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The purpose of this paper is to build a novel data envelopment analysis (DEA) model to evaluate the efficiencies of decision making units (DMUs).

Using the Choquet integrals as aggregating tool, the authors give a novel DEA model to evaluate the efficiencies of DMUs.

It extends DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form. At last, the authors use the numerical examples to illustrate the performance of the proposed model.

The proposed DEA model can be used to evaluate the efficiency of the DMUs with multiple interactive inputs and outputs.

This paper introduce a new DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form.

Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.

In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.

The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.

The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.

The purpose of this paper is to review maintenance practices, tools and parameters for marine mechanical systems that can be classified as plant, machinery and equipment (PME). It provides an insight for the maintenance crew on which maintenance parameters and practices are critical for a given PME systems.

The review paper characterizes the various maintenance parameters and maintenance practices used onshore and offshore for PME and identifies the possible gaps.

A variety of maintenance techniques are being used in the marine industry such as corrective maintenance, preventive maintenance and condition-based maintenance. As marine vehicles (MV) get older, the most important maintenance parameters become maintenance costs, reliability and safety. Maintenance models that have been developed in line with marine mechanical systems have been validated using a single system, whose outcome could be different if another PME system is used for validation.

There is a limited literature on MV maintenance parameters and maintenance characterization regarding mechanical systems. The maintenance practices or strategies of marine mechanical systems should be based on maintenance parameters that suit the marine industry for a given PME.

Based on the available literature, the paper provides a variety of maintenance framework, parameters and practices for marine mechanical systems. The paper further gives an insight on what maintenance parameters, strategies and platforms are given preference in the shipping industry.