This paper investigates the existence of a fundamental link between two disciplines that emerged during last few decades: complexity science and advanced engineering. During this time many industries, especially those related to the high-tech end of technological development, have faced the problem of increasing complexity of design, production and operation. Industrial projects have grown to become multidisciplinary, tightly interconnected, costly and difficult to control and predict. Two trends can be identified in this respect: one is the consistent effort of systems engineering in reducing the uncertainties of complex industrial operations and the other is the effort undertaken in complexity studies to account for uncertainties present in the real world.

In this work, we provide a brief overview of recent developments in advanced engineering and give a consistent interpretation of technological evolution from the perspective of complexity science in general and complex competitive systems (CCS) in particular. CCS is a general framework that was recently developed for analysis of complex systems involving competition. Transitivity of the decision-making process and the cyclic nature of technological progress are considered. Correctness of intransitive decisions is inherently relativistic: the same decisions can be seen as correct or incorrect when considered from different perspectives. When treated simplistically, intransitivity may seem to be illogical but, nevertheless, it is common in nature and needs to be studied. CCS provides a formalised scientific framework for analysis of intransitivity and establishes the existence of an important connection linking complexity and uncertainty with intransitivity. Implications of intransitivity for engineering decision-making and strategic planning are considered in the context of CCS. A working example of intransitivity in competition between major car manufacturers is presented.

This paper investigates the ordering policy for the newsvendor problem with customer balking and penalties for balking and stockout. Our analysis is based on the assumption that only the mean and the variance of the demand distribution are known. In contrast to the existing research, we provide a new tradeoff tool as a replacement of the traditional one to weigh the holding cost and the goodwill cost segment: the balking penalty cost and the stockout penalty cost. Specifically, in addition to the stockout penalty, we also introduce the balking penalty, provide a new proof of the optimality of robust ordering policy to guarantee that the lower bound of expected profit obtained by us is tight, and get an robust optimal order quantity which is an exact solution but not an approximate one as before. Numerical experiments are conducted to illustrate the effect of penalties for balking and stockout.

In this article, we present a continuous review (s, S) inventory system with a service facility consisting of finite buffer (capacity N) and a single server. The customers arrive according to a Poisson process. The individual customer’s unit demand is satisfied after a random time of service, which is assumed to be exponential. When the inventory level drops to s an order for Q(= S − s) items is placed. The lead time of reorder is assumed to be exponential distribution. An arriving customer, who finds the buffer is full, enters into the pool of infinite size or leaves the system according to a Bernolli trial. At the time of service completion, if the buffer size drops to a preassigned level L (1 < L < N) or below and the inventory level is above s, we select the customers from the pool according to two different policy: in first policy, with probability p(0 < p < 1) we select the customer from the head of the pool and we place the customer at the end of the buffer; in the second policy, with p(0 < p < 1) the customer from the pool is transferred to the buffer for immediate service and after completion of his service we provide service to the customer who is in the buffer with probability one. If at a service completion epoch the buffer turns out to be empty, there is at least one customer in the pool and the inventory level is positive, then the one ahead of all waiting in the pool gets transferred to the buffer, and his service starts immediately. The joint probability distribution of the number of customers in the pool, number of customers in the buffer and the inventory level is obtained in the steady-state case. Various stationary system performance measures are computed and total expected cost rate is calculated. A comparative result of two models is illustrate numerically.

From the viewpoint of service level agreements, the transmission accuracy rate is one of critical performance indicators to assess internet quality for system managers and customers. Under the assumption that each arc’s capacity is deterministic, the quickest path problem is to find a path sending a specific of data such that the transmission time is minimized. However, in many real-life networks such as computer networks, each arc has stochastic capacity, lead time and accuracy rate. Such a network is named a multi-state computer network. Under both assured accuracy rate and time constraints, we extend the quickest path problem to compute the probability that d units of data can be sent through multiple minimal paths simultaneously. Such a probability named system reliability is a performance indicator to provide to managers for understanding the ability of system and improvement. An efficient algorithm is proposed to evaluate the system reliability in terms of the approach of minimal paths.

Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model (DTM) to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in mainland China. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.

Considering the decision maker’s risk psychological factors and information ambiguity under uncertainty, a novel TOPSIS based on prospect theory (PT) and trapezoidal intuitionistic fuzzy numbers (TrIFNs) for group decision making is investigated, in which the criteria values and the criteria weights take the form of TrIFNs, and weights of decision makers are unknown. Firstly, distance measures for TrIFNs are used to induce value function under trapezoidal intuitionistic fuzzy environment. Secondly, the concepts of distance measures and trapezoidal intuitionistic fuzzy weighted averaging operator are employed to induce the weights of decision makers and thus the decision makers’ options can be aggregated. Then the PT-based separation measures and relative closeness coefficient are defined and an algorithm for ranking alternatives under trapezoidal intuitionistic fuzzy environment is proposed. Finally, a numerical example further illustrates the practicality and effectiveness of the proposed TOPSIS method.