This paper reviews the literature on quantitatively-oriented approaches for determining lot sizes when production or procurement yields are random. We discuss issues related to the modeling of costs, yield uncertainty, and performance in the context of systems with random yields. We provide a review of the existing literature, concentrating on descriptions of the types of problems that have been solved and important structural results. We identify a variety of shortcomings of the literature in addressing problems encountered in practice, and suggest directions for future research.
We address the problem of sequencing jobs, each of which is characterized by one of a large number of possible combinations of customer-specified options, on a paced assembly line. These problems arise frequently in the automotive industry. One job must be launched into the system at equal time intervals, where the time interval (or cycle time) is prespecified. The problem is to sequence the jobs to maximize the total amount of work completed, or equivalently, to minimize the total amount of incomplete work (or work overload). Since there is a large number of option combinations, each job is almost unique. This fact precludes the use of existing mixed model assembly line sequencing techniques. We first consider the sequencing problem for a single station which can perform two different sets of operations. We characterize the optimal solution for this problem and use the results as the basis for a heuristic procedure for multiple stations. Computational results with data from a major automobile company are reported.assembly lines, sequencing, dynamic programming, heuristics
It has been nearly 50 years since researchers began to develop analytical models to aid in simultaneous decisions regarding pricing strategy, which influences demands, and production/procurement decisions, which determine the cost of satisfying those demands. In this chapter, we provide a comprehensive review of analytical models on this topic, focusing on models in which external demand is price-sensitive. We review models in both continuous and discrete-time frameworks, considering both constant and time-varying demand functions, with and without demand uncertainty. Although our emphasis is on decision problems facing a single firm, we also provide a brief overview of models involving multiple firms or multiple decision-makers. We also offer suggestions for future research.
We investigate the problem of determining optimal planned leadtimes in serial production systems in which the actual procurement and processing times may be stochastic. The objective is to minimize the sum of inventory holding costs and job tardiness costs given a customer specified due-date. We present a general solution procedure for two stage serial systems, which for most cost structures and leadtime distributions is a single-pass algorithm. We also indicate how the procedure can be extended to N-stage systems. We present computational results which provide some insight into the characteristics of optimal safety time policies.inventory/production, multi-echelon, leadtime policies, stochastic model
Many manufacturing processes involved in the fabrication and assembly of “high-tech” components have highly variable yields that complicate the planning and control of production. We develop a procedure to determine optimal input quantities at each stage of a serial production system in which process yields at each stage of production may be stochastic. The procedure is applied to an example in the manufacture of a light-emitting diode (LED) display using actual yield data. We also provide a brief analysis of the quantifiable savings obtained by reducing the variability of the yield at one production stage.
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