A multilevel machine and vehicle scheduling in a flexible manufacturing system

The paper presents a multilevel decision model for simultaneous machine and vehicle scheduling in a flexible manufacturing system. The system is composed of various machine types and a set of automated guided vehicles that permit each part to move between any pair of machines. The upper level of the decision model involves machine loading and part routing for which a bicriterion integer formulation is presented with the objective of balancing machine workloads and intermachine flows of parts. The lower level involves simultaneous scheduling of machines and vehicles for which a period-by-period heuristic is proposed based on a family of complex dispatching rules. The scheduling objective is to meet all part type requirements in a minimum time. Computational examples are included to illustrate the approach proposed.     

A just-in-time three-level integrated manufacturing system for linearly time-varying demand process

This paper considers a just-in-time (JIT) manufacturing system in which a single manufacturer procures raw materials from a single supplier, process them to produce finished products, and then deliver the products to a single-buyer. The customer demand rate is assumed to be linearly decreasing time-varying. In the JIT system, in order to minimize the suppliers as well as the buyers holding costs, the supply of raw materials and the delivery of finished products are made in small quantities. In this case, both the supply and the delivery may require multiple installments for a single production lot. We develop a mathematical model for this problem, propose a simple methodology for solving the model, and illustrate the effectiveness of the method with numerical examples.     

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

This paper presents a comprehensive mathematical model for integrated cell formation and inventory lot sizing problem. The proposed model seeks to minimize cell formation costs as well as the costs associated with production, while dynamic conditions, alternative routings, machine capacity limitation, operations sequences, cell size constraints, process deterioration, and machine breakdowns are also taken into account. The total cost consists of machine procurement, cell reconfiguration, preventive and corrective repairs, material handling (intra-cell and inter-cell), machine operation, part subcontracting, finished and unfinished parts inventory cost, and defective parts replacement costs. With respect to the multiple products, multiple process plans for each product and multiple routing alternatives for each process plan which are assumed in the proposed model, the model is combinatorial. Moreover, unreliability conditions are considered, because moving from “in-control” state to “out-of-control” state (process deterioration) and machine breakdowns make the model more practical and applicable. To conquer the breakdowns, preventive and corrective actions are adopted. Finally, a Particle Swarm Optimization (PSO)-based meta-heuristic is developed to overcome NP-completeness of the proposed model.     

A model for determining machine requirements in a multistage manufacturing system with discretely distributed demand

A model is presented for resolving the problem of determining the optimum number of machines, and their discrete operating rates, required to meet discretely distributed production demands with a minimum total expected cost. The model is developed for a serial, multistage manufacturing system with both straight-time and overtime operating periods.     

Architecture of manufacturing scheduling systems: Literature review and an integrated proposal

This paper deals with the development of customised and realistic manufacturing scheduling systems. More specifically, we focus onto a key element that may help driving their efficient design and implementation: i.e., the set of building blocks that should include a generic scheduling system and its interconnections, a set collectively known as the architecture of a system. To do so, we first analyse existing contributions on the topic together with papers describing different functional requirements of scheduling systems. These contributions are then discussed and classified, and a modular architecture for manufacturing scheduling systems is proposed. This proposal updates, extends and refines the well-known architecture proposed earlier by Pinedo and Yen’s [Pinedo, M.L., Yen, B.P.-C., 1997. On the design and development of object-oriented scheduling systems. Annals of Operations Research 70 (1), 359–378], and serves to integrate the different requirements identified in the literature review.     

A generic approach to measuring the machine flexibility of manufacturing systems

Despite extensive studies on the flexibility of manufacturing systems over the last two decades, a unified measurement approach has not been developed. To this end, we integrate two domains of machine flexibility models from the literature: operational capability-based machine flexibility and time and cost-based machine flexibility, and propose a generic model to measure machine flexibility with consideration of uncertainties in the system. Furthermore, in our approach we include part characteristics such as processing time and processing cost, the number of operations that a machine can perform, and uncertainties in demand and machine-part assignment. The resulting framework to measure machine flexibility is a two-stage model: a super efficiency Data Envelopment Analysis Model and a flexibility model. The results show that the marginal system machine flexibility does not always increase as the number of operations that a machine can perform increases, and the system machine flexibility depends on the demand uncertainty.     

Using machine code within an algol system

The use of machine code within programs run in the GIER ALGOL system is made possible through the provision of standard procedures which transfer the control of the machine to the instruction held as a variable of the ALGOL program. The paper describes these and some associated standard procedures.     

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method.     

A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system

This paper develops a mixed-integer programming model to design the cellular manufacturing systems (CMSs) under dynamic environment. In dynamic environment, the product mix and part demand change under a multi-period planning horizon. Thus, the best designed cells for one period may not be efficient for subsequent periods and reconfiguration of cells is required. Reconfiguration may involve adding, removing or relocating machines; it may also involve a change in processing rout of part types from a period to another. The advantages of the proposed model are as follows: considering the batch inter/intra-cell material handling by assuming the sequence of operations, considering alternative process plans for part types, and considering machine replication. The main constraints are maximal cell size and machine time-capacity. The objective is to minimize the sum of the machine constant and variable costs, inter- and intra-cell material handling, and reconfiguration costs. An efficient hybrid meta-heuristic based on mean field annealing (MFA) and simulated annealing (SA) so-called MFA–SA is used to solve the proposed model. In this case, MFA technique is applied to generate a good initial solution for SA. The obtained results show that the quality of the solutions obtained by MFA–SA is better than classical SA, especially for large-sized problems.     

Statistical characteristics of an integrated oceanographic data processing system

Integration of special- and general-purpose application packages is considered as an efficiency improving technique, A package integration system is defined and its statistical characteristics are computed. Parallel and sequential package integration systems are analyzed. A real-life system, developed on the basis of sequential integration principles is described. This is a computer-aided oceanographic data processing system for the ES OS environment, which includes preprocessing and data preparation programs, a number of special-purpose application packages, and interfaces between these packages.Translated from Vychislitel'naya i Prikladnaya Matematika, No. 62, pp. 113–122, 1987.     

Modeling and analysis of machine sharing in manufacturing systems

The issue of machine sharing arises quite frequently in the design and operation of automated manufacturing systems. It is often championed as a mechanism for enhancing the flexibility and versatility of these systems. However despite its importance, our understanding of machine sharing and of its effect on system performance has remained inadequate, relying mainly on anecdotal data or limited empirical evidence. In this paper, we present an analytical model that captures the various dimensions of machine sharing and use this model to study the effect of machine sharing on performance of manufacturing systems. In particular, we examine the relationship between machine sharing and several performance measures, such as production rate, machine utilization, flow time and work-in-process inventory, for varying assumptions of system utilization, setup times, batch sizes and demand and processing variability. These relationships are then used to identify conditions under which machine sharing is of value and to determine the corresponding optimal sharing levels.     

A signal-recovery system: asymptotic properties,and construction of an infinite-volume process

We consider a linear sequence of ‘nodes’, each of which can be in state 0 (‘off’) or 1 (‘on’). Signals from outside are sent to the rightmost node and travel instantaneously as far as possible to the left along nodes which are ‘on’. These nodes are immediately switched off, and become on again after a recovery time. The recovery times are independent exponentially distributed random variables. We present results for finite systems and use some of these results to construct an infinite-volume process (with signals ‘coming from infinity’), which has some peculiar properties. This construction is related to a question by Aldous and we hope that it sheds some light on, and stimulates further investigation of, that question.     

Saddle problem and optimization problem as an integrated system

A parametric problem of convex programming and a linear optimization problem on a convex set are considered as an integrated system of problems. Properties of this system are studied, the sphere of its application is discussed, and methods for its solution are proposed. The convergence of the proposed methods is established.     

Feed-forward neural networks and feature construction with correlation information: an integrated framework

Feature construction has been shown to be an useful technique to improve the efficiency of extracting information from raw data. We develop a modified feature construction algorithm, using correlation information among the initial set of features, and study its performance. Feed-forward neural networks, using the back-propagation algorithm to learn, have been shown to have excellent properties while plagued with the problem of time needed to learn concepts. We alleviate this inherent problem with the back-propagation algorithm using data pre-processed by the proposed feature construction algorithm. Initial results suggest that this methodology can be beneficially used along with other means of improving the learning performance in feed-forward neural networks.     

IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection

A fundamental principle of modern portfolio theory is that comparisons between portfolios are generally made using two criteria, corresponding to the first two moments of return distributions, namely the expected return and portfolio variance. According to this model and according to most of the portfolio models derived from the stochastic dominance approach, the group of portfolios open to comparisons is divided into two parts: on the one hand there are the efficient portfolios (those that are not dominated by any other portfolio in the group), and on the other, those that are dominated. In other words, these models do not solve for one optimal portfolio, but rather solve for an efficient set of portfolios, among which the investor must choose, given his preference system. One criticism over these models, which has often been addressed both by practitioners and academics, is that they fail to embody the objectives of the decision maker (DM), through the various stages of the decision process. Our purpose in this article is to present an integrated and innovative methodological approach for the construction and selection of equity portfolios, which will take into account the inherent multidimensional nature of the problem, while allowing the DM to incorporate his preferences in the decision process. The proposed approach, which grounds its basis on the field of multiple criteria decision making (MCDM) and more specifically on multiobjective mathematical programming (MMP), is implemented in the IPSSIS (Integrated Portfolio Synthesis and Selection Information System) decision support system (DSS). The validity of the proposed approach is tested through an illustrative application in the Athens Stock Exchange (ASE).     

Multivariable identification and controller design of an integrated flight control system

This paper investigates the multivariable identification and controller design for the longitudinal channel of a Boeing 747 transport. The transfer function matrix of the system is identified using the prediction error (PE) identification method with multivariable ARX model. An ellipsoidal parametric uncertainty set is constructed from the covariance matrix of the identified parameters. It contains the parameters of actual system at a certain probability level. The identified models and the associated uncertainty sets are validated by measuring the worst-case ν-gap and then compared with the maximum value of the generalized stability margin. In automatic flight control system or autopilots, multiple specifications criteria are needed to be satisfied concurrently, such as good holding (small static altitude holding error), fast response, smooth transition (less oscillation, overshoot). The design of a Multiple Simultaneous Specifications (MSS) controller effectively and practically is a very significant and challenging job. Liu and Mills [H.H.T. Liu, J.K. Mills, Multiple specification design in flight control system, in: Proceedings of the American Control Conference, Chicago, Illinois, 2000, pp. 1365–1369] proposed a MSS controller design method using a convex combination approach. In this paper, we apply the method [H.H.T. Liu, J.K. Mills, Multiple specification design in flight control system, in: Proceedings of the American Control Conference, Chicago, Illinois, 2000, pp. 1365–1369; H.H.T. Liu, Design combination in integrated flight control, in: Proceedings of the American Control Conference, Arlington, Virginia, 2001, pp. 494–499; H.H.T. Liu, Multi-objective design for an integrated flight control system: a combination with model reduction approach, in: Proceedings of IEEE International Symposium on Computer Aided Control System Design, Glasgow, 2002, pp. 21–26] to design a MSS controller based on the identified models of the Boeing 747 transport aircraft longitudinal channel. The controllers are also validated by simulation using the true plant transfer functions.     

Hierarchical design of an integrated production and 2-echelon distribution system

This study uses the method of Lagrangean relaxation in the hierarchical design of an integrated model of production–distribution functions in a 2-echelon system. A mixed integer mathematical model is developed with a centralized planning perspective to address production and distribution decisions simultaneously. In order to solve the resulting large-scale problem, the Lagrangean relaxation is used to decouple the imbedded distribution and production subproblems, and subgradient optimization is implemented to coordinate the information flow between these in a hierarchical manner. This corresponds to a decentralized organizational design where a central agent coordinates the information exchange between the distribution and production organizational units. A forward heuristic designed to solve the distribution subproblem is shown to provide good solutions. Hierarchical interdependency is incorporated into the Lagrangean heuristic such that distribution decisions are placed in the top level to restrict the solution of the production subproblem in the lower level.     

A dynamic saint-venant principle for an elastic semi-infinite strip

We consider the problem of longitudinal deformation of an elastic semi-infinite strip under the action of various kinds of self-balanced harmonic loads. Using numerical analysis we show that in a region of relatively low frequencies, when only one propagating wave exists in the wave conductor, one can formulate an analog of Saint-Venant's principle. When this is done, the given frequency range decomposes into two subranges. In the first of these the main role in the Fourier series expansion of the load is played by the non-self-balanced component, while in the second it is necessary to take account of the first harmonic as well. One figure. Bibliography: 5 titles. Translated fromTeoreticheskaya i Prikladnaya Mekhanika, No. 22, pp. 40–46, 1991.     

Production control of an unreliable manufacturing system under the assumption of no backlog

This paper deals with an unreliable manufacturing system in which no backlog is allowed. The hedging point policy is used to control production. This paper's objective is to find the optimum hedging point so as to minimize the average inventory cost. Firstly, for a tentative hedging point, we derive the limiting distribution of the inventory level. This derivation is accomplished by comparison between this system and a finite storage-production system. Based on it we calculate the average inventory cost and find the optimum hedging point.This research was supported in part by the National Natural Science Foundation of China. Bin Liu was partially supported by ITDC, contract No. 105-82150.