Manufacturing and services constitute two of the five sectors of every country’s economy; depending on the maturity of the economy, they are — in terms of employment — typically the two largest sectors. The outputs or products of an economy can also be divided into goods products (due to manufacturing, construction, agriculture and mining) and services products. To date, the goods and services products have, for the most part, been mass produced; it is the premise of this paper that recent technological advances — including flexible manufacturing, cloud computing, nanotechnology and smart sensing — can better enable the transformation from mass production to mass customization. We regard mass customization as the simultaneous and real time management of supply and demand chains, based on a taxonomy that can be defined in terms of its underpinning component and management foci. From a components perspective, we first consider the value chain of supplier, manufacturer, assembler, retailer, and customer, and then develop a consistent set of definitions for supply and demand chains based on the location of the customer order penetration point. From a management perspective, we classify the methods that are employed in the management of these chains, based on whether supply and/or demand are flexible or fixed. Interestingly, our management taxonomy highlights a very critical research area at which both supply and demand are flexible, thus manageable. Simultaneous management of supply and demand chains sets the stage for mass customization which is concerned with meeting the needs of an individualized customer market. Simultaneous and real time management of supply and demand chains set the stage for real time mass customization (e.g., wherein a tailor first laser scans an individual’s upper torso and then delivers a uniquely fitted jacket within a reasonable period, while the individual is waiting). The benefits of real time mass customization cannot be over-stated as goods and services become indistinguishable and are co-produced — as “servgoods” — in real time, resulting in an overwhelming economic advantage.

This paper presents an augmented network model to represent urban transit system. Through such network model, the urban transit assignment problem can be easily modeled like a generalized traffic network. Simultaneously, the feasible route in such augmented transit network is then defined in accordance with the passengers’ behaviors. The passengers’ travel costs including walking time, waiting time, in-vehicle time and transfer time are formulated while the congestions at stations and the congestions in transit vehicles are all taken into account. On the base of these, an equilibrium model for urban transit assignment problem is presented and an improved shortest path method based algorithm is also proposed to solve it. Finally, a numerical example is provided to illustrate our approach.

The capacitated multi-level lot sizing problem is to schedule a number of different items with a bill-of-materials structure over a horizon of finite periods. To advance techniques of solving this class of problems, this paper proposes a new mixed integer programming formulation. Theoretical proofs and computational tests are provided to show that this formulation is able to provide better linear programming relaxation lower bounds than a previously-proposed strong mixed integer programming formulation. Based on the new strong formulation, a progressively stochastic search approach is proposed for solving the problem. Computational results showed that the approach generates high quality solutions, especially for problems of large sizes.

To cope with requirement changes flexibly and rapidly, the existing component-based paradigm is being evolved into a service-oriented computing paradigm. The main characteristic of the service-oriented computing paradigm is that service-oriented applications are developed as loosely coupled services that reflect business concerns. This paradigm also promotes business agility, facilitating quick reactions to business changes. Therefore, to enhance and support the benefits of the service-oriented computing paradigm, we must consider how to improve flexibility and reusability during the development of service-oriented applications. We propose the variability modeling approach to specify and control the common and distinguishing characteristics of service-oriented applications. That is, the key concepts of product-line technology can be used to make service-oriented applications more flexible and reusable. This paper describes variability modeling at two levels; the composition level and the specification level. At the composition level, we describe the variability of composition and the flow of domain services that fulfill business processes. At the specification level, we present a domain service that is an abstract service with variability. The use of our systematic variability modeling approach can greatly increase the flexibility, applicability, and reusability of service-oriented applications.

Inspired by the idea of Bonferroni mean, in this paper we develop an aggregation technique called the interval-valued intuitionistic fuzzy Bonferroni mean for aggregating interval-valued intuitionistic fuzzy information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define a weighted interval-valued intuitionistic fuzzy Bonferroni mean, based on which we give a procedure for multi-criteria decision making under interval-valued intuitionistic fuzzy environments.

This paper presents an algorithm to evaluate estimated and exact system reliabilities for a computer network in the cloud computing environment. From the quality of service (QOS) viewpoint, the computer network should be maintained when falling to a specific state such that it cannot afford enough capacity to satisfy demand. Moreover, the transmission time should be concerned as well. Thus, the data can be sent through several disjoint minimal paths simultaneously to shorten the transmission time. Under the maintenance budget B and time constraint T, we evaluate the system reliability that d units of data can be sent from the cloud to the client through multiple paths. Two procedures are integrated in the proposed algorithm-an estimation procedure for estimated system reliability and an adjusting procedure utilizing the branch-and-bound approach for exact system reliability. Subsequently, the estimated system reliability with lower bound and upper bound, and exact system reliability are computed by applying the recursive sum of disjoint products (RSDP) algorithm.

Data Envelopment Analysis (DEA) and Ratio Analysis (RA) are two widely used methods for measuring units’ productivity and any other criteria that could be assessed based on the available input and output variables. A number of researchers have studied DEA and RA and noted the positive and negative differences between them. Aggregated ratio analysis (ARA) model, which provide an important linkage between DEA and RA theory, is equivalent to the CCR DEA model, and this equivalence property offers a great deal of opportunities for DEA to be interpreted and applied in different ways. This paper extends the results of ARA model and proposes an extended aggregated ratio analysis (EARA) model, similar as the development from CCR model to BCC model in DEA context. The proposed model can offer an insight into the characteristic of returns to scale, playing the corresponding role as BCC model does. The numerical example is revisited in the paper and the results are compared.