers elements which are inherent to the schedule itself and does not introduce any subjectivity to the buffer time generation. In the last stage, we use the ABC algorithm with an objective function that seeks to maximize robustness while staying within the makespan boundaries defined OPEN ACCESSEntropy 2014, 16 5033 previously and in some cases even below the lower boundary. We evaluate our approach with two different benchmarks sets: when using the PSPLIB for the MRCPSP benchmark set, the computational results indicate that it is possible to generate robust schedules which generally result in an increase of less than 10% of the best known solutions while increasing the robustness in at least 20% for practically every benchmark set. And, in an attempt to solve larger instances with 50 or 100 activities, we also used the MRCPSP/max benchmark sets, where the increase of the makespan is approximately 35% with respect to the best known solutions at the same time as with a 20% increase in robustness.

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“…The hybrid approaches have attracted most of the research attention. [8][9][10] However, for workingsteps sequencing with MTCM, only a few works have been carried out. N-C Chiu et al 1 proposed a genetic algorithm for scheduling a machining sequence, but the turning and drilling were not considered as a synchronous machining process.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid discrete differential evolution algorithm for workingsteps sequencing in multi-channel turn-milling complex machining

Shi

Qiao

Zhu

2015

Advances in Mechanical Engineering

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To address the problem of workingsteps sequencing in multi-channel turn-milling complex machining, a novel hybrid optimization algorithm titled hybrid discrete differential evolution has been proposed, which improves the algorithm to achieve better results for the workingsteps sequencing problem. The main thrust of this article is twofold: (1) to analyze the characteristics of synchronous machining in multi-channel turn-milling complex machining and their constraints, and to propose a zero-wait micro-resource allocation strategy; (2) to develop a hybrid discrete differential evolution algorithm for process planning in multi-channel turn-milling complex machining, and to describe in detail about the operation of crossover, mutation, and selection. Then, analysis of variance has been used to investigate the contribution and effects of variables (parameters) in hybrid discrete differential evolution, so the optimal parameters can be obtained. Finally, a comparison of the performance and efficiency between the proposed algorithm and the classical differential evolution algorithm is made. And experimental results show that the hybrid discrete differential evolution algorithm is good at solving the sequencing problem, and the results approximate the optimum expectation.

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Differential evolution for solving multi-mode resource-constrained project scheduling problems

Cited by 137 publications

References 25 publications

A Differential Evolution Algorithm Developed for a Nurse Scheduling Problem

A Differential Evolution Algorithm Developed for a Nurse Scheduling Problem

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

A novel hybrid discrete differential evolution algorithm for workingsteps sequencing in multi-channel turn-milling complex machining

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