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Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

Discusses the use of optimizing techniques such as linear programming (LP) in managerial decision making. Argues that while LP has always offered a powerful tool for solving allocation type problems, the technique was rarely used except in large organizations. Lack of use has often been due, not to the limitations or assumptions associated with the technique, but to the need to use expensive and unintuitive application software which required extensive knowledge and training. Argues that new advances in spreadsheet software now offer the decision‐maker a powerful yet easy‐to‐use way of applying linear programming which can greatly enhance decision‐making effectiveness. Uses a simple example to contrast different approaches to solving allocation type problems and concludes that linear programming should now become an essential tool within the decision‐makers’ portfolio of problem‐solving techniques.

This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP models may have more implications – and broader ones – in industry practice than deterministic linear programming (DLP) models do.

This paper introduces both DLP and SLP methods. In addition, continuous and discrete SLP models are explained. Applications are demonstrated using practical examples and simulations.

This research work extends the current knowledge of SLP, especially concerning managerial accounting issues. Through numerical examples, SLP demonstrates its great ability of hedging against all scenarios.

This study serves as an addition to building a cumulative tradition of research on SLP in managerial accounting. Only a few SLP studies in managerial accounting have focused on the development of such an instrument. Thus, the measurement scales in this research can be used as the starting point for further refining the instrument of optimization in managerial accounting.

The traditional network scheduling methods such as the Critical Path Method (CPM), Programme Evaluation Review Technique (PERT), and bar charting are typically not effective for the planning of linear construction projects. Linear scheduling methods, on the other hand, model the progress of repetitive activities in sloping lines and are more effective for linear modelling and analysis. Nonetheless, their use in the construction industry has so far been very limited. Among other reasons for this is the unfamiliarity of construction personnel with these techniques, which plays a major role in hampering their application. This paper introduces a graphically based approach to assist in the linear programming (LP) modelling of linear scheduling analysis. The Planning & Optimization for Linear Operations (POLO) system provides a graphic LP modelling environment in which model formulation can be easily accomplished in a graphic and interactive fashion. Thus, the application of linear scheduling methods can be facilitated. The Isle of Palms Connector Bridge project in Mount Pleasant, South Carolina is used to demonstrate the use of the system.

Managers often face the problem of making project decisions that involve a large number of interrelated activities – the planning and scheduling of which is project management. These problems often arise in areas such as product development, production planning and control and setting up of production facilities. One important aspect of project management is activity crashing, i.e. reducing activity time by adding more resources such as workers, overtime and so on. It is important to decide the optimal crash plan to complete the project within the desired time period. The usual practice is to use a linear programming (LP) approach. Finds that the solution obtained from LP is not always effective and shows that a goal‐programming approach can be used efficiently in such decision‐making problems.

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.

The advantages of Goal Programming (GP) over linear programming (LP) are discussed in the context of the healthcare industry. Decision makers must give considerable attention to the formulation of a GP model. However, long‐ and short‐term solutions must not be confused. Solutions also require implementations, which may be impractical or difficult. The full utilisation of facilities is recommended in an attempt to reduce unit costs and increase output.

One strategy that has been used extensively to cut operation costs is downsizing, a planned reduction in a firm’s work force. Downsizing must be based on a thorough analysis of the firm’s prioritized opportunities and their limited economic resources to achieve them. Some operations research techniques have appeared in the literature as practical aids in downsizing methodology. The purpose and significance of the research in this paper is to: provide the first demonstration of how a prioritized multi‐objective programming‐oriented methodology (i.e. goal programming) can be used for planning the downsizing of production/operations resources; and demonstrate a new methodological approach that can be used to determine previously hidden goals in a manufacturing linear programming model of the downsizing problem. Based on a problem reported in the literature, this paper will illustrate how an optimal allocation of production resources can be achieved while providing useful information in which to ensure other prioritized goals and their economic tradeoffs are considered in the downsizing analysis.

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.