Fleet sizing and empty equipment redistribution for center-terminal transportation networks

Article Abstract:

An alternative to complex mathematical programming models formulated for fleet sizing and empty equipment redistribution was developed. A decentralized equipment distribution policy was designed using rules resembling the (s, Q) policy in inventory control theory to achieve a balance among transportation equipment among locations. To apply this technique to hub-and-spoke networks, sometimes referred to as center-terminal networks, the stochastic processes that consider different stock-control variables are analytically modelled, the analytical results of which were compared to monte-carlo simulations. In addition, a decomposition approach was introduced to identify stock-out probabilities as a function of the fleet size as a whole and as a function of localized control variables. The results were robustly performing models that are easily understood with control parameters that can be computed offline.

Management, Industrial equipment, Transportation, Motor vehicle fleets, Fleets (Automotive), Transportation planning, Distribution of goods, Distribution (Commerce)

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Performance Bounds for Lot Sizing Heuristics

Article Abstract:

This paper deals with the classical dynamic lot size problem without backlogging and capacity limitation. We derive worst case performance bounds for a class of lot sizing heuristics. When the considered methods are applied a decision whether to have a set-up or not in a certain period is taken without regarding the future demand. It is shown that 2 is a lower bound for the worst case performance ratio for such heuristics. The results illustrate circumstances under which the approximate techniques fail. (Reprinted by Permission of Publisher.)

Business planning, Performance Measurement, Heuristic Methods, Worst-Case Analysis

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Learning Effects in Economic Lot Sizing

Article Abstract:

The square-root formula for the optimal lot size is useful. Its simplicity is the result of assuming production cost to be a linear function of the quantity producer. However, the production of labor-intensive products is subject to learning effects. Thus, there are nonlinear production costs. A model is developed that accounts for the learning effects in lot sizing. Graphs and tables of numerical results are included.

Manufacturing, Production control, Modeling, Data modeling software, Learning Curve, Scheduling, Scientific Research

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