The Hit and Miss manufacturing company has received an order to simply one item of a...
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The Hit and Miss manufacturing company has received an order to simply one item of a particular type. However, the customer has specified such stringent quality requirements that the manufacturer may have to produce more than one item to obtain an item that is acceptable. The number of extra items produced in a production run is called the reject allowance. Including a reject allowance is common practice when producing for a custom order, and it seems advisable in this case. The manufacturer estimates that each item of this type that is produced will be acceptable with probability % and defective (without possibility for rework) with probability %. Thus, the number of acceptable items produced in a lot of size L will have a binomial distribution; i.e., the probability of producing no acceptable items in such a lot is (1/2)-. Marginal production costs for this product are estimated to be $100 per item (even if defective), and excess items are worthless. In addition, a setup cost of $300 must be incurred whenever the production process is set up for this product, and a completely new setup at this same cost is required for each subsequent production run if a lengthy inspection procedure reveals that a completed lot has not yielded an acceptable item. The manufacturer has time to make no more than three production runs. If an acceptable item has not been obtained by the end of the third production run, the cost to the manufacturer in lost sales income and penalty costs will be $1,600. 1. The objective is to determine the policy regarding the lot size (1+reject allowance) for the required production run(s) that minimizes total expected cost for the manufacturer. 2. After a more careful analysis, you now estimate that each item produced will be acceptable with probability 2/3, so that the probability of producing zero acceptable items in a lot of size L is (1/3)'. Furthermore, there now is only enough time available to make two production runs. Use dynamic programming to determine the new optimal policy for this problem. The Hit and Miss manufacturing company has received an order to simply one item of a particular type. However, the customer has specified such stringent quality requirements that the manufacturer may have to produce more than one item to obtain an item that is acceptable. The number of extra items produced in a production run is called the reject allowance. Including a reject allowance is common practice when producing for a custom order, and it seems advisable in this case. The manufacturer estimates that each item of this type that is produced will be acceptable with probability % and defective (without possibility for rework) with probability %. Thus, the number of acceptable items produced in a lot of size L will have a binomial distribution; i.e., the probability of producing no acceptable items in such a lot is (1/2)-. Marginal production costs for this product are estimated to be $100 per item (even if defective), and excess items are worthless. In addition, a setup cost of $300 must be incurred whenever the production process is set up for this product, and a completely new setup at this same cost is required for each subsequent production run if a lengthy inspection procedure reveals that a completed lot has not yielded an acceptable item. The manufacturer has time to make no more than three production runs. If an acceptable item has not been obtained by the end of the third production run, the cost to the manufacturer in lost sales income and penalty costs will be $1,600. 1. The objective is to determine the policy regarding the lot size (1+reject allowance) for the required production run(s) that minimizes total expected cost for the manufacturer. 2. After a more careful analysis, you now estimate that each item produced will be acceptable with probability 2/3, so that the probability of producing zero acceptable items in a lot of size L is (1/3)'. Furthermore, there now is only enough time available to make two production runs. Use dynamic programming to determine the new optimal policy for this problem.
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Related Book For
Management Science The Art of Modeling with Spreadsheets
ISBN: 978-1118582695
4th edition
Authors: Stephen G. Powell, Kenneth R. Baker
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