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Browsing by Author "Moore, L.J."

Now showing 1 - 3 of 3
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    Analysis of air cargo transport systems using stochastic simulation
    Green, Forrest B. (Virginia Polytechnic Institute and State University, 1982)
    A major problem associated with air cargo transport is the assignment and scheduling of aircraft to routes that include several transloading points. This problem is complicated by the fact that shipping quantities vary at each terminal from one day to the next, and there are often wide fluctuations in demand for high priority cargo. Rapid delivery requirements calling for frequent flights to maintain satisfactory service often result in over-assignment and excess capacity. The balancing of capacity and service is a significant problem for air freight carriers. The problem investigated was to develop a means of evaluating various combinations of aircraft and route schedules taking into account the frequency of flights and the stochastic nature of shipping quantities. Key performance and cost variables were identified, and shipping data were analyzed to determine distribution parameters. A computer simulation model called CARGOSIM was developed to represent the air transport system and provide a tool for the evaluation of various alternatives. The simulation model allows for the stochastic behavior of cargo quantities and the detection of shipment delays due to random surges in demand. Accordingly, both the extent to which assigned aircraft can transport available cargo and the level of service at each terminal are determined through simulation. The simulation model is used in conjunction with a heuristic designed to search through aircraft types and flight frequency combinations until a least-cost solution is found. The cost function includes both the cost of operating the air transport system and the cost of service delays, thus a balance is achieved between capacity and service when an efficient solution is obtained. This feature represents a decision framework designed so that successive iterations of the simulation model will lead to a least-cost solution within statistically determined margins of error.
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    An experimental investigation: uncertainty in MRP systems
    Grasso, Edward T. (Virginia Polytechnic Institute and State University, 1982)
    Material Requirements Planning (MRP) has evolved as a technique for the planning and controlling of inventories and production of complex manufactured products. The problem addressed in this research is that of uncertainty in MRP systems. Demand uncertainties are those which involve variations in the gross requirements for a component. Supply uncertainties deal with variations in the scheduled receipts for a component. The purpose of this research is to examine the impact of specific operating policies on the performance of an MRP system under conditions of supply/timing uncertainty. Supply uncertainties include either the timing or quantity type. Supply uncertainty that results because of timing involves the receipt of an order after its scheduled delivery date. The specific supply/timing uncertainty examined in this study is that which is caused by variability in the lead time of purchased parts. Experiments are conducted in order to assess the impact of lead time variability, the amount of safety stock buffering, the amount of safety lead time buffering, and the lot-size rule on the average total cost of an MRP system. In addition, the question of whether the results obtained are sensitive to changes in the system's cost parameters is examined. Based on the results, guidelines are developed for practitioners to use when making decisions involving uncertainty in MRP systems. The simulation model used in this research is a versatile MRP/Production simulator designed to provide a framework for the investigation of a wide variety of MRP related problems. The hypothetical manufacturing system is simulated and its average total cost is recorded for varying levels of lead time variability, safety stock, safety lead time, lot-size rule and the holding cost and lateness penalty values. A 3x5x3x3 balanced factorial experiment is performed and multifactor analysis of variance is used to assess significant differences in the average total cost of the MRP system. On those means judged significantly different by Duncan's multiple range test, further analysis is provided in the form of general linear contrasts and confidence intervals.
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    The mixed-integer bilinear programming problem with extensions to zero-one quadratic programs
    Adams, Warren Philip (Virginia Polytechnic Institute and State University, 1985)
    This research effort is concerned with a class of mathematical programming problems referred to as Mixed-Integer Bilinear Programming Problems. This class of problems, which arises in production, location-allocation, and distribution-application contexts, may be considered as a discrete version of the well-known Bilinear Programming Problem in that one set of decision variables is restricted to be binary valued. The structure of this problem is studied, and special cases wherein it is readily solvable are identified. For the more general case, a new linearization technique is introduced and demonstrated to lead to a tighter linear programming relaxation than obtained through available linearization methods. Based on this linearization, a composite Lagrangian relaxation-implicit enumeration-cutting plane algorithm is developed. Extensive computational experience is provided to test the efficiency of various algorithmic strategies and the effects of problem data on the computational effort of the proposed algorithm. The solution strategy developed for the Mixed-Integer Bilinear Programming Problem may be applied, with suitable modifications,. to other classes of mathematical programming problems: in particular, to the Zero-One Quadratic Programming Problem. In what may be considered as an extension to the work performed on the Mixed-Integer Bilinear Programming Problem, a solution strategy based on an equivalent linear reformulation is developed for the Zero-One Quadratic Programming Problem. The strategy is essentially an implicit enumeration algorithm which employs Lagrangian relaxation, Benders' cutting planes, and local explorations. Computational experience for this problem class is provided to justify the worth of the proposed linear reformulation and algorithm.