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The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of 'Alternatives × Criteria' as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others. [ABSTRACT FROM AUTHOR]

Facility layout design, a NP hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new sustainable stochastic dynamic facility layout problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated are then analyzed by data envelopment analysis to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi attribute decision-making techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, Borda-Kendall and integer linear programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size $$N=12$$ for time period $$T=5$$ having Gaussian demand are considered. [ABSTRACT FROM AUTHOR]

This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. [ABSTRACT FROM AUTHOR]

In this study, a Markovian fluid flow system with two stages separated by a finite buffer is considered. Fluid flow models have been analyzed extensively to evaluate the performance of production, computer, and telecommunication systems. Recently, we developed a methodology to analyze general Markovian continuous flow systems with a finite buffer. The flexibility of this methodology allows us to analyze a wide range of systems by specifying the transition rates and the flow rates associated with each state of each stage. In this study, in order to demonstrate the applicability of our methodology, we model and analyze a range of models studied in the literature. The examples we analyze as special cases of our general model include systems with phase-type failure and repair-time distributions, systems with machines that have multiple up and down states, and systems with multiple unreliable machines in series or parallel in each stage. For each case, the Markovian model is developed, the transition and flow rates are determined, and representative numerical results are obtained by using our methodology. [ABSTRACT FROM AUTHOR]

Serial production lines with machines having their quality parameters monotonically decreasing as a function of their efficiencies are analyzed. The Bernoulli reliability and quality models are assumed. It is shown that in such systems the production rate of good parts is a non-monotonic function with respect to machine efficiencies. Thus, bottlenecks may be eliminated by either increasing or decreasing machine efficiency, depending on the operating point. [ABSTRACT FROM AUTHOR]

The paper proposes a decomposition method for evaluating the performance of transfer lines where machines can fail in multiple modes and can be repaired with non-exponential times. Indeed, while times to machine failure can be often modeled using exponential distributions with acceptable accuracy, times to repair are very rarely observed to be exponentially distributed in actual systems. This feature limits the applicability of existing approximate analytical methods to real production lines. In this paper, the discrete acyclic phase-type distribution is used to model the repair process, for each failure mode of the machines composing the system. The exact analysis of the two-machine system is used as a building block for the decomposition method, proposed to study multi-stage lines. Numerical results show the high accuracy of the developed method in estimating the average throughput and buffer levels. [ABSTRACT FROM AUTHOR]

The analysis of manufacturing systems with finite capacity and with general service time distributions is made of two steps: the distributions have first to be transformed into tractable phase-type distributions, and then the modified system can be analytically modelled. In this paper, we propose a new alternative in order to build tractable phase-type distributions, and study its effects on the global modelling process. Called 'probability masses fitting' (PMF), the approach is quite simple: the probability masses on regular intervals are computed and aggregated on a single value in the corresponding interval, leading to a discrete distribution. PMF shows some interesting properties: it is bounding, monotonic, refinable, it approximates distributions with finite support and it conserves the shape of the distribution. With the resulting discrete distributions, the evolution of the system is then exactly modelled by a Markov chain. Here, we focus on flow lines and show that the method allows us to compute upper and lower bounds on the throughput as well as good approximations of the cycle time distributions. Finally, the global modelling method is shown, by numerical experiments, to compute accurate estimations of the throughput and of various performance measures, reaching accuracy levels of a few tenths of a percent. [ABSTRACT FROM AUTHOR]

The problem of makespan minimization on a flexible flow shop with k stages and m s machines at any stage proposed by Paternina-Arboleda et al. [Ann. Oper. Res. 164:29–40, ] was recently published in Annals of Operations Research journal to solve the flexible flow-shop scheduling problems. In this work, some comments and suggestions are given to the paper. The comment is about the proposed mathematical formulation and the modification of it is suggested. [ABSTRACT FROM AUTHOR]

Certain types of chemical reactions, such as the global deprotection of a polypeptide, are extremely complex. As a result, it may be very difficult or expensive to develop accurate models of these chemical reactions. Without a satisfactory kinetic model for the reaction, it is difficult to develop an optimum operating policy that will maximize the profit. Stochastic optimization is applied in this work to an example process step to obtain the optimum reaction temperature and reaction time. In the case of the “here and now” problem, the optimal conditions are a lower reaction temperature and a longer reaction time than obtained from the deterministic problem. The average reaction time for the “wait and see” problem is also longer than the deterministic case, but the average reaction temperature is very close to that of the deterministic problem. Both normally and uniformly – distributed uncertain parameters are considered. [ABSTRACT FROM AUTHOR]

Design of the optimal cure temperature cycle is imperative for low-cost of manufacturing thermosetting-matrix composites. Uncertainties exist in several material and process parameters, which lead to variability in the process performance and product quality. This paper addresses the problem of determining the optimal cure temperature cycles under uncertainty. A stochastic model is developed, in which the parameter uncertainties are represented as probability density functions, and deterministic numerical process simulations based on the governing process physics are used to determine the distributions quantifying the output parameter variability. A combined Nelder–Mead Simplex method and the simulated annealing algorithm is used in conjunction with the stochastic model to obtain time-optimal cure cycles, subject to constraints on parameters influencing the product quality. Results are presented to illustrate the effects of a degree of parameter uncertainty, constraint values, and material kinetics on the optimal cycles. The studies are used to identify a critical degree of uncertainty in practice above which a rigorous analysis and design under uncertainty is warranted; below this critical value, a deterministic optimal cure cycle may be used with reasonable confidence. [ABSTRACT FROM AUTHOR]

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, a decision-making framework (DMF) based on stochastic dynamic programming (SDP) is presented for a wastewater treatment system, consisting of a liquid treatment line with eleven levels and a solid treatment line with six levels (Chen and Beck, 1997). A continuous-state SDP solution approach based on the OA/MARS method (Chen, Ruppert, and Shoemaker, 1999) is employed, which provides an efficient method for representing a wide range of possible influent conditions. The DMF is used to evaluate current and emerging technologies for the multi-level liquid and solid lines of the wastewater treatment system. At each level, one technology unit is selected out of a set of options which includes the empty unit. The DMF provides a comparison on possible technologies for screening which types of technologies may best be further developed in order for an urban wastewater infrastructure to be judged progressively more sustainable. The results indicate that one or a pair of technologies are dominant in each level. The cheap, lower-technology unit processes receive a mixed review. Some of them are selected as the most promising technology units while the others are not considered as good candidates. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savings of this new algorithm versus the traditional constraint method increase dramatically when the number of objectives increases. A real world case study of multiobjective optimal design of a best available control technology for Nitrogen Oxides (NOx) and Sulfur Oxides (SOx) reduction illustrates the usefulness of this approach. [ABSTRACT FROM AUTHOR]

This article addresses the problem of finding an optimal allocation of funds among different asset classes in a robust manner when the estimates of the structure of returns are unreliable. Instead of point estimates used in classical mean-variance optimization, moments of returns are described using uncertainty sets that contain all, or most, of their possible realizations. The approach presented here takes a conservative viewpoint and identifies asset mixes that have the best worst-case behavior. Techniques for generating uncertainty sets from historical data are discussed and numerical results that illustrate the stability of robust optimal asset mixes are reported. [ABSTRACT FROM AUTHOR]

We consider capacity management with a long-term strategic choice, such as the number of production lines to install before demand is known, and short-term tactical decisions relating to production, inventory, and subcontracting (recourse actions made after demand is known). We present an integrated scenario-based mathematical modeling and solution framework. For a single-product environment, we examine properties of total profit as a function of demand and the long-term capacity z. We investigate two measures of risk (profit variance and mean downside risk) and their corresponding profit-risk frontiers. Computational experiments are used to illustrate parameter sensitivity results obtained from the model. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the effect of parameter uncertainty on process design and operation is essential for optimal and feasible operation of a process plant. Existing approaches dealing with uncertainty in the design and process operations level assume the existence of a well defined model to represent process behavior and in almost all cases convexity of the involved equations. However, most of the realistic case studies cannot be described by well characterised models. Thus, a new approach is presented in this paper based on the idea of High Dimensional Model Reduction technique which utilize a reduced number of model runs to build an uncertainty propagation model that expresses process feasibility. Building on this idea a systematic iterative procedure is developed for design under uncertainty with a unique characteristic of providing parametric expression of the optimal objective with respect to uncertain parameters. The proposed approach treats the system as a black box since it does not rely on the nature of the mathematical model of the process, as is illustrated through a number of examples. [ABSTRACT FROM AUTHOR]

Design of controllable batch processes can be more challenging than continuous processes because of their unsteady nature of operation. The operating strategy of a batch process is characterized by trajectories of manipulated variables. This precludes the use of conventional controllability measures in evaluating the controllability of a given batch process design. Short process development cycles typically accredited to batch processes lead to uncertainty in the model formulation. Integrated approach to batch process design and control addresses the problem of controllability of a batch process during the design phase. This is best achieved by treating the problem as a dynamic optimization problem with time invariant (design) and time variant (operating) variables. The method proposed in this paper uses the decomposition feature of Generalized Benders Decomposition (GBD) to evolve a 2-level nested optimization problem (primal and master), one involving time variant decision (operating) variables and the other involving time invariant decision (design) variables. To enhance the computational efficiency, a relaxed LP formulation of the master problem is proposed. This variant of GBD, termed as ExGBD, is guaranteed to converge to the optimum for convex problems. A simple batch reactor design problem has been chosen to demonstrate ExGBD. [ABSTRACT FROM AUTHOR]

Logistics planning in emergency situations involves dispatching commodities (e.g., medical materials and personnel, specialised rescue equipment and rescue teams, food, etc.) to distribution centres in affected areas as soon as possible so that relief operations are accelerated. In this study, a planning model that is to be integrated into a natural disaster logistics Decision Support System is developed. The model addresses the dynamic time-dependent transportation problem that needs to be solved repetitively at given time intervals during ongoing aid delivery. The model regenerates plans incorporating new requests for aid materials, new supplies and transportation means that become available during the current planning time horizon. The plan indicates the optimal mixed pick up and delivery schedules for vehicles within the considered planning time horizon as well as the optimal quantities and types of loads picked up and delivered on these routes. In emergency logistics context, supply is available in limited quantities at the current time period and on specified future dates. Commodity demand is known with certainty at the current date, but can be forecasted for future dates. Unlike commercial environments, vehicles do not have to return to depots, because the next time the plan is re-generated, a node receiving commodities may become a depot or a former depot may have no supplies at all. As a result, there are no closed loop tours, and vehicles wait at their last stop until they receive the next order from the logistics coordination centre. Hence, dispatch orders for vehicles consist of sets of “broken” routes that are generated in response to time-dependent supply/demand. The mathematical model describes a setting that is considerably different than the conventional vehicle routing problem. In fact, the problem is a hybrid that integrates the multi-commodity network flow problem and the vehicle routing problem. In this setting, vehicles are also treated as commodities. The model is readily decomposed into two multi-commodity network flow problems, the first one being linear (for conventional commodities) and the second integer (for vehicle flows). In the solution approach, these sub-models are coupled with relaxed arc capacity constraints using Lagrangean relaxation. The convergence of the proposed algorithm is tested on small test instances as well as on an earthquake scenario of realistic size. [ABSTRACT FROM AUTHOR]

In many production/inventory systems, not only is the production/inventory capacity finite, but the systems are also subject to random production yields that are influenced by factors such as breakdowns, repairs, maintenance, learning, and the introduction of new technologies. In this paper, we consider a single-item, single-location, periodic-review model with finite capacity and Markov modulated demand and supply processes. When demand and supply processes are driven by two independent, discrete-time, finite-state, time-homogeneous Markov chains, we show that a modified, state-dependent, inflated base-stock policy is optimal for both the finite and infinite horizon planning problems. We also show that the finite-horizon solution converges to the infinite-horizon solution. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mixed MTS/MTO policy to manage a single manufacturing facility producing two classes of end-products. A few end-products have high volume demands, whereas a fairly large number of end-products have low volume demands. In this situation, it is appealing to try to produce the high volume products according to an MTS policy and the low volume products according to an MTO policy. The purpose of this paper is to analyze and compare the impact of the choice of the scheduling policy on the overall performance of the system. We consider two policies: the classical FIFO policy and a priority policy (PR). The PR policy gives priority to production orders corresponding to low volume products over production orders corresponding to high volume products. Under some simple stochastic modeling assumptions, we develop analytical/numerical solutions to optimise each system. We then provide insights regarding this issue with the help of numerical examples. It appears that for some range of parameters, the PR rule can outperform the FIFO rule in the sense that, to achieve the same service level constraint, the corresponding cost under the PR rule is much lower. This situation is encountered when the low volume products can be managed with an MTO policy under the PR scheduling rule, while they have to be managed according to an MTS policy under the FIFO scheduling rule. We also derive some theoretical properties that support our empirical findings. [ABSTRACT FROM AUTHOR]

Advance demand information, when used effectively, improves the performance of production/inventory systems. In this paper, we investigate the value of advance demand information in production/inventory systems. For a single-stage make-to-stock queue, we assess the value of using advance demand information under a variety of assumptions on the cost of obtaining advance demand information, and the delivery timing requirements. This analysis enables us to identify conditions under which advance demand information may bring significant benefits. [ABSTRACT FROM AUTHOR]

One of the major features of Japanese manufacturing techniques is the extensive use of teams, i.e., workers who as a group work together to produce sub-assemblies or finished products, or, alternatively, work together to accomplish a project. Many naive promoters of the team concept assume that the team members work together on the same tasks and activities and somehow accomplish them faster and better. However, observation of actual teams indicate that, unless a team member is new and quite inexperienced, teams split the required tasks or activities up so that each member has a unique task and most of the time members work independently. This paper develops models that try to describe the features of division of labour in teams and the way in which differences in capability between different team members impact performance. The models suggest that the advantage of teams probably lies primarily in the structure that enables faster/better workers to help out others. [ABSTRACT FROM AUTHOR]

In this paper we propose a unified formulation based on (min ,+) algebra to express the dynamics of pull control policies for serial single product manufacturing systems. For policies such as basestock, kanban, extended kanban and generalized kanban, the formulation has the same parametric form with different parameters for each policy. To calculate these parameters efficiently, (min ,+) algebra tools are used. This formulation allows us to identify under what parameter values two different policies have the same dynamics behavior. This has been applied to extended kanban and generalized kanban. [ABSTRACT FROM AUTHOR]

We consider a decentralized, pull-type manufacturing system with each stage having its own input and output stock keeping activities. Material handling between stages is carried out according to a fixed quantity, non-constant withdrawal cycle. We approximate the system behavior using a two-node decomposition approach, which decomposes the system into smaller subsystems. The analysis of two-node subsystems is achieved using a matrix-recursive approach due to phase-type modeling of certain random variables. Our solution algorithm resolved a major difficulty (due to batch transfers) in the analytical approach to study multi-stage manufacturing systems. We also discuss system behavior and suggest several rules-of-thumb to improve system performance. [ABSTRACT FROM AUTHOR]

In planning and managing production systems, manufacturers have two main strategies for responding to uncertainty: they build inventory to hedge against periods in which the production capacity is not sufficient to satisfy demand, or they temporarily increase the production capacity by “purchasing” extra capacity. We consider the problem of minimizing the long-run average cost of holding inventory and/or purchasing extra capacity for a single facility producing a single part-type and assume that the driving uncertainty is demand fluctuation. We show that the optimal production policy is of a hedging point policy type where two hedging levels are associated with each discrete state of the system: a positive hedging level (inventory target) and a negative one (backlog level below which extra capacity should be purchased). We establish some ordering of the hedging levels, derive equations satisfied by the steady-state probability distribution of the inventory/backlog, and give a more detailed analysis of the optimal control policy in a two state (high and low demand rate) model. [ABSTRACT FROM AUTHOR]

We study a manufacturing firm that builds a product to stock to meet a random demand. If there is a positive surplus of finished goods, customers make their purchases without delay and leave. If there is a backlog, the customers are sensitive to the quoted lead time and some choose not to order if they feel that the lead time is excessive. A set of subcontractors, who have different costs and capacities, are available to supplement the firm's production capacity. We derive a feedback policy that determines the production rate and the rate at which the subcontractors are requested to deliver products. The performance of the system, when it is managed according to this policy, is evaluated. [ABSTRACT FROM AUTHOR]

This paper considers a failure-prone manufacturing system with exponentially distributed operational times and constant repair times. Under the hedging-point policy, the steady-state probability density function of the buffer level is determined. The optimality of the hedging-point policy for this problem is proven, and analytical expressions for the computation of the optimal hedging point are determined. It is also shown that the optimal hedging point for the system with constant repair times provides a lower bound for the optimal hedging point for any other independent, identically distributed (iid) repair time sequence having the same mean. Using a queueing equivalence, an expression is derived for determining the optimal hedging point for any iid repair time sequence. [ABSTRACT FROM AUTHOR]

A model that selects and produces products at an equilibrium point in multi-unit manufacturing systems is presented. It enables each business subsystem in the company to select and produce products so that the whole organization meets both internal and external demands at minimum inventory cost. Unlike previously proposed models, this model does not place any restrictions on the number of products or services that each business subsystem can provide. Proofs are provided to show existence of solutions and solvability, and a numerical example is given to demonstrate the utility of the model. [ABSTRACT FROM AUTHOR]

The Sequential Ordering Problem (herewith, SOP) with precedence relationships was introduced in Escudero (1988), and extended lo cover release and due dates in Escudero and Sciomachen (1993). It has a broad range of applications, mainly in production planning for manufacturing systems. The problem consists of finding a minimum weight Hamiltonian path on a directed graph with weights on the nodes and the arcs, satisfying precedence relationships among the nodes and given lower and upper bounds on the weights of the Hamiltonian subpaths. In this paper we present a model for the constrained minimum weight Hamiltonian path problem with precedences and due dates forcing constraints, and introduce related valid cuts that can be used in a separation framework for the dual (Lagrangian based) relaxation of the problem. We also provide an heuristic separation procedure lo obtain those cuts, so-called the Lagrangian Relax-and- Cut (LRC) scheme. Computational experience is given for variations of some SOP cases already reported in the literature. [ABSTRACT FROM AUTHOR]

Large production variations caused by abnormal disturbances can significantly reduce the production capacity of a flexible manufacturing system (FMS). To prevent production delays, short-term capacity adjustment strategies can be used to augment the capacity of the FMS, such as working overtime, using alternative tools that are suited for faster processing, and producing parts outside of the FMS. We propose a mixed integer programming (MIP) model to obtain an optimal production plan for a multi-machine FMS. Our model evaluates both the FMS loading decision and the effective use of short-term capacity adjustment strategies to minimize the total part production cost. We develop an iterative procedure to solve the model that uses the Lagrangian relaxation method for finding lower bounds and a Lagrangian heuristic for obtaining feasible solutions. The procedure exploits certain special structures found in the Lagrangian multipliers which enable us to obtain good solutions to reasonably large test problems quickly. [ABSTRACT FROM AUTHOR]

In this paper we develop a model to examine the role of quantity discounts in synchronizing production and order cycles for a system consisting of one manufacturer and two heterogeneous buyers. The model developed can serve as a guideline for the manufacturer and the buyers as how to integrate their decisions to achieve higher benefits. The numerical examples indicate that the benefit of synchronization decreases with the degree of heterogeneity of the buyers. The conditions under which it is profitable for the manufacturer to synchronize with the heterogeneous buyers are demonstrated through numerical examples. [ABSTRACT FROM AUTHOR]

This paper considers design and operational issues that arise in repetitive manufacturing systems served by automated guided vehicles (AGVs) in loop layouts with unidirectional material flow. Such systems are in widespread industrial use, and play an important role in modem manufacturing environments. The objective considered is the minimization of AGV fleet size, given the minimum steady state cycle time required to produce a minimal job set (or equivalently, given the maximum throughout rate). We also study whether the decomposition of a large AGV-served flowshop loop into several smaller loops improves productivity. The original loop and the decomposed design are compared with respect to the minimum cycle time needed for the repetitive manufacture of a minimal Job set. When there are three or more machines in the loop, finding the optimal cycle time is an intractable problem. We therefore use the genetic algorithm developed in the companion paper [17], lo identify whether the original or the decomposed design is more efficient. Our study suggests that many systems perform more productively as a result of decomposition. Finally, we discuss a joint sequencing issue that arises in decomposed systems with limited buffers between the loops, and analyze the tractability of all the relevant joint sequencing problems. [ABSTRACT FROM AUTHOR]

We consider a production planning problem in a two-machine flowshop subject to breakdown and repair of machines and subject to nonnegativity and upper bound constraints on work-in-process. The objective is to choose machine production rates over time to minimize the long-run average inventory/backlog and production costs. For sufficiently large upper bound on the work-in-process, the problem is formulated as a stochastic dynamic program. We then establish a verification theorem and a partial characterization of the optimal control policy if it exists. [ABSTRACT FROM AUTHOR]

Production planning in manufacturing industries is concerned with the determination of the production quantities (lot sizes) of some items over a time horizon, in order to satisfy the demand with minimum cost, subject to some production constraints, In general, production planning problems become harder when different types of constraints are present, such as capacity constraints, minimum lot sizes, changeover times, among others. Models incorporating some of these constraints yield, in general, NP-hard problems. We consider a single-machine, multi-item lot-sizing problem, with those difficult characteristics. There is a natural mixed integer programming formulation for this problem. However, the bounds given by linear relaxation are in general weak, so solving this problem by LP based branch and bound is inefficient. In order to improve the LP bounds, we strengthen the formulation by adding cutting planes. Several families of valid inequalities for the set of feasible solutions are derived, and the corresponding separation problems are addressed. The result is a branch and cut algorithm, which is able to solve some real life instances with 5 items and up to 36 periods. [ABSTRACT FROM AUTHOR]

There exists a considerable literature on reliability growth modelling. Recently some common models have been extended to encompass innovation. They assume the time of an innovation is known and that there is a coincidental improvement in performance. However, despite such developments, it remains that most models do not fully address the engineering concerns as they do not capture the underlying physical processes and they tend to be optimistic about ensuing performance. This paper aims to address these issues by specifying a general framework for reliability growth that supports more effective modelling. Further, we develop a strategy for using such models proactively during development to facilitate meaningful improvements in reliability performance. [ABSTRACT FROM AUTHOR]

A Markov decision model is presented to determine the optimal maintenance policy for a general production unit. The unique characteristic of the proposed model is the inclusion of the unit's throughput rate as a decision variable. We show that under appropriate assumptions, the generator matrix of the underlying Markov chain that models the unit's deterioration process exhibits a separability property which significantly facilitates the solution procedure. [ABSTRACT FROM AUTHOR]

We examine periodic pacing in a multi-stage non-synchronous production system where production stages are distributed over various locations. The processing rates and inspection failure rates at each stage are random. The optimal policy for pacing (e.g., buffering) the flow of in-process inventory between stages is pursued to minimize operational costs over a finite horizon. It is known that the optimal control is extremely complex in general. However, separability across the stages is shown under weighted penalties which results in a decentralized heuristic pacing. Numerical comparisons of heuristic and optimal solutions are reported. We obtained sufficient conditions under which the heuristic pacing is finite. As a result, an efficient algorithm is developed for the design of inter-stage buffers. [ABSTRACT FROM AUTHOR]

The objective of this article is to present an expository overview of our recent studies on developing dynamic approaches to quality control in batch manufacturing. The approaches are dynamic in the sense that they are concerned with finding policies or controls, instead of merely parametric or statistical designs. In particular, we emphasize identifying certain optimal sequential procedures and revealing their structural properties. We shall review our studies on the following issues: (i) coordination between the inspection-repair of finished products and their follow-up services; (ii) coordination between inspection of different component parts in assembly lines; (Hi) coordination between inspection and process revision; and (iv) inter-stage coordination of the inspection processes at different stages, under capacity constraints, and between production and inspection. The basic methodology underlying our approaches is Markov decision programming (MDP). We also incorporate into MDP the usage of stochastic comparison techniques, including those based on notions of stochastic convexity and stochastic submodularity. These properties play a crucial role in revealing the structures of the policies and in proving their optimality. [ABSTRACT FROM AUTHOR]

A real-time hierarchical routing control scheme for a large class of material handling systems is presented. The higher level (coordinator) performs resource allocation tasks and supplies parameter values to the lower (local control) level. The lower level operates in an autonomous (without continuous supervision) and distributed fashion. If state information is made available to the coordinator, the routing strategy can furthermore be adaptively adjusted. [ABSTRACT FROM AUTHOR]

In this paper we present an approach for modelling and analyzing flexible manufacturing systems (FMSs) using Petri nets. In this approach, we first build a Petri net model (PNM) of the given FMS in a bottom-up fashion and then analyze important qualitative aspects of FMS behaviour such as existence/absence of deadlocks and buffer overflows. The basis for our approach is a theorem we state and prove for computing the invariants of the union of a finite number of Petri nets when the invariants of the individual nets are known. We illustrate our approach using two typical manufacturing systems: an automated transfer line and a simple FMS. [ABSTRACT FROM AUTHOR]

Unless procedures for the economic evaluation of flexible manufacturing technology axe revamped, much of U.S. industry could repeat the experience of American automobile and steel companies from the recent past. The dynamic character of flexible manufacturing technology in terms of associated developments in microelectronics, a trend toward equipment modularity, and the increasing diversity of relevant markets make flexible manufacturing especially vulnerable to the weaknesses of conventional procedures for the justification of capital expenditures. Building on the work of V. Smith, M.I. Kamien and N.L. Schwartz, this paper presents optimization models from which economically, reasonable plans can be derived for the implementation of flexible manufacturing technology. Although the assumptions of these models are not focused on the situation of a specific plant, model implications and the overall perspectives here on the economic evaluation of flexible system options should provide general guidance for manufacturing planners. [ABSTRACT FROM AUTHOR]

The recent perturbation analysis approach to discrete event systems is applied to flexible manufacturing systems (FMS). While analytic (queueing) models are useful in preliminary design of such systems, they are not accurate enough at the detailed design/operation stage. Thus, experimentation on detailed simulations or on the actual system has been the way to optimize system performance. Perturbation analysis allows us to derive the sensitivity of system performance, with respect to several design/operating parameters, by observing a single experiment (and without having to actually alter the parameters — often a costly operation). Thus, observation of one experiment can give accurate directions for the improvement of several parameter values. Here we give a simulation example illustrating how perturbation analysis could be used on-line on an FMS to improve its performance, including reducing its operating cost. Experimental results are also presented validating the estimates obtained from this technique. [ABSTRACT FROM AUTHOR]

A new methodology for performance analysis of flexible manufacturing systems (FMSs) with priority scheduling is presented. The analytic model developed extends the mean value analysis of closed networks of queues with multiple product types, various non-preemptive priority service disciplines, and with parallel machine stations. Performance measures derived include the expected throughput per product and per station, utilization of machines and transporters, queuing times and queue length measures for various configurations. Extensive numerical calculations have shown that the algorithm used for solving the problem converges rapidly and retains numerical stability for large models. The paper also illustrates the application of the model to a system with a mixture of FCFS and HOL disciplines which gives insights into various priority assignment policies in FMSs. Special attention was given to the problem of scheduling the robot carriers (transporters). [ABSTRACT FROM AUTHOR]

In this paper, we describe a flow model of an automated-printed circuit card assembly line at the International Business Machines Corporation (IBM) plant at Tucson, Arizona. We use a simulation based on this model as a test bed to discuss the performance of a hierarchical scheduling policy described in [3]. We compare this with other policies for loading parts into a flexible manufacturing system. We demonstrate that the hierarchical strategy is effective in meeting production requirements (both total volume and balance among part types) while limiting average work-in-process (WIP). This is a consequence of the feedback nature of the policy. Hedging (i.e. building up buffer stock) compensates for machine failures, thus resulting in high production percentages. The work-in-process (WIP) is low, as the policy reduces internal queues by respecting the capacity constraints of the system at every instant. [ABSTRACT FROM AUTHOR]

This paper introduces an analysis and optimization technique for discrete event dynamic systems, such as flexible manufacturing systems (FMSs), and other discrete part production processes. It can also be used for enhancement of the simulation results of, or the monitoring of the operations of such systems in real time. Extensive references are given where readers may pursue futher details. [ABSTRACT FROM AUTHOR]

This paper presents a new two-phase (TP) approximate method for real-time scheduling in a flexible manufacturing system (FMS). This method combines a reduced enumeration schedule generation algorithm with a 0–1 optimization algorithm. In order to make the combined algorithm practicable, heuristic rules are introduced for the selection of jobs to be scheduled. The relative performance of the TP method vis-a-vis conventional heuristic dispatching rules such as SPT, LPT, FCFS, MWKR, and LWKR is investigated using combined process-interaction/discrete-event simulation models. An efficient experimental procedure is designed and implemented using these models, and the statistical analysis of the results is presented. For the particular case investigated, the conclusions are very encouraging. In terms of mean flow time, the TP method performs significantly better than any other tested heuristic dispatching rules. Also, the experimental results show that using global information significantly improves the FMS performance. [ABSTRACT FROM AUTHOR]

Flexible manufacturing is characterized by versatile work stations with minimum change over times and a versatile material handling system. The loading problem in flexible manufacturing is to assign tools, material, operations and jobs to work stations in order to minimize the total number of job-to-work station assignments. In this paper, we describe a special case of the general loading problem applied to flexible assembly and develop a discrete optimization model. We then discuss approaches for obtaining good heuristic solutions and present results for a large scale study. [ABSTRACT FROM AUTHOR]

Parts grouping into families can be performed in flexible manufacturing systems (FMSs) to simplify two classes of problems: long horizon planning and short horizon planning. In this paper the emphasis is on the part families problem applicable to the short horizon planning. Traditionally, parts grouping was based on classification and coding systems, some of which are reviewed in this paper. To overcome the drawbacks of the classical approach to parts grouping, two new methodologies are developed. The methodologies presented are very easy to implement because they take advantage of the information already stored in the CAD system. One of the basic elements of this system is the algorithm for solving the part families problem. Some of the existing clustering algorithms for solving this problem are discussed. A new clustering algorithm has been developed. The computational complexity and some of the computational results of solving the part families problem are also discussed. [ABSTRACT FROM AUTHOR]

Batching and balancing constitute two of the several scheduling and resource allocation problems important over different time scales in FMS operations. The time scale for batching and balancing is of the order of days to weeks — which puts it between long-term planning and part selection, of the order of months to years, and real-time part entry and dispatching, of the order of minutes. All these scheduling and resource allocation problems have been addressed at the Charles Stark Draper Laboratory by a common methodology. We have used a sequential decision algorithm, each decision based on optimization of a probabilistic performance criterion designed to trade off whatever and however many competing attributes may be important in the particular problem. This paper discusses the common methodology, and its application to FMS batching and balancing in particular. Our computer program BATCH/BAL which implements the algorithms given here has been the starting point for all the other sequential decision programs subsequently developed at our laboratory. [ABSTRACT FROM AUTHOR]

Both analytic and simulation models were used to analyze the capabilities and requirements of an automated circuit card manufacturing system. Analytic models were used to determine the sensitivity of the measures of effectiveness (MOEs) to various design parameters. This analysis gave approximate results and bounded the range of input parameters for the simulation model. A detailed simulation model was required for use during both the design and production phases of the project. This simulation model incorporated only those variables to which the MOEs are most sensitive, and provided additional features to observe system behavior. The benefits and appropriate uses for each class of models are discussed. [ABSTRACT FROM AUTHOR]