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Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.

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The newsvendor problem is a classic problem of decision making under risk that is taught in traditional Operations and Supply Chain Management classes as a single‐period inventory problem. We discuss the following three pedagogical points of interest to any instructor tasked with teaching this topic: a) why the newsvendor model is relevant in this day and age; b) how to make better sense of the newsvendor problem for students; and c) how to easily implement and administer an active learning exercise in either a traditional classroom, or an online setting. This active learning exercise is a quick, effective, and meaningful way of demonstrating a variety of concepts related to the newsvendor problem that include: a) the rational economic method of calculating optimal order quantity, b) the inherent risk in forecasting and ordering decisions as they relate to surpluses and shortages; and c) the cognitive limitations in decision making that often result in irrational but predictable decision‐making behavior as demonstrated by empirical research on newsvendor behavior. This exercise can help instructors and students transition into broader discussions on human behavior, cognitive limitations, and perceptions of risk. Overall, it should provide the student with an improved understanding of the operational and behavioral issues associated with decision making under risk.

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