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Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cash‐flow liquidity in forecasting. However, a great challenge for contracting firm to manage his multiproject cash flow when large and multiple construction projects are involved (manipulate large amount of resources, e.g. labour, plant, material, cost, etc.). In such cases, the complexity of the problem, hence the constraints involved, renders most existing regular optimization techniques computationally intractable within reasonable time frames. This limit inhibits the ability of contracting firms to complete construction projects at maximum efficiency through efficient utilization of resources among projects. Recently, artificial neural networks have demonstrated its strength in solving many optimization problems efficiently. In this regard a novel recurrent‐neural‐network model that integrates multi‐objective linear programming and neural network (MOLPNN) techniques has been developed. The model was applied to a relatively large contracting company running 10 projects concurrently in Hong Kong. The case study verified the feasibility and applicability of the MOLPNN to the defined problem. A comparison undertaken of two optimal schedules (i.e. risk‐avoiding scheme A and risk‐seeking scheme B) of cash flow based on the decision maker's preference is described in this paper.

Although the modern portfolio theory (MPT) asset allocation framework can be adopted to enable decision making for international and direct real estate investing, and that many institutional investors adopt it to support their decision making, this framework can be enhanced to capture the multi‐causal factors influencing international and direct real estate investing. The purpose of this paper is to explain how a fuzzy decision‐making approach is a more intuitive, yet rigorous alternative in this regard.

This paper is concerned with the model formation and estimation of a unique fuzzy tactical asset allocation (FTAA), which in turn comprises the FTAA flexible programming model and the FTAA robust programming model.

Both these FTAA models enhance the classical, Markowitz MPT portfolio theory on asset allocation through making it more intuitively appropriate for decision making in international and direct real estate investing.

These two FTAA models achieve the benefits of intuitively greater risk diversification by city or real estate sector and enable effective risk management. These two short‐run fuzzy models would be accepted and more such models would emerge as an effective extension of quadratic programming optimization, as more computable software programs of this kind are widespread.

Fuzzy approaches to asset allocation in the short run, are limited by some drawbacks. Fuzzy models possess the common feature of converting the equality function under quadratic programming optimization into inequality functions. Such inequality optimization replaces the point solution of the MPT TAA optimization problem, obtained through the rigid intersection of all functions, via a generalized or intuitive answer over a defined space of alternatives. The product of the fuzzy process with fuzzy inputs, in the form of fuzzy outcome is in actual fact a more natural and intuitive approach to asset optimization.

To discuss a new parallel algorithmic platform (minlp_machine) for complex mixed‐integer non‐linear programming (MINLP) problems.

The platform combines features from classical non‐linear optimization methodology with novel innovations in computational techniques. The system constructs discrete search zones around noninteger discrete‐valued variables at local solutions, which simplifies the local optimization problems and reduces the search process significantly. In complicated problems fast feasibility restoration may be achieved through concentrated Hessians. The system is programmed in strict ANSI C and can be run either stand alone or as a support library for other programs. File I/O is designed to recognize possible usage in both single and parallel processor environments. The system has been tested on Alpha, Sun and Linux mainframes and parallel IBM and Cray XT4 supercomputer environments. The constrained problem can, for example, be solved through a sequence of first order Taylor approximations of the non‐linear constraints and feasibility restoration utilizing Hessian information of the Lagrangian of the MINLP problem, or by invoking a nonlinear solver like SQP directly in the branch and bound tree. minlp_machine( ) has been tested as a support library to genetic hybrid algorithm (GHA). The GHA(minlp_machine) platform can be used to accelerate the performance of any linear or non‐linear node solver. The paper introduces a novel multicomputer partitioning of the discrete search space of genuine MINLP‐problems.

The system is successfully tested on a small sample of representative MINLP problems. The paper demonstrates that – through concurrent nonlinear branch and bound search – minlp_machine( ) outperforms some recent competing approaches with respect to the number of nodes in the branch and bound tree. Through parallel processing, the computational complexity of the local optimization problems is reduced considerably, an important aspect for practical applications.

This paper shows that binary‐valued MINLP‐problems will reduce to a vector of ordinary non‐linear programming on a suitably sized mesh. Correspondingly, INLP‐ and ILP‐problems will require no quasi‐Newton steps or simplex iterations on a compatible mesh.

A large number of research articles have appeared in the literature during the last two decades on the subject of system reliability optimisation, each with a view to providing simple, exact and efficient techniques. Here, an efficient, fast and exact technique is proposed for solving integer‐programming problems that normally arise in optimal reliability design problems. The algorithm presented is superior to any of the earlier methods available so far, being based on functional evaluations and a limited systematic search close to the boundary of resources. Thus it can quickly solve even very large system problems. It can also be effectively used with other operations research problems involving integer‐programming formulations.

For extending the common definitions and concepts of grey system theory to the optimization subject, a dual problem is proposed for the primal grey linear programming problem.

The authors discuss the solution concepts of primal and dual of grey linear programming problems without converting them to classical linear programming problems. A numerical example is provided to illustrate the theory developed.

By using arithmetic operations between interval grey numbers, the authors prove the complementary slackness theorem for grey linear programming problem and the associated dual problem.

Complementary slackness theorem for grey linear programming is first presented and proven. After that, a dual simplex method in grey environment is introduced and then some useful concepts are presented.

The multiple objective decision making problem arises when two or more non‐comparable objective functions are to be simultaneously optimised. There is a definite trend towards utilising interactive techniques for solving the multiple objective decision making problem. Interactive techniques allow the involvement of the DM throughout the decision process. In this paper we first provide a brief overview of multiple objective decision making, and then give a survey of literature dealing with interactive multiple objective decision making from 1965 to 1988.

The paper focuses on two topics, optimizing the proposed triangular tube for crashworthiness and solving a non‐linear programming problem by a “mapping” technique, which the condition of Lagrange Multiplier Theorem is violated within the feasible region. The purpose of studying optimized triangular tubes is to prepare them for redesigning vehicle bumpers. The dimension optimization of triangular tube is carried out for its thickness and lateral length, based on the accomplished shape optimization under an impact. The load uniformity is taken as the objective function, which is defined as the ratio of maximum peak force and means crushing force. Meanwhile the mean crushing force and absorbed energy are treated as constraints. Based on FEA analysis, the regression functions for load uniformity, mean crushing force, and absorbed energy are formulated by RSM. The result has shown that triangular tube possesses an optimization region, under which the better‐integrated property can be achieved to supply a more safety environment for vehicular occupants.

An appropriate space allocation among different residence types gives higher profitability and liquidity for cash flow management in real estate projects for developers. Thereby, a balance between debt and equity should be kept for capital formation in developers where high level of cost, profit and risk exists. The purpose of this paper is to provide cash flow optimization under debt and equity financing while providing an appropriate space allocation of residence types via synchronous consideration of profitability and liquidity.

A novel optimization methodology that includes project financing, optimization and experimental design modules is proposed. The first module, project financing, considers the flexibility of utilizing one or both of debt financing and equity financing when making capital. The optimization module addresses space allocation among different residence types for a construction while maximizing profitability and liquidity using two mixed-integer linear programming models in a pre-emptive manner. The experimental design module assesses the effects of decisive parameters within the methodology via multivariate analysis of variance (MANOVA).

The proposed methodology is applied to a real-life residential project in Istanbul. The optimization module yielded 42.5% profitability via the first linear programming model and 2.2% trade-off between liquidity and profitability while minimizing the payback period by the second linear programming model. Meanwhile, MANOVA results showed that profit per square meter and sale rate trends are the most prominent factors considering their significant effects on net present value and payback period.

To the best knowledge of the author, related papers focused only on profitability under equity financing. Liquidity (as an objective) and equity financing (as a financing method) have not been handled. Hence, this paper not only performs profitability and liquidity-oriented cash flow optimization under debt and equity financing but also optimizes space allocation of residences for the first time.

This paper aims to present two mathematical models to solve the Energy Management problem of a building microgrid (MG). In particular, it proposes a deterministic mixed integer linear programming (MILP) and non-linear programming (NLP) formulations. This paper focuses on the modelling process and the optimization performances for both approaches regarding optimal operation of near-zero energy buildings connected to an electric MG with a 24-h time horizon.

A general architecture of a MG is detailed, involving energy storage systems, distributed generation and a thermal reduced model of the grid-connected building. A continuous non-linear model is detailed along with linearizations for the mixed-integer liner formulation. Multi-physic, non-linear and non-convex phenomena are detailed, such as ventilation and air quality models.

Results show that both approaches are relevant for solving the energy management problem of the building MG.

Introduction and modelling of the thermal loads within the MG. The resulting linear program handles the mutli-objective trade-off between discomfort and the cost of use taking into account air quality criterion. Linearization and modelling of the ventilation system behaviour, which is generally non-linear and non-convex equality constraints, involving air quality model, heat transfer and ventilation power. Comparison of both MILP and NLP methods on a general use case provides a solution that can be interpreted for implementation.