Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Puchinger, Jakob; Raidl, Günther R.

doi:10.1007/11499305_5

Cited by 211 publications

(125 citation statements)

References 34 publications

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“…In [17] different strategies to combine heuristics are presented. In this case we proposed a similar scheme used in [7] (and presented in figure 1).…”

Section: Improved Simplex-genetic Methodsmentioning

confidence: 99%

An Improved Simplex-Genetic Method to Solve Hard Linear Programming Problems

Frausto–Solís

Nieto-Yáñez

2007

Computational Science – ICCS 2007

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Abstract. Linear programming (LP) is an important field of optimization. Even though, interior point methods are polynomial algorithms, many LP practical problems are solved more efficiently by the primal and dual revised simplex methods (RSM); however, RSM has a poor performance in hard LP problems (HLPP) as in the Klee-Minty Cubes problem. Among LP methods, the hybrid method known as Simplex-Genetic (SG) is very robust to solve HLPP. The objective of SG is to obtain the optimal solution of a HLPP, taking advantages from each one of the combined methods -a genetic algorithm (GA) and the classical primal RSM-. In this paper a new SG method named Improved Simplex Genetic Method (ISG) is presented. ISG combines a GA (with special genetic operators) with both primal and dual RSM. Numerical experimentation using some instances of the Klee-Minty cubes problem shows that ISG has a better performance than both RSM and SG.

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“…In [17] different strategies to combine heuristics are presented. In this case we proposed a similar scheme used in [7] (and presented in figure 1).…”

Section: Improved Simplex-genetic Methodsmentioning

confidence: 99%

An Improved Simplex-Genetic Method to Solve Hard Linear Programming Problems

Frausto–Solís

Nieto-Yáñez

2007

Computational Science – ICCS 2007

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“…Approximate methods do not guarantee optimality but they are able to find 'good quality solutions in a limited time' (Puchinger & Raidl, 2005 (Ehrgott & Gandibleux, 2000). A metaheuristic, on the other hand, is defined in (Osman & Laporte, 1996) as 'an iterative generation process which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting search space'.…”

Section: Chapter Fourmentioning

confidence: 99%

Modelling activities at a neurological rehabilitation unit

Griffiths

Williams

Wood

2013

European Journal of Operational Research

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DeclarationThis work has not previously been accepted in substance for any degree and is not concurrently being submitted in candidature for any degree. Signed:Date: Statement 1This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by giving explicit references. A bibliography is appended. Signed:Date: Statement 2I hereby give consent for my thesis, if accepted, to be available for photocopying and interlibrary loan, and for the title and summary to be made available to outside organisations. SummaryA queuing model is developed for the neurological rehabilitation unit at Rookwood Hospital in Cardiff. Arrivals at the queuing system are represented by patient referrals and service is represented by patient length of stay (typically five months). Since there are often delays to discharge, length of stay is partitioned into two parts: admission until date ready for discharge (modelled by Coxian phase-type distribution) and date ready for discharge until ultimate discharge (modelled by exponential distribution). The attributes of patients (such as age, gender, diagnosis etc) are taken into account since they affect these distributions. A computer program has been developed to solve this multi-server (21 bed) queuing system to produce steady-state probabilities and various performance measures.However, early on in the project it became apparent that the intensity of treatment received by patients has an effect on the time, from admission, until they are ready for discharge. That is, the service rates of the Coxian distribution are dependent on the amount of therapy received over time. This directly relates to the amount of treatment allocated in the weekly timetables. For the physiotherapy department, these take about eight hours to produce each week by hand. In order to ask the valuable what-if questions that relate to treatment intensity, it is therefore necessary to produce an automated scheduling program that replicates the manual assignment of therapy. The quality of timetables produced using this program was, in fact, considerably better than its alternative and so replaced the by-hand approach. Other benefits are more clinical time (since less employee input is required) and a convenient output of data and performance measures that are required for audit purposes.Once the model is constructed a number of relevant hypothetical scenarios are considered.Such as, what if delays to discharge are reduced by 50%? Also, through the scheduling program, the effect of changes to the composition of staff or therapy sessions can be evaluated, for example, what if the number of therapists is increased by one third? The effects of such measures are analysed by studying performance measures (such as throughput and occupancy) and the associated costs.

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“…To tackle the problem we propose the use of local search based [12] hybrid metaheuristics. We focus on integrative hybrid metaheuristics that incorporate an exact algorithm into a metaheuristic to solve subproblems to optimality [13]. Section 4.1 details the problem representation as well as the operators which define the neighborhood structure and Section 4.2 presents solutions based on hybrid metaheuristics.…”

Section: Heuristic Solutions To the Grid Resource Trading Problemmentioning

confidence: 99%

Scalable Grid Resource Allocation for Scientific Workflows Using Hybrid Metaheuristics

Buss

Lee

Veit

2010

Advances in Grid and Pervasive Computing

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Abstract. Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid.

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Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Cited by 211 publications

References 34 publications

An Improved Simplex-Genetic Method to Solve Hard Linear Programming Problems

An Improved Simplex-Genetic Method to Solve Hard Linear Programming Problems

Modelling activities at a neurological rehabilitation unit

Scalable Grid Resource Allocation for Scientific Workflows Using Hybrid Metaheuristics

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