The critical risk factors that influence production-oriented projects in the United Arab Emirates : a ‘best-worst method’ (BWM) analysis

The aim of this paper is to categorize and prioritize the critical risk factors that influence production-oriented projects. Utilizing data obtained from the metal production (manufacturing) and fabrication industry in United Arab Emirates, we employ multicriteria decision analysis encompassing the ‘Best-Worst Method’ (BWM) for factor ranking and categorization. The outcome of this exercise being the development of substantial proficiency in risk management that will have a significant impact on the overall success of projects commissioned within the production space. Findings drawn against an integrated ‘Technology–Organization–Environment’ and ‘Four levels of uncertainty’ framework suggests that ‘Automation,’ ‘Cycle time,’ and ‘Feed rate’ (technological factors), ‘Manpower utilization’ and ‘Agility’ (organizational factors), and ‘Occupational health and safety’ (environmental factors), ranked highest in terms of critical risk factors likely to impact upon the outcome of projects. This paper makes a specific contribution to the literature in that our use of an integrated ‘Technology–Organization–Environment’ – ‘Four levels of uncertainty’ framework as a risk intelligence focused typology allows us to focus on proactive as against reactive management of risk. This forms the core element of our theorization of risk knowledge as risk intelligence

LSP-Constrained Supply Chains: A Discrete Event Simulation Model

In this paper, we present a logistics service provider (LSP) constrained supply chain problem, particularly; we propose a novel supply chain model that consists of three layers of non-cooperative manufacturers, distribution centers and retailers. Products flow from the manufacturers across different warehouses to retailers via LSP. Inventories at warehouses follow smooth and continuous replenishment policy, i.e., perpetual review. The supply chain is represented as an optimization model that maximizes the revenue of manufacturers meets the retailers’ demand and at the same time identifies the necessary warehouses, particularly for supply chains that are affected by leadtime (LT) variation such as fast response industry and short life products. The model solution is adaptive; it determines the best manufacturing rates and identifies the logistic bottlenecks in dynamic supply chain networks. Numerical solutions along with simulation experiments of different supply chain topologies are presented. The simulation results demonstrate the model capability to maximize the revenues by tuning the manufacturing rates and monitoring the workinprocess, products in transit as well as products in inventorie

Risk premiums and certainty equivalents of loss-averse newsvendors of bounded utility

Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to model risk attitudes fails to explain some decision-making paradoxes. In contrast, this paper deals with the utility maximization of the newsvendor using a class of bounded utility functions to study the effect of loss aversion on the newsvendor certainty equivalents and risk premiums. New formulas are introduced to find the utility-optimal order quantity of the normal distribution. The results show that when an exponential loss aversion exists, the classical newsvendor optimal quantity serves as a lower bound when the overage costs are high and as an upper bound when the underage costs are high. In addition, we show that high loss aversion entails higher risk premiums. Similar conclusion holds when the overage/underage costs increase. Higher standard deviations, on the other hand, mean lower utility-optimal quantities and higher risk premiums. The presented formulas are advantageous in finding the optimal order quantities and risk premiums of a stochastic short-shelf life inventory when the loss is a key factor in the decision-making process

Voluntary overbooking in commercial airline reservations

This paper studies a voluntary overbooking model under rational expectation equilibrium to promote cooperation between customers and airlines, maintain goodwill of customers, and maximize the expected total returns to airlines. A decision tree analysis is constructed for both customers and airlines. Sensitivity analysis is conducted in both realistic and simulated no-show random variables for validation. The findings suggest considerable mutual benefits associated with a ‘voluntary overbooking’ policy that emphasizes mutual cooperation between passengers and commercial airlines. The main underlying assumption of the paper is that customers are willing to provide valuations to airlines seeking volunteers for overbooking. The originality of the proposed model is the incorporation of elements of the Rational expectations hypothesis into classical overbooking models gleaned from the literature

Duality of the improved algebraic method (DIAM)

In this note we present a variant of the improved algebraic method (IAM) using a duality analysis to solve linear programming (LP) problems where more insights to the method are presented. When the coordinates of all vertices are computed, any feasible point can be expressed as a linear combination of the vertices. The objective function is expressed as a weighted sum of its evaluation at the feasible vertices and the optimal point is associated with the highest/lowest coefficient of the weighted sum. In this work two adaptations of LP objective function are formulated in primal and dual domains. A simple LP bounds test is also presented which includes unbounded solution space in the IAM. The presented analysis can determine degeneracy and/or alternative optima from the dual parametric objective function. It also spots the optimal solution by intersecting the primal and dual parametric objective functions. The proposed approach is simple and enhances the understanding of the simplex method. We demonstrate several numerical examples to explain the proposed analysis.Linear programming Learning Education

Predictive decision making under risk and uncertainty: A support vector machines model

In this paper, a decision making model using support vector machine (SVM) approach is presented. Here, human attitude towards risk and uncertainty is identified via optimizing SVM certainty classification model. In particular, individuals are given different pairs of gambles in order to reveal their preference. Unlike traditional methods used to estimate the utility function through direct inquiry of the certainty equivalents, pair-wise comparisons are used here in the training process to predict human preferences and to compute the utility parameters. The presented study is characterized by first, the use of SVM in the field of decision making to classify individuals’ choices, second, it uses such model to search for the optimal utility parameters, third, the model can be used to guide the decision makers towards better decisions. In contrast to existing utility models, the SVM utility approach is characterized by its tolerance to misclassification in the training and testing data sets which makes it cope with the existing violations such as the common consequence, common ratio and violation of betweenness in the utility theory. To demonstrate the merits of the model, different data sets were used from well known literature studies and new conducted surveys that elicit individual preferences. The data is split into training and testing sets. The results demonstrated a notable consistency in the computed utility parameters and remarkable predictions without the need to strict certainty equivalent estimation. The model can be beneficial in predictive decision making under risk and uncertainty

On perishable inventory in healthcare: Random expiration dates and age discriminated demand

The aim of the study is to explore how best to mitigate against inventory volatility in perishable inventory, which is characterized by random premature expiration, random demand, irregular supply, age differentiated demand and custom replenishment guidelines. Through the adoption of simulation-optimization along with new settings and replenishment policies, the optimized quantity level of daily orders could be determined for this combination of inventory restrictions. Owed to their custom medical compatibility guidelines, and their notable accelerated expiration, blood platelets were considered here. As study outcome, the emergent model presents a perspective of supply chains and their healthcare imperatives that will enable healthcare supply chain managers not only to discern, but also to interpret and facilitate the management and implementation of optimal inventories

A Support Vector Machine model for due date assignment in manufacturing operations

The relationship between product flow times and manufacturing system status is complex. This limits use of simple analytical functions for job shop manufacturing due date assigning, especially when dealing with orders involving multiple-resource manufacturing systems in receipt of random orders of different process plans. Our approach involves developing a Support Vector Machine classifier to articulate job shop manufacturing due date assigning in heterogeneous manufacturing environments. The emergent model allows not only for the complex relationships between flowtimes and manufacturing system status, but also for the prediction of random order flowtime of manufacturing systems with multiple resources. Our findings also suggest that service levels play a major role in negotiated due dates and eventual customer propensity to place manufacturing orders. In emphasizing negotiated due dates as against exogenous assigned due dates, the study focuses scholarly attention toward the need for participative, open and inclusive due date assignments.</p