Jiping Tao

An optimal semi-online algorithm for a single machine scheduling problem with bounded processing time

The single machine semi-online scheduling problem is considered with the assumption that the ratio of the longest processing time to the shortest one is not greater than @c. The goal is to minimize the total weighted completion time. We design a semi-online algorithm and prove that it has a competitive ratio of [emailxa0protected]^[emailxa0protected], which is also shown to be the best performance achieved by any deterministic semi-online algorithm for the problem.

Randomized Lagrangian heuristic based on Nash equilibrium for large scale single machine scheduling problem

Lagrangian relaxation method for jobshop scheduling problems has been studied in the framework of combinatorial auction. In this paper a noncooperative game model is built for the Lagrangian relaxation method, and we prove that the equivalent continuous relaxation computed from the Lagrangian dual problem provides a mixed strategy Nash equilibrium for this game model. Based on this interpretation a randomized heuristic is exploited to get feasible schedules. Numerical experiments are carried out on a large scale single machine problem.

WSPT's Competitive Performance for Minimizing the Total Weighted Flow Time: From Single to Parallel Machines

We consider the classical online scheduling problem over single and parallel machines with the objective of minimizing total weighted flow time. We employ an intuitive and systematic analysis method and show that the weighted shortest processing time (WSPT) is an optimal online algorithm with the competitive ratio of for the case of single machine, and it is ()-competitive for the case of parallel machines , where P is the ratio of the longest to the shortest processing time.

Endocrine-immune network and its application for optimization

A novel artificial immune network model (EINET) based on the regulation of endocrine system is proposed. In this EINET for optimization, several operators are employed or revised which aim at faster convergence speed and better optimal solution. Further speaking, a new operator, hormonal regulation, exerts a bidirectional regulatory mechanism inspired from endocrine system, which undergoes elimination and mutation according to hormone updating function, to increase the diversity of antibody population. And antibody learning is an evolution of individuals through learning from memory antibody in immune network. Then, a local search procedure called enzymatic reaction is utilized to facilitate the exploitation of the search space and speed up the convergence. To evaluate whether the proposed model can be directly extended to an effective algorithm for solving combinatorial optimization problem, EINET-TSP algorithm is designed. Comparative experiments are conducted using some benchmark instances from the TSPLIB, and the results compared with the existing immune network applied to combinatorial optimization problem shows that the EINET-TSP algorithm is capable of improving search performance significantly in solution quality.

A 2.5-competitive Online Algorithm for Pm|r j |Σw j C j

The classical identical machine scheduling problem with the objective of minimizing total weighted completion time is considered in the online setting where jobs arrive over time. An online algorithm is presented and is proven to be (2.5 - 1/2m)-competitive based on the idea of instances reduction.

Comments on Competitive analysis of a better on-line algorithm to minimize total completion time on a single-machine

For the single machine scheduling problem of minimizing the total completion time, Montoya Torres (J Glob Opt 27:97–103, 2003) presented a semi-online algorithm under the assumption that release dates are known in advance, and showed that it was

A novel routing scheme in OBS network with sparse wavelength conversion capabilities

{sqrt{3}}

Optimal design of multi-channel fiber Bragg grating filters based on particle swarm optimization algorithm

-competitive. However, there are flaws in the proof, and the conclusion about the competitive ratio is not correct. In this note, we show that the semi-online algorithm cannot perform better than the best non-clairvoyant online algorithm with a competitive ratio of 2.