Abstract:
Purpose: Improving the productivity of a large manufacturing firm through development and implementation of a group technology model in a real manufacturing environment. This incorporates classification of parts, coding of parts and par families, formation of machine cells, and minimization of machine idle times through machine cell capacity adjustment.Design/methodology/approach: An algorithm has been developed for classification of all the parts into part families on the basis of manufacturing similarities. This formed 144 par families for more than 7500 different parts in production. The algorithm assigned group technology code to each part and a part family code to each part family. The former represents manufacturing characteristics of the part and the latter simplifies determination of part families. A number of machine cells were developed to produce all of the part families. After classification the system automatically directs each part to the appropriate machine cell for manufacturing. A computer software has been developed that automated the functions of classification of parts and assigning a group technology code to each part, determination of part families and assigning part family codes, and directing part families to appropriate machine cells for production.Findings: Introducing part family codes in addition to group technology codes considerably simplified the task of part family determination. Application of the system immediately resulted in an increased productivity of about 50%. This was due to the reduced setup times, less flow of parts in the workshop, production of similar parts due to parts classification, etc. Yet a productivity improvement of 100% or more is anticipated in near future.Research limitations/implications: As the manufacturing firm was producing a wide range of products at the time of implementation of this work, it was difficult to implement the project without affecting the production flow significantly. There has also been some resistance from technical people opposing a change in traditional production methods.Originality/value: Integration of machine cell formation with capacity adjustment is of great value that resulted in significant productivity improvements. Also some issues regarding actual implementation of group technology and associated problems and issues, coding of parts and part families, formation of machine cells, and capacity adjustment of machine cells have been dealt with and discussed in this paper.

Abstract:
We provide a general framework to construct finite dimensional approximations of the space of convex functions, which also applies to the space of c-convex functions and to the space of support functions of convex bodies. We give estimates of the distance between the approximation space and the admissible set. This framework applies to the approximation of convex functions by piecewise linear functions on a mesh of the domain and by other finite-dimensional spaces such as tensor-product splines. We show how these discretizations are well suited for the numerical solution of problems of calculus of variations under convexity constraints. Our implementation relies on proximal algorithms, and can be easily parallelized, thus making it applicable to large scale problems in dimension two and three. We illustrate the versatility and the efficiency of our approach on the numerical solution of three problems in calculus of variation : 3D denoising, the principal agent problem, and optimization within the class of convex bodies.

Abstract:
A popular method for handling state and output constraints in a model predictive control (MPC) algorithm is to use 'soft constraints', in which penalty terms are added directly to the objective function. Improved closed loop performance can be obtained for plants with nontninimum phase zeros by modifying the MPC formulation to include suitably-designed time-dependent weights on the penalty terms associated with the state and output constraints. When the penalty terms are written in terms of the 'worst-ease' l-infinity norm, incorporating the appropriate time dependence into the weights provides much better closed loop performance. The approach is illustrated using two multivariable plants with nonminimum phase transmission zeros, where the time-dependent weights cause the open loop predictions to coincide with closed loop predictions, which results in a reduction of output constraint violations.

Abstract:
Condensate gas reservoir is a special type reservoir. Fluid phase state study is very important to the development of this kind reservoir, especially to high liquid hydrocarbon content condensate reservoir. Through the study of fluid phase state, the fluid composition variance of the condensate gas reservoir during the different development phase can be acquired, thus better development mode can be decided. Fluid sample can be acquired periodically and the study for the phase characteristic and fluid composition variance can be carried out to direct the plan implementation and direction adjustment. Fluid evaluation study can be widely used and has an important role in the development of condensate gas reservoir.

Abstract:
In the Scheduling Machines with Capacity Constraints problem, we are given k identical machines, each of which can process at most m_i jobs. M jobs are also given, where job j has a non-negative processing time length t_j >= 0. The task is to find a schedule such that the makespan is minimized and the capacity constraints are met. In this paper, we present a 3-approximation algorithm using an extension of Iterative Rounding Method introduced by Jain. To the best of the authors' knowledge, this is the first attempt to apply Iterative Rounding Method to scheduling problem with capacity constraints.

Abstract:
Recent research modeling uncertainty in water resource systems has highlighted the use of fuzzy logic based approaches. The uncertainties in water resource systems include fuzziness, subjectivity, imprecision and lack of adequate data. In this paper we focus on Fuzzy Linear Programming (FLP) problem for reservoir opera- tion with fuzzy objectives function and fuzzy constraints. Uncertainty in reservoir operation parameters such as reservoir storages, releases for irrigation, releases for hydropower production, irrigation demands, and power demands are considered by treating them as a fuzzy set. This study is devoted to the identification of optimal operating policy using three different models. A fuzzy linear programming reservoir operation models are developed within a linear programming framework. These models are applied to a case study of Jayakwadi reservoir stage -II, Maharashtra State, India with the objective of maximization of releases for irrigation and hydropower. Fuzzy set theory is used to model imprecision in various parameters by developing three models. First model considers fuzzy resources, second model is with fuzzy technological coefficients and third model considers both, fuzzy technological coefficients and fuzzy resources in linear programming framework. Fuzziness in objective function and in the constraints is quantified by a membership functions. These three models are solved to obtain compromise solution by simultaneously optimizing the fuzzified objectives and constraints. The degree of satisfaction is obtained by simultaneously optimizing the objectives are 0.53, 0.52 and 0.525 by three models respectively. The obtained result show that proposed methodology provides an effective and useful tool for reservoir operation where decision maker can decides to opt for a model depends on the imprecision involved in reservoir operation model parameters.

Abstract:
The division problem consists of allocating a given amount of an homogeneous and perfectly divisible good among a group of agents with single-peaked preferences on the set of their potential shares. A rule proposes a vector of shares for each division problem. Most of the literature has implicitly assumed that all divisions are feasible. In this paper we consider the division problem when each agent has a maximal capacity due to an objective and verifiable feasibility constraint which imposes an upper bound on his share. Then each agent has a feasible interval of shares where his preferences are single-peaked. A rule has to propose to each agent a feasible share. We focus mainly on strategy-proof, efficient and consistent rules and provide alternative characterizations of the extension of the uniform rule that deals explicitly with agents’ maximal capacity constraints.

Abstract:
Directed links -- representing asymmetric social ties or interactions (e.g., "follower-followee") -- arise naturally in many social networks and other complex networks, giving rise to directed graphs (or digraphs) as basic topological models for these networks. Reciprocity, defined for a digraph as the percentage of edges with a reciprocal edge, is a key metric that has been used in the literature to compare different directed networks and provide "hints" about their structural properties: for example, are reciprocal edges generated randomly by chance or are there other processes driving their generation? In this paper we study the problem of maximizing achievable reciprocity for an ensemble of digraphs with the same prescribed in- and out-degree sequences. We show that the maximum reciprocity hinges crucially on the in- and out-degree sequences, which may be intuitively interpreted as constraints on some "social capacities" of nodes and impose fundamental limits on achievable reciprocity. We show that it is NP-complete to decide the achievability of a simple upper bound on maximum reciprocity, and provide conditions for achieving it. We demonstrate that many real networks exhibit reciprocities surprisingly close to the upper bound, which implies that users in these social networks are in a sense more "social" than suggested by the empirical reciprocity alone in that they are more willing to reciprocate, subject to their "social capacity" constraints. We find some surprising linear relationships between empirical reciprocity and the bound. We also show that a particular type of small network motifs that we call 3-paths are the major source of loss in reciprocity for real networks.

Abstract:
Much of the existing work on the broadcast channel focuses only on the sending of private messages. In this work we examine the scenario where the sender also wishes to transmit common messages to subsets of receivers. For an L user broadcast channel there are 2L - 1 subsets of receivers and correspondingly 2L - 1 independent messages. The set of achievable rates for this channel is a 2L - 1 dimensional region. There are fundamental constraints on the geometry of this region. For example, observe that if the transmitter is able to simultaneously send L rate-one private messages, error-free to all receivers, then by sending the same information in each message, it must be able to send a single rate-one common message, error-free to all receivers. This swapping of private and common messages illustrates that for any broadcast channel, the inclusion of a point R* in the achievable rate region implies the achievability of a set of other points that are not merely component-wise less than R*. We formerly define this set and characterize it for L = 2 and L = 3. Whereas for L = 2 all the points in the set arise only from operations relating to swapping private and common messages, for L = 3 a form of network coding is required.

Abstract:
The adaptive constraints relaxing rule for swarm algorithms to handle with the problems with equality constraints is presented. The feasible space of such problems may be similiar to ridge function class, which is hard for applying swarm algorithms. To enter the solution space more easily, the relaxed quasi feasible space is introduced and shrinked adaptively. The experimental results on benchmark functions are compared with the performance of other algorithms, which show its efficiency.