Optimal buffer allocation in short μ-balanced unreliable production lines

In this work, we investigate the optimal buffer allocation in short μ-balanced production lines consisting of machines that are subject to breakdown. Repair times and times to failure are assumed exponential, whereas service times are allowed to follow the Erlang-k distribution (with k=1, 2, 4 and 8). By an improved enumeration procedure and applying the evaluative algorithm of Heavey et al. (European Journal of Operational Research 1993;68:69–89) for the calculation of throughput, we have examined in a systematic way several systems with 3, 4, 5, 6 and 7 stations and with a different total number of buffer slots. We have been able to give answers to some critical questions. These include the effect of the distribution of the service and repair times, the availability of the stations and the repair rates on the optimal buffer allocation and the throughput of the lines.     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

‘Best’ for whom?: the tension between ‘best practice’ ERP packages and diverse epistemic cultures in a university context

The idea that so-called ‘best’ business practices can be transferred to organizations when they purchase enterprise resource planning (ERP) software packages is a major selling point of these packages. Yet recent research has illustrated a gap between the espoused theory of a best practice solution and the theory-in-use experienced by those who install software with such a design. As researchers begin to examine the difficult process by which organizations recast the best practices model handed down to them by consultancies and software vendors in an effort to make the software ‘work for them’ in practice, it is equally important that we begin to understand the reasons that such a gap exists. To this end, we analyze the strategic partnership between a multinational software vendor and a university who together designed a ‘best practice’ ERP package for the higher education industry. Through the theoretical lens of ‘epistemic cultures’ we argue that in organizational contexts made up of more than one epistemic culture, the use of a best practice model will be problematic because, by definition, the model mandates one epistemological position through the software design. This is counter to a university's loosely coupled organizational form.     

Queueing-model based analysis of assembly lines with finite buffers and general service times

In this paper, we study the production process on multi-stage assembly lines. These production systems comprise simple processing as well as assembly stations. At the latter, workpieces from two or more input stations have to be merged to form a new one for further processing. As the flow of material is asynchronous with stochastic processing times at each station, queueing effects arise as long as buffers provide waiting room. We consider finite buffer capacities and generally distributed processing times. Processing is a service operation to customer items in the sense of a queueing system. The arrival stream of customer items is generated by processing parts at a predecessor station. This paper describes an approximation procedure for determining the throughput of such an assembly line. Exact solutions are not available in this case. For performance evaluation, a decomposition approach is used. The two-station subsystems are analyzed by G/G/1/N$G/G/1/N$ stopped-arrival queueing models. In this heuristic approach, the virtual arrival and service rates, and the squared coefficients of variation of these subsystems are determined. A system of decomposition equations which are solved iteratively is presented. Any solution to this system of equations indicates estimated values for the subsystems’ unknown parameters. The quality of the presented approximation procedure is tested against the results of various simulation experiments.     

Improvement of the evaluation of closed-loop production systems with unreliable machines and finite buffers

A closed-loop production system, or loop, is a system in which a constant amount of material flows through a single fixed cycle of workstations and storage buffers. Manufacturing processes that utilize pallets or fixtures can be viewed as loops. Control policies such as CONWIP and Kanban create conceptual loops. Gershwin and Werner (2007) developed a decomposition algorithm that accurately evaluates Buzacott type closed-loop systems of any size. However there are cases where the evaluated production rate, as a function of some system parameter, is discontinuous. Such a discontinuity may give misleading results for loop system design and optimization method. We present two modifications that improve the algorithm of Gershwin and Werner (2007). Two new special types of two-machine one-buffer building blocks are developed for the decomposition, and analytical solutions for them are found. Numerical experiments are provided to show the improvement of the evaluation accuracy as compared with the existing algorithm. The discontinuity in production rate is greatly diminished with these modifications.     

A continuous analysis framework for the solution of location–allocation problems with dense demand

Location–allocation problems arise in several contexts, including supply chain and data mining. In its most common interpretation, the basic problem consists of optimally locating facilities and allocating customers to facilities so as to minimize the total cost. The standard approach to solving location–allocation problems is to model alternative location sites and customers as discrete entities. Many problem instances in practice involve dense demand data and uncertainties about the cost and locations of the potential sites. The use of discrete models is often inappropriate in such cases. This paper presents an alternative methodology where the market demand is modeled as a continuous density function and the resulting formulation is solved by means of calculus techniques. The methodology prioritizes the allocation decisions rather than location decisions, which is the common practice in the location literature. The solution algorithm proposed in this framework is a local search heuristic (steepest-descent algorithm) and is applicable to problems where the allocation decisions are in the form of polygons, e.g., with Euclidean distances. Extensive computational experiments confirm the efficiency of the proposed methodology.