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Eversure Textiles was founded in 1965 and moved to its present location in 1976. In 1986 the company became a wholly‐owned subsidiary of Sirdar Plc, which is based in Wakefield. Eversure is one of the leading manufacturers of window dressings in the UK and its marketplace is very varied including independent retailers, mail order, high street multiples, export, and as such, this variety places great demand on the business, specifically on delivery performance. The aim of the paper is to describe the introduction of team working in Eversure and to discuss the advantages derived by the company from this decision.

The reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment.

The schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work.

The study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism.

The proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.

Evaluating housing projects is an essential task to determine the effectiveness of these projects and to provide a useful feedback to the projects’ initiators ‐ be it the public or the private sector. The evaluation could be on a number of different bases. Reports the results of research work aiming to compare the quality and effectiveness of three housing projects initiated by the Yemeni government in the capital ‐ Sana’a. The basis chosen for evaluating the public low‐income housing schemes is the housing quality measurement technique. This technique is used for measuring quality under widely different housing standards. It is a way of assessing the environmental quality and hence evaluating the success or failure of a particular housing project. Satisfaction of residents has also been evaluated and reported in an earlier paper (Al‐Abed and Mustapha, 1996).

The Equal Pay Act 1970 (which came into operation on 29 December 1975) provides for an “equality clause” to be written into all contracts of employment. S.1(2) (a) of the 1970 Act (which has been amended by the Sex Discrimination Act 1975) provides:

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation schemes. However their efficiency is strongly dependent on the value of the penalty parameter and usually results in a poor rate of convergence to the exact solution. In this paper we propose a new method to perform the augmentations. It is based on estimated values of the augmented Lagrangians. At each augmentation the converged state is used to extract some data. Such information updates a database used for the Lagrangian estimation. The prediction is primarily based on the evolution of the constraint violation with respect to the evolution of the contact forces. The proposed method is characterised by a noticeable efficiency in detecting nearly exact contact forces, and by superlinear convergence for the subsequent minimisation of the residual of constraints. Remarkably, the method is relatively insensitive to the penalty parameter. This allows a solution which fulfils the constraints very rapidly, even when using penalty values close to zero.

Multi-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.

This paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.

Allowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.

To the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

Control of quality in the mail order and promotional distribution systems raises problems not easily handled by established techniques. Application of normal, batch based, charting and decision methods to the low levels of defects in continuing and variable flows of items is difficult. Cu‐sum methods are shown to have many advantages and offer both sensitivity and economy of operation. This article describes the development of a system for control of quality in a premium distribution organisation based on 100 per cent inspection by the customers and cu‐sum analysis of the returns

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 two‐armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information. The purpose of this paper is to report research into a completely new family of solution schemes for the TABB problem: the Bayesian learning automaton (BLA) family.

Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. BLA avoids the problem of computational intractability by not explicitly performing the Bayesian computations. Rather, it is based upon merely counting rewards/penalties, combined with random sampling from a pair of twin Beta distributions. This is intuitively appealing since the Bayesian conjugate prior for a binomial parameter is the Beta distribution.

BLA is to be proven instantaneously self‐correcting, and it converges to only pulling the optimal arm with probability as close to unity as desired. Extensive experiments demonstrate that the BLA does not rely on external learning speed/accuracy control. It also outperforms established non‐Bayesian top performers for the TABB problem. Finally, the BLA provides superior performance in a distributed application, namely, the Goore game (GG).

The value of this paper is threefold. First of all, the reported BLA takes advantage of the Bayesian perspective for tackling TABBs, yet avoids the computational complexity inherent in Bayesian approaches. Second, the improved performance offered by the BLA opens up for increased accuracy in a number of TABB‐related applications, such as the GG. Third, the reported results form the basis for a new avenue of research – even for cases when the reward/penalty distribution is not Bernoulli distributed. Indeed, the paper advocates the use of a Bayesian methodology, used in conjunction with the corresponding appropriate conjugate prior.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.