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We propose a novel mixed integer programming formulation for the capacitated lot-sizing problem with set-up times and set-up carryover. We compare our formulation to two earlier formulations, the Classical and Modified formulations, and a more recent formulation due to Suerie and Stadtler. Extensive computational experiments show that our formulation consistently outperforms the Classical and Modified formulations in terms of CPU time and solution quality. It is competitive with the Suerie-Stadtler (S&S) formulation, but outperforms all other formulations on the most challenging instances, those with low-capacity slack and a dense jobs matrix. We show that some of the differences in the performance of these various formulations arise from their different use of binary variables to represent production or set-up states. We also show that the LP relaxation of our Novel formulation provides a tighter lower bound than that of the Modified formulation. Our experiments demonstrate that, while the S&S formulation provides a much tighter LP bound, the Novel formulation is better able to exploit the intelligence of the CPLEX solution engine. [ABSTRACT FROM AUTHOR]

A fundamental scheduling problem is to determine a production start (ready) time for jobs based on customer-specified due dates. Typically, the objective is to delay the ready time in an attempt to minimize work-in-process inventory and maximize production system utilization. In many practical situations, notably remanufacturing operations, highly variable operation times and intricate process plans complicate this problem. In such a case, a specific ready time implies a confidence of on-time completion. Prior analytical results imply the optimal solution is a function of: (i) customer due dates; (ii) desired confidence levels; and (iii) stochastic makespan minimization. This paper proposes the use of a network flow model to represent a remanufacturing flowshop and then presents a structured heuristic approach that is able to develop sequences and ready times for remanufacturing systems by balancing the three factors. A broad experimental design is used to demonstrate that the proposed scheduling method outperforms previous list rules in terms of the calculated mean and robustness values., 1. Introduction The current emphasis on sustainable growth, which advocates present economic growth without diminishing future resources, has driven the increased reuse of manufactured products. Emerging forms of product reuse [...]

Effective sequencing and scheduling of the material handling system can have a major impact on the productivity of the manufacturing system. This is especially true in the case where material handling times are on par with machine processing times. In a dynamic, real-time environment, the optimal solution of this scheduling problem may be computationally infeasible. In this paper, we develop a branch and bound approach which is coupled with quick, effective bounds to optimize the movement of a robot which serves the material handling requirements within a manufacturing cell. Computational results are given which explore the tradeoff between computation time and deviation from optimal for different scenarios.

Approaches to multistage production scheduling can be conveniently classified into push type (i.e., Materials Requirements Planning (MRP) systems) or pull type (i.e., kanban systems). Each is generally thought to have both advantages and disadvantages. In this paper, a hybrid push/pull strategy is developed with the aim of gaining the advantages of both approaches. Material flow between work centers is regulated using the standard single card kanban/pull arrangement. Superimposed on this is the MRP-TM information flow which feeds forward demand information directly to one or more (but not necessarily all) work centers. A general N-stage hybrid push/pull model is developed. The use of the approach is illustrated using 3-stage and 4-stage serial flowlines. The results indicate that the push/pull approach has lower inventory levels and a better response to demand changes than the pure pull system. The hybrid approach seems to combine many of the advantages of MRP approaches while retaining much of the simplicity of kanban/pull systems.

The Economic Lot Scheduling Problem is a classical scheduling problem with the objective of minimising the long-run inventory and set-up costs of a single machine, multi-product inventory system. Demand rates, production rates and set-up times are assumed to be deterministic. The problem has been extensively studied and methods exist to obtain cyclic schedules which minimise total cost. In order to apply a cyclic schedule without a stock-out occurring, certain initial inventory levels are required. This paper considers the scenario where inventory levels are critically low and the cyclic schedule cannot be applied. Analytical methods are developed to determine: if the initial inventory is sufficient to prevent a stock-out from occurring; the maximum time until a stock-out will occur if a stock-out is unavoidable; and the minimum time required to obtain the necessary inventory to resume the cyclic schedule if a stock-out can be avoided. [ABSTRACT FROM AUTHOR]

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army's adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk. [ABSTRACT FROM AUTHOR]

This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a 'forecasting' mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies. [ABSTRACT FROM AUTHOR]