Dynamic lot size/sequencing policies in a multi-product,single-machine system

This paper is concerned with the modeling and analysis of a single-machine, multi-product, integrated production–inventory system. A semi-Markov model is developed that incorporates a state-dependent control system. Control optimization is achieved through the use of Markov-renewal programming. Three- and four-product models are developed. The model(s) are exercised over a range of parameters in order to determine the characteristics of the system, and to identify the structure of the optimal control rules of the system. The result is that one can identify simple basic rules of operation that are near-optimal in a dynamic environment, and that make sense intuitively.     

The effects of routing flexibility in a multi-stage pull-type system

The pull-type manufacturing system is known for its need for a frozen schedule, balanced work load and inability to handle fluctuations in demand. But today research and development are addressing ways to handle a pull-type system to accommodate greater fluctuations. One approach is to design the system with machines that have a greater routing flexibility in what work they can perform. However, most of the existing literature dealing with routing flexibility has primarily been devoted to the push-type production systems. This research examined the effects of routing flexibility for a multi-stage kanban-controlled pull-type system.     

Flowshops with processing flexibility across production stages

This paper addresses a scheduling problem for a two stage hybrid flowshop (HFS) environment with significant processing flexibility across stages. A job can be either completely processed at one of the stages or its processing is split in a prespecified fashion between stages. For the latter case, the routing is only in one direction, from the 'upstream' stage to the 'downstream' stage. A stage may consist of one or more parallel identical machines. We first study the special case of a single machine per stage and present a pseudopolynomial dynamic programming algorithm to minimize makespan. Then, we provide a heuristic for the HFS_m1,m2 problem with m_k machines at stage k = 1,2, and prove its worst case and average performance. Also, we extend our algorithms to the case of stage dependent processing times. Finally, we report computational results on the average performance of HFS_m1,m2 with comparable flowshop and parallel machine environments, and provide managerial guidelines for process choice decisions.     

Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies

This article presents initial results in the search for analytical models that can predict the performance of one-machine systems under periodic and event-driven rescheduling strategies in an environment where different job types arrive dynamically for processing and set-up must incur when production changes from one product type to another. The scheduling algorithm considered uses a first-in firstout dispatching rule to sequence jobs and it also groups jobs with similar types to save set-up time. The analytical models can estimate important performance measures like average flow time and machine utilization, which can then be used to determine optimal rescheduling parameters. Simulation experiments are used to show that the analytical models accurately predict the performance of the single machine under the scheduling algorithm proposed.     

Layout optimization considering production uncertainty and routing flexibility

The unequal area facility layout problem is studied considering both production uncertainty and routing flexibility. Stochastic production quantities are introduced under the assumption of dependent demands and incorporated into the usual objective of material handling cost minimization. Concurrently, alternative routings are considered based on production quantity. An efficient simulation approach is used to estimate the resulting department pairwise flows, both their expected values and variances. Using these estimates, a tabu search heuristic procedure performs the layout design optimization.     

Quantifying the advantages of production quota flexibility in the automotive industry

Manufacturers use production quotas to smooth and synchronize production. We use a Markov chain to quantify the effect of quota flexibility on overtime and inventory costs. This quantification allows us to trade off these costs to find the optimal amount of quota flexibility. We define quota flexibility as the permitted amount and duration of deviations from the cumulative quota. We specifically analyse the cost savings as a function of the maximum allowed deviation amount from the cumulative quota for four policies. These example policies are the four permutations of two factors: whether or not backlogging is permitted; and whether or not actual production and the quota must be reconciled weekly. We apply the model to three automotive assembly plants and conclude that permitting modest flexibility in production quotas (of ? 1 h of production) significantly reduces total cost.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

Factory design and process optimisation with flexibility measurements in industrial production

Manufacturing companies operate in increasingly turbulent and discontinuous environments. Uncertain projections regarding future sales, large volume fluctuations, the steady advance of new technologies, and ever-shorter product life cycles, combined at the same time with increasing product variety, demand the constant adjustment of production structures to meet the current market requirements. Uncertainty in the production area requires a continuous transformation of production structures to meet the current requirements on the market. This poses a challenge for manufacturers in the various branches of industry and creates an ever-increasing need for flexibility to find an economic balance, in times of tight budgets, between the prevailing market uncertainties and the correct arrangement of the production systems. In the following document, the applied experience is presented, extracted from the flexibility assessment tool economical flexibility measurement (ecoFLEX). It will be shown along with the practical experience gained in its application at a car-engines plant.     

Strategies for evaluating performance of flexibility in product recovery system

In view of the increasing business opportunities with changing customer attitudes and stricter legislations, the handling of returns has become a daunting challenge. The need for decision models for evaluating return performance has been observed in the academia and the corporate world. To improve return system performance, integrated flexible reverse enterprise systems have attracted attention from researchers as well as practitioners. This paper addresses these critical issues and proposes a novel integrated and Flexible recovery system decision model. The proposed model aims to facilitate enterprises in assessing their product recovery system capability, and in improving overall performance. The proposed model is a natural extension of several well-grounded policies for conventional reverse supply chains and can be verified on a simulation platform.     

A large-scale distributed decision-making procedure for a single-machine scheduling problem

This paper presents a distributed scheduling procedure for a large-scale single-machine problem with precedence constraints, and identifies phenomena using large-scale distributed decision-making for a decomposable and distributed situation. The approach proposed exhibits efficient computational performance over a large-sized work load (23,000 jobs and 2,209,536 precedence constraints) in a distributed computing environment. We highlight three findings: (1) the communication burden originating from the large-scale problem can lead to performance loss while distributed agents collaborate to solve the problem, but after a threshold the computational gain by distribution offsets the loss; (2) when the problem size is sufficiently large, the real computation gain outperforms the expected gain by the number of agents (super-linear effect); and (3) when the number of precedence constraints of each agent differs, the slowest agent processing the largest number of precedence constraints restrains the computational performance (load imbalance). We believe that our research is the first distributed decision-making model that meets the requirements of distributed information, distributed decision authority, and distributed computation in a large-scale single-machine scheduling situation with precedence constraints.     

Integrated maintenance and production control of a deteriorating production system

We consider a make-to-stock production/inventory system consisting of a single deteriorating machine which produces a single item. We formulate the integrated decisions of maintenance and production using a Markov Decision Process. The optimal dynamic policy is shown to have a rather complex structure which leads us to consider more implementable policies. We present a double-threshold policy and derive exact and approximate methods for evaluating the performance of this policy and computing its optimal parameters. A detailed numerical study demonstrates that the proposed policy and our approximate method for computing its parameters perform extremely well. Finally, we show that policies which do not address maintenance and production control decisions in an integrated manner can perform rather badly.     

Multi-stage lot sizing in a serial production system

This paper summarizes the development and implementation of a large-scale, multi-objective, lot-sizing model for scheduling tablet pharmaceuticals in a serial production system. The model places multiple resource capacity constraints on production at various stages of the manufacturing process and explicitly considers resource set-up times to ensure the generation of a feasible schedule. The multi-period model uniquely allows for variable lot sizes by stage and by period, subject to integrality restrictions arising from the technological nature of the multi-stage process. An integer goal programming approach is used for determining non-dominated solutions to the multi-item problem. The implementation and utilization of the model by an international manufacturer of pharmaceuticals is described, and model results are presented and discussed.     

On control strategies in a multi-stage production system

A multi-stage production system is composed of a set of stations, each station performing a given task, and a set of vehicles, each vehicle moving between two successive stations. A station can choose a buffer or a kanban mechanism for controlling the work-in-process (WIP) in the station. A vehicle can choose a push or a pull policy for carrying parts from its upstream station to its downstream station. A control strategy is formed by combining the WIP mechanisms adopted in all stations and the carrying policies employed by all vehicles. The production system is modelled as a queuing system. Some structural properties of performance measures are characterized. We develop a decomposition approach for large systems, which performs very well. We determine the optimal numbers of buffers or kanbans at all stations in the design period, and the optimal control strategy during operation. Many numerical computations are given for evaluating the efficiencies of the decomposition approach and optimization methods, and further providing some intuitions and insights.     

Economic batch quantity in a multi-stage production system

A mathematical model has been developed in this paper for determining the total annual variable cost for a product which requires processing on a number of production stages. The demand for the product is assumed uniform over time and it is manufactured in equal lot sizes. In order to minimize the manufacturing cycle time of o production lot, the movement of the items between production stages is in sub-batches of equal sizes, As a result of manufacturing a production lot in sub-batches, the following additional costs are incurred: (1) additional cost of stock holding for process inventory; (2) cost of transporting sub-batches; (3) cost of multiple set-ups. A simple method is then adopted for minimizing the total annual variable cost of the multistage production system. An example has been solved to illustrate the method.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

Optimal production planning for a multiproduct,multistage production system

A mathematical model of production planning for a multiproduct, multistage produetionaystom (production equation, transition equation and objective function) was constructed in order to derive the optimal decision rule to be adapted to demand fluctuation. With this model the optimal solution and the computational algorithm were determined by functional space approach. With use of this algorithm, a numerical example of optimal production planning was solved.     

Master production scheduling,customer service and manufacturing flexibility in an assemble-to-order environment

A key element of manufacturing planning and control involves the inter-functional coordination of various manufacturing requirements. This paper reports the results of a simulation experiment that compares alternative master production scheduling (MPS) procedures in an assemble-to-order environment. The MPS procedures are superbills and covering sets. For a given investment in safety stocks the managerial problem is how to construct the MPS in order to minimize the firm's delivery time pressures. The results of the simulation experiment strongly support the use of the superbill techniques over the covering set technique. Moreover, of the experimental factors that influence delivery time performance, the choice of master production scheduling technique has the largest effect. Demand variability has the next greatest effect on delivery time performance. The safely stock level has the third greatest effect and product commonality was fourth. Finally, the managerial implications of the results are discussed in detail.     

Make-to-order versus make-to-stock in a production–inventory system with general production times

We study the optimality of make-to-order (MTO) versus make-to-stock (MTS) policies for a company producing multiple heterogeneous products at a shared manufacturing facility. Manufacturing times are general i.i.d. random variables, and different products may have different manufacturing-time distributions. Demands for the products are independent Poisson processes with different arrival rates. The costs of managing the production-inventory system are stationary and include inventory holding and backordering costs. Backordering costs may be $ per unit or $ per unit per unit time. We derive optimality conditions for MTO and MTS policies. We also study the impact of several managerial considerations on the MTO versus MTS decision.     

A two-echelon inventory system with supply lead time flexibility

The impact of manufacturing flexibility on inventory investments in a distribution network consisting of a central depot and a number of local stockpoints is investigated. The lead time of outstanding orders in the pipeline of the central depot can be shortened by the use of flexibility. Stock levels are controlled by a periodic review echelon-order-up-to-policy under service level constraints.