Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment

The buffer allocation problem (BAP) is a well-known difficult problem in the design of production lines. We present a stochastic algorithm for solving the BAP, based on the cross-entropy method, a new paradigm for stochastic optimization. The algorithm involves the following iterative steps: (a) the generation of buffer allocations according to a certain random mechanism, followed by (b) the modification of this mechanism on the basis of cross-entropy minimization. Through various numerical experiments we demonstrate the efficiency of the proposed algorithm and show that the method can quickly generate (near-)optimal buffer allocations for fairly large production lines.

Minimum-order stable recursive filter design via the genetic algorithm approach

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Warranty and preventative maintenance

For products sold with warranty, preventive maintenance actions by manufacturers and/or buyers have an impact on the total costs for both parties. This paper develops a framework to study preventive maintenance actions when items are sold under warranty and reviews the models that have appeared in the literature. It then develops a new model and carries out its analysis.

A tutorial on the cross-entropy method

The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss applications in combinatorial optimization and machine learning. combinatorial optimization

Understanding service experience in non-profit performing arts: Implications for operations and service management

Recent research in the non-profit performing arts has shown that marketing efforts designed to increase revenue from ticket sales are not achieving the results required to sustain the performing arts. This paper applies operations management analytical techniques to the non-profit performing arts to increase understanding of operational issues and inform service management strategy. The paper takes a two-study idiographic approach. Implementing a modified version of service transaction analysis (STA), Study One describes a performing arts service from provider and customer perspectives, identifies service gaps and develops an elaborated service description incorporating both perspectives. In Study Two, building on the elaborated service description and extant research, in-depth interviews are conducted to gather thick descriptions of predictors of satisfaction, value and service quality as they relate to repurchase intention (RI). Technical, functional and critical factors required to improve organizational performance are identified. Implications for operational strategy, service design and service management theory for this context are discussed. (c) 2005 Published by Elsevier B.V.

Maintenance Strategies for used equipment under lease

Abstract: Purpose – The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs). Design/methodology/approach – The problem is a four-parameter optimization with two parameters being k-dimensional. The optimal solution is obtained by using a four-stage approach where at each stage a one-parameter optimization is solved. Findings – Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF. Practical implications – There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment. Originality/value – Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Business education in operations management

This paper examines trends in the practice of Operations Management and in teaching the field in major Business Schools. Operations Management has been defined as the design and management of transformation processes that create value for society. The operations function is the one function directly involved in that transformation, and hence is directly responsible for the activities that justify the existence of the firm, both economically and as a value-creating organization in society. The top rated schools in Operations Management are the top-rated research-intensive Business Schools in the world. Operations Management is an area that has been undergoing rapid change in response to changes in business practices worldwide. It is at the heart of changes of which the AACSB report Management Education at Risk, August 2002 (p 20), comments of Business Schools in general: ‘With regard to global relevance (of Business Schools), the complex opportunities and challenges that emanate from the world scope of operations, outsourcing, supply chains, partnerships, and financial and consumer markets – all linked in real time through the Internet – are not reflected adequately in curricula and learning approaches.’ Products, and even services, depend increasingly on advanced technology. This is true globally and especially so for countries in South East and East Asia, from which Australian Universities draw a significant number of students. Services operations management has become much more important, while there are both educational and industrial needs in management science or operations research.

Weibull model selection for reliability modelling

A large number of models have been derived from the two-parameter Weibull distribution and are referred to as Weibull models. They exhibit a wide range of shapes for the density and hazard functions, which makes them suitable for modelling complex failure data sets. The WPP and IWPP plot allows one to determine in a systematic manner if one or more of these models are suitable for modelling a given data set. This paper deals with this topic.

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.

A fast cross-entropy method for estimating buffer overflows in queuing networks

In this paper, we propose a fast adaptive importance sampling method for the efficient simulation of buffer overflow probabilities in queueing networks. The method comprises three stages. First, we estimate the minimum cross-entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer level. Finally, the tilting parameter just found is used to estimate the overflow probability of interest. We study various properties of the method in more detail for the M/M/1 queue and conjecture that similar properties also hold for quite general queueing networks. Numerical results support this conjecture and demonstrate the high efficiency of the proposed algorithm.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.