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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.

This paper aims to present two-stage network models in the presence of stochastic ratio data.

Black-box, free-link and fix-link techniques are used to apply the internal relations of the two-stage network. A deterministic linear programming model is derived from a stochastic two-stage network data envelopment analysis (DEA) model by assuming that some basic stochastic elements are related to the inputs, outputs and intermediate products. The linkages between the overall process and the two subprocesses are proposed. The authors obtain the relation between the efficiency scores obtained from the stochastic two stage network DEA-ratio considering three different strategies involving black box, free-link and fix-link. The authors applied their proposed approach to 11 airlines in Iran.

In most of the scenarios, when alpha in particular takes any value between 0.1 and 0.4, three models from Charnes, Cooper, and Rhodes (1978), free-link and fix-link generate similar efficiency scores for the decision-making units (DMUs), While a relatively higher degree of variations in efficiency scores among the DMUs is generated when the alpha takes the value of 0.5. Comparing the results when the alpha takes the value of 0.1–0.4, the DMUs have the same ranking in terms of their efficiency scores.

The authors innovatively propose a deterministic linear programming model, and to the best of the authors’ knowledge, for the first time, the internal relationships of a two-stage network are analyzed by different techniques. The comparison of the results would be able to provide insights from both the policy perspective as well as the methodological perspective.

Farming in Bangladesh is confronted with various types of uncertainties, which contribute to farmers’ income volatility over the years. As a result, cereal, mainly rice, which is a less riskier crop remained dominantly planted in the current farm plan. But the return generated from rice cultivation has not been able to improve the livelihood of the poor, as rice profitability is low compared to some profitable but risky crops like jute and vegetables. To investigate the behavioral pattern of the farmers towards risk, Dhaka division, largely known as central region of Bangladesh, is selected. The prevailing farm plan of Dhaka division is compared with the efficient one at the current level of expected return in order to check whether the current farm plan is risky or otherwise. Quadratic and MOTAD as well as linear programming techniques have been employed for the analysis. The result of the study reveals that the prevailing farm plan in Dhaka division is risky compared to the efficient plan. Since the current return level is low, the study has recommended that more jute and vegetables should be planted to achieve higher remuneration.

To determine conditions under which a hotel in Barbados can benefit from the use of revenue management.

Monte Carlo simulation is used to compare a first‐come first‐served approach for accepting booking requests to a bid price approach. Comparisons are made using different assumptions about upgrading, downgrading and overbooking.

When demand intensity is high, the bid price method yields higher revenue than the first‐come first‐served method. If demand intensity is low, but some necessary resources are scarce and the hotel practises upgrading and downgrading, then the bid price approach can also lead to improved revenue. No evidence was found to suggest that overbooking or downgrading costs affect the relative performances of the two approaches if these costs are taken into consideration.

This research was conducted for one type of hotel using a particular sample period and the conclusions will not necessarily be true for other sample periods and other types of hotels.

The results show that hotels, which practise upgrading, downgrading and overbooking, should consider adopting a revenue management approach, when allocating their scarce resources among competing market segments.

Existing linear programming models of the revenue management problem are extended here to allow for upgrading and downgrading, when one resource is substituted for another in a package, and to allow for overbooking, when the hotel cannot honour a booking because of the unavailability of some resource. This formulation emphasizes the efficient allocation of all of the hotel's scarce resources.

The purpose of this paper is to explore the functionality of multistage programming approach on network supply chain structure.

The general supply chain structure is considered and the supply chain planning model is developed using a two stage programming approach. The same model is extended to cover the applicability and advantages of a multi‐stage programming approach.

A multi‐period supply chain model for new product launches under uncertain demand for supply chain network structure has been developed. The model allows simultaneous determination of optimum procurement quantity, production quantity across the different plants, transportation routes and the outsourcing cost in case of shortages. The proposed multi‐stage model is compared with the standard two‐stage model by examining the difference between the objective values of two solutions. The research clearly shows the importance of the multi‐stage model as compared to the two‐stage programming model.

The models developed here are limited to covering demand uncertainty, whereas real supply chain exhibits different uncertainties like capacity, processing time, etc. This can be the future direction for extending the work.

The model is very useful in designing and planning the supply chain in an uncertain environment. The model allows the adjustment of the production plan as time progresses and uncertainties become resolved.

The model uses a scenario approach to address the supply chain planning problem for a supply chain network structure under an uncertain environment and compares the two‐solution approach for a set of problems. Generally supply chain costs are in millions of dollars and the saving using multi‐stage programming can be significant.

In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To generate the global supply chain plan, we first formulate a GSCP model. Then, we need to generate several scenarios which can represent various demand uncertainties. Lastly, a planning procedure for considering those defined scenarios is applied. Unlike the past related researches, we adopt the fuzzy set theory to represent the demand scenarios. Also, a scenario voting process is added to calculate a probability (possibility) of each scenario. An illustrative example based on a real world case is presented to show the feasibility of the proposed planning process.

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.

Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks.

The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010.

The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability.

The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature.

The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis.

The goal programming model used here is invariant to the type of bank and year of consideration.

Renewable energy sources are likely to play a major role in meeting the future energy requirement of a developing country like India. Among the various renewable energy sources, the bio‐energy plays a key role for the power generation. In this paper, an attempt is made to develop a fuzzy based linear programming optimal electricity allocation model (OEAM) that minimizes the cost and determines the optimum allocation of different energy sources for the centralized and decentralized power generation in India with special emphasis to bio‐energy.

The OEAM model optimizes and selects the appropriate energy options for the power generation on the factors such as cost, potential, demand, efficiency, emission and carbon tax. The objective function of the model is minimizing the cost of power generation. The other factors are used as constraints in the model. The fuzzy linear programming optimization approach is used in the model.

The extents of energy sources distribution for the power generation in the year 2020 would be 15,800 GWh (4 per cent) from the coal based plants, 85,400 GWh (20 per cent) from the nuclear plants, 191,100 GWh (44 per cent) from the hydro plants, 22,400 GWh (5 per cent) from the wind mills, 45,520 GWh (11 per cent) from the biomass gasifier plants, 14,112 GWh (3 per cent) from the biogas plants, 8,400 GWh (2 per cent) from the solid waste, 33,600 GWh (8 per cent) from the cogeneration plants and 11,970 GWh (3 per cent) from the mini hydel plants, respectively.

The OEAM has been developed for the electricity demand allocation for the year 2020. An extensive literature survey revealed that carbon tax and emission constraints were never used in the previous models and they are considered in the present model.

The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices.

Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model.

Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered.

This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department.