SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization

Grasas, Alex; Juan, Ángel A.; Lourenço, Helena

doi:10.1057/jos.2014.25

Cited by 57 publications

(35 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the Job-Shop Scheduling Problem with failure-risk penalties. Other relevant extension of the present work is to consider stochastic processing times in the actual non-smooth PFSP and solve this stochastic version throughout a simheuristic approach as the ones proposed in Juan et al (2015) and Grasas et al (2014). Finally, as discussed above, it can also be interesting to explore multi-objective approaches in scenarios where the makespan and the failure-risk costs cannot be measured in the same units.…”

Section: Discussionmentioning

confidence: 98%

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

Ferrer

Guimarans

Ramalhinho

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

This paper analyzes a realistic variant of the permutation flow-shop problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.

show abstract

Section: Discussionmentioning

confidence: 98%

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

Ferrer

Guimarans

Ramalhinho

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Also, according to the numerical experiments performed, our approach is able to generate near-optimal solutions for instances up to 200 arcs in just a few seconds or minutes (depending on the specific instance). Several research lines remain open at this stage, among others: (a) explore other ways of integrating a simulation stage inside a metaheuristic algorithm as suggested in Grasas et al (2016) and Juan et al (2015); (b) analyse how parallel executions of this algorithm-each of them running with a different simulation seed -can speed up clock times necessary to obtain 'high-quality' solutions; (c) enrich the ARP model even further by including also stochastic costs because of random travelling times; and (d) developing similar simheuristics for other combinatorial optimization problems that have traditionally assumed deterministic inputs even when uncertainty is present in most real-life situations.…”

Section: Discussionmentioning

confidence: 99%

A simheuristic algorithm for solving the arc routing problem with stochastic demands

González-Martín

Juan

Riera

et al. 2017

Journal of Simulation

View full text Add to dashboard Cite

This paper proposes a simheuristic algorithm for solving the Arc-Routing Problem with Stochastic Demands. Our approach combines Monte Carlo Simulation (MCS) with the RandSHARP metaheuristic, which was originally designed for solving the Capacitated Arc-Routing Problem with deterministic demands (CARP). The RandSHARP metaheuristic is a biased-randomized version of a savings-based heuristic for the CARP, which allows it to obtain competitive results for this problem in low computational times. The RandSHARP is then combined with MCS to cope with the stochastic variant of the problem in a natural and efficient way. Our work is based on the use of a safety stock during the route-design stage. This safety stock can then be used during the delivery stage to satisfy unexpected demands. A reliability index is also defined to evaluate the robustness of each solution with respect to possible route failures caused by random demands. Some numerical experiments contribute to validate our approach and to illustrate its potential benefits.

show abstract

“…Moreover, a reasonable approach in the optimization of SCN is selecting the control parameters of each entity through simulation runs. Generating more realistic solutions through the consideration of uncertainty belongs to the former function, which is addressed in [122][123][124]. These authors gained the advantage of using simulation within an algorithmic framework to produce more reliable solutions out of the optimization part.…”

Section: Simulation-based Optimizationmentioning

confidence: 99%

The New Generation of Operations Research Methods in Supply Chain Optimization: A Review

Pourhejazy

Kwon

2016

Sustainability

View full text Add to dashboard Cite

Abstract:The possibilities of applying Operations Research (O.R.) techniques in the design of real-world systems are vast. The optimization and design of the supply chain network (SCN) is one of the relevant topics that has directed the attention of many scholars. Sound decisions in this regard, including the proper selection of the facility's location, transportation modes and routes and inventory management policies, can noticeably improve the systems performance. Over 380 articles published between 2005 and 2016 in the ISI/Web of Science database have applied advanced O.R. techniques in SCN optimization studies. This paper offers a systematic review of these published contributions by focusing on two categories of O.R. approaches most recently applied for the design of SC systems: integrated mathematical modeling and simulation-optimization (S-O) frameworks. A taxonomy analysis of the mentioned approaches is presented based on the supply chain elements. A bibliometric analysis is also conducted to provide technical insights into the possible gaps in the field. Moreover, the relevant studies on SC sustainability are highlighted. The research results are supportive of the S-O frameworks as either an alternative approach or an effective solution method for the integrated problems. The research outcomes can provide researchers in the field with useful details of the integrated problems and S-O frameworks as the most recent O.R. methodologies in the field of SC optimization.

show abstract

SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization

Cited by 57 publications

References 71 publications

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

A simheuristic algorithm for solving the arc routing problem with stochastic demands

The New Generation of Operations Research Methods in Supply Chain Optimization: A Review

Contact Info

Product

Resources

About