Some researchers have suggested that scientific foundations expressed in a mathematical form are needed to thrust the success of systems engineering as a discipline on its own merit. In order to contribute the development of such systems science, this paper investigates from a foundational standpoint the relationships between stakeholder needs, system requirements, and sets of systems. Various theorems and corollaries are proposed and mathematically proven. The theoretical elements are presented as a foundation for the development of a science for requirements engineering. The proposed foundations are finally tested to mathematically describe, in a rigorous and precise manner, qualities of good requirements, which are otherwise traditionally defined using vague narrative. By showcasing practical examples of the theoretical aspects, the paper is intended to serve as a bridge between practitioners and theorists.

The article A Contribution to the Scientific Foundations of Systems Engineering: Solution Spaces and Requirements written by Alejandro Salado, Roshanak Nilchiani and Dinesh Verma, has been revised due to a missing part the authors forgot to add. The missing part goes at the end of Appendix B, Page 36.

This paper proposes a dynamic advertising model for deteriorating items, and the demand is influenced by goodwill and inventory level. The goodwill affected by advertising effort has a positive effect on demand while the inventory level has a negative effect on demand, which is named as inverse inventory sensitive demand effect. We assume that the deteriorating rate could be influenced by the inventory level and we determine the deteriorating rate formulation based on this assumption. The optimal advertising effort and inventory level are obtained by solving the optimization problem based on Pontryagin’s maximum principle. Furthermore, numerical studies are provided and some managerial insights are presented.

Investing on value-added service (VAS) amplifies users’ participation and platform profit. However, the investing resource is usually limited in practice. This paper investigates VAS investing and pricing strategies for a two-sided platform under investing resource constraint. We reveal that with VAS investment, Subsidizing can still be done to enlarge users’ demand, even when the investing cost becomes higher. For optimal pricing strategies, the network effect will be the dominating determinant if the gap between two marginal cross-side benefits (i.e. the benefit that users obtain when each new user join the other side of the platform) is large. Interestingly, we show that with the increase of the marginal investing cost, users might either be priced higher or lower. If the marginal investing cost increases to a high level, and the gap between the two marginal cross-side benefits is large, lowering the access fee for users possessing the higher cross-side network effect does not necessarily compensate more profit loss caused by higher cost. Moreover, after VAS is developed, raising the access fee for those whose marginal investing benefit is large does not necessarily generate more profit as well. The opposite strategy further enlarges users’ utility, and promotes the investment to benefit more users.

Due to bounded rationality, people often rush for instantaneous gratification if the decisions involve immediate rewards, but procrastinate if the decisions involve immediate costs. Present-biased preference and time-inconsistency will be more salient when people are making inter-temporal decisions to trade off rewards and costs in the future. People outweigh the present and their preference for the outcome in the future will decline over time. Incorporating present-biased preference and time-inconsistency under quasi-hyperbolic discounting, time value of money under inflation and time-varying demand with shortages completely backlogged, we develop an inventory replenishment model for a deteriorating item. Inventory replenishment policies that benefit the decision maker are as follows. With the increase of time-inconsistency, one can increase ordering number and decrease fraction of shortages; with the increase of hazard rate within an upper limit, one can increase ordering number and decrease fraction of shortage; with the increase of compounded discount rate, one can increase ordering number and fraction of shortage. Hyperbolic discounting increases the ordering number and decreases the final profit, and the degree of hyperbolic discounting strengthens such outcomes. As a result, the optimal policy for a decision maker is to keep as rational as possible when making inter-temporal decisions.

With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature’s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.

This article studies a problem of joint pricing and dynamic product quality investment with consumers’ reference quality effect under the existence of quality inflation. Optimal control models are constructed to maximize the total profit with a limited quality investment capacity, where the demand is sensitive to historical product quality level. The optimal quality investment strategies for finite and infinite planning horizon are given respectively by solving these optimal control models on the basis of Pontryagin’s maximum principle, which enables the exact trajectory of the optimal quality investment with the reference quality effect over time to be depicted. In addition, an effective algorithm is designed to generate the optimal joint pricing and dynamic quality investment policy for the system. The main difference between the strategy of finite planning horizon and that of infinite planning horizon is that the latter is a constant. Our study indicates that it is never optimal for firm to increase quality investment all the way throughout the planning horizon. The level of quality investment is higher when taking into account the impact of reference quality. Moreover, numerical example is given to illustrate the validness of the theoretical results. Also, sensitivity analysis is carried out to show how system parameters affect the optimal policies, and some managerial suggestions are presented.