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Bearings and exhaust valves are components of vital importance to the engine of a motor‐car. They operate under conditions which favour corrosive attack, and the problem of mitigating this attack is made more difficult, in the case of the bearings, by the tendency towards increasing engine power and, in the case of the exhaust valves, by the use of petrols containing tetra‐ethyl lead. Previous articles by Mr. Michalewicz in this series have appeared in CORROSION TECHNOLOGY in the issues of August 1954 (‘Corrosive Wear of Piston Rings and Cylinders’), November 1954 (‘The Cooling System’) and March 1955 (‘The Bodywork’).

This chapter discusses a collaborative strategy for noncompetitive small- and medium-sized enterprises (SME's) aiming to reduce their logistics costs by means of a joint replenishment of multiple items. The proposed approach is an extension of the classical joint replenishment problem, named as a Stochastic Collaborative Joint Replenishment problem (S-CJRP) because it considers stochastic demand, warehouse and transport capacity constraints, and multiple buyers and vendors. Operating this method implies three main challenges: (1) determining the frequency with which each buyer should replenish the products; (2) allocating investments and benefits between partnering buyers; and (3) deciding whether to coordinate the supply chain internally or outsource its coordination. The S-CJRP is solved through a heuristic approach, which deals with uses of the Shapley Value Function to allocate the investments and benefits, and it explores the coordination through several simulation scenarios, all of which exhibit prospective cost reductions in inventory management. Preliminary results show that third-party logistics providers could be a valuable resource in coordinating SMEs along a supply chain.

The finishes used on the bodywork of motor‐cars must provide adequate protection against corrosion, effective over a period of many years, under various climatic, atmospheric and operational conditions. Lasting, attractive appearance is also an important requirement of these finishes. The treatment of steel prior to painting, and the electroplated bright finishes are briefly described. The first article in this series, ‘Corrosive Wear of Piston Rings and Cylinders,’ appeared last August and the second, ‘The Cooling System,’ appeared in the November issue of Corrosion Technology. A further article will deal with ‘Corrosion of Components, etc’.

Many forms of corrosion can be present in the cooling system of a motor‐car, in which a hot coolant, often unsuitable from the point of view of corrosion, flows through a number of parts made of different metals. Attack is aggravated by such conditions as exhaust gas or air leakage into the system. Corrosion can be minimised by the use of effective inhibitors. The first article in this series, ‘Corrosive Wear of Piston Rings and Cylinders,’ appeared in the August issue of CORROSION TECHNOLOGY.

The purpose of this paper and its companion (Part II: multi‐silo supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In tis part, the paper aims to devote attention to single silo and two‐silo supply chains. It also aims to discuss three models. The first model is based on the winebottling real‐world system and exposes complexities of a single operational component of the supply chain. The second model extends it to two components: production and distribution. The last system is a real‐world implementation of the two‐component supply chain.

Evolutionary approach is proposed for a single component problem. The two‐component experimental supply chain is addressed by the algorithm based on cooperative coevolution. The final problem of steel sheet production is tackled with the evolutionary algorithm.

The proposed systems produce solutions better than solutions proposed by human experts and in a much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The proposed systems are in the production use.

Corrosion is one of the chief factors contributing to the wear of the cylinders and piston rings of an automotive engine. It is caused by acids formed in the combustion process, particularly those resulting from the combination of certain gases with water condensed on the cylinder walls in low‐temperature operation. In this article the author discusses the mechanism of corrosive cylinder wear, together with the factors influencing it, and reviews some of the methods of mitigating the attack.

The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems.

For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping.

The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation.

The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems.

The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature.

It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.

The hot dip galvanising process is a well‐established means of rust‐proofing iron and steel. In Western Europe 78,000 tons of zinc are used every year for galvanising sheet and strip. In the U.S.A., where galvanised sheet is widely used, the figure is 132,000 tons p.a. Galvanisers on both sides of the Atlantic can learn much from each other and last month about 200 experts from Britain, the Continent and America met at Oxford for an International Conference on Hot Dip Galvanising under the auspices of the Zinc Development Association. The subjects discussed ranged from the galvanising of wire to an incentive wages scheme for galvanising workers. Here are summaries of some outstanding papers.

In the paper we design a super genetic hybrid algorithm (SuperGHA), an integrated optimization system for simultaneous parametric search and nonlinear optimization. The parametric search machine is implemented as a genetic superstructure, producing tentative parameter vectors that control the ultimate optimization process. The family of parameter vectors evolves through ordinary genetic operators aimed at producing the best possible parameterization for the underlying optimization problem. In comparison to traditional genetic algorithms, the integrated superstructure involves a twofold ordering of the population of parameter vectors. The first sorting key is provided by the objective function of the optimization problem at issue. The second key is given by the total mesh time absorbed by the parametric setting. In consequence, SuperGHA is geared at solving an optimization problem, using the best feasible parameterization in terms of optimality and time absorbance. The algorithm combines features from classical nonlinear optimization methodology and evolutionary computation utilizing a powerful accelerator technique. The constrained problem can be cast into multiple representations, supporting the integration of different mathematical programming environments. We show by extensive Monte Carlo simulations that SuperGHA extracts suitable parameter vectors for fast solution of complicated nonlinear programming problems.

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.

First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.

Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.

This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.