The Acquisition of Automation Subject to Diminishing Returns

Gaimon, Cheryl

doi:10.1080/07408178508975285

Cited by 16 publications

(4 citation statements)

References 4 publications

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“…A final point to be made about problem (2) is that it is static rather than dynamic in nature (cf Gaimon [10], Kimemia and Gershwin [13]). If it is desirable to take into account time-phased acquisition of equipment, growing demand, cost discounting, improved (or alternate) process plans, and expected changes in the product mix, substantial modifications in the model would be needed.…”

Section: Solution Methodologymentioning

confidence: 99%

An Algorithm for the Manufacturing Equipment Selection Problem

Bard

Feo

1991

IIE Transactions

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Section: Solution Methodologymentioning

confidence: 99%

An Algorithm for the Manufacturing Equipment Selection Problem

Bard

Feo

1991

IIE Transactions

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“…Other issues such as process improvement, automation, quality, and learning effects are also important in production problems. For optimal control applications dealing with these issues, see Vickson (1985), Amit and Ilan (1990), Li and Rajagopalan (1998), Gaimon (1985aGaimon ( , 1985b), J0rgensen, Kort, and Zaccour (1999), and Carrillo and Gaimon (2000).…”

Section: A Production-inventory Systemmentioning

confidence: 99%

Optimal Control Theory

2000

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“…The terms in each of the objective functions are a penalty for deviating from a planned production schedule, the cost of automation acquisition, the costs of increasing and decreasing the level of labor, and a salvage value term. In Gaimon (1985a), production cost is also included; in (1985b) the cost of maintaining both labor and automated equipment is in the objective function. Each paper establishes the optimality of not simultaneously increasing and decreasing the rate of manual output.…”

Section: Prior Researchmentioning

confidence: 99%

Optimal Acquisition of Automated Flexible Manufacturing Processes

Monahan

Smunt

1989

Operations Research

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In Section 5, we give explicit examples of production, inventory, and acquisition cost functions that satisfy the three, rather general, assumptions.The proofs of the results in this section are given in the Appendix.The first result characterizes the optimal cost functions, given in (2) and (A).For any interest-technology pair s, the optimal cost incurred from period n through period for any a, < a < 1.With the structure of the optimal cost function developed in Proposition 1 and the structure of the unconstrained solution given in Lemma 1, we can now describe the structure of the optimal acquisition policy. Generally, period n's optimal level of automation is independent of the current level of automation (unless the level of automation in place at the beginning of period n exceeds the desired level).Proposition 2. For all a, s, and n, p (s,a) = max{p (s,a),a).It follows from Lemma 1 and Proposition 2 that p (s,a) is nondecreasing in a.The next result asserts that the optimal level of automation declines as the planning horizon shortens.Proposition 3. For all a, s, and n, p (s,a) > p .(s,a). In summary, the inventory carrying cost function is modeled as follows: where s represents the interest-technology pair, and c(0) represents the "base-line" cost per period of producing with no automation. The multiplier T(p-a) represents the diseconomies (economies) associated with purchasing flexible automation incrementally over time rather than all at once. To capture this phenomenon, we assume that T(*) is increasing and concave (convex), T(0) = 0, and T(l) = 1. To better understand the influence of uncertainty on the acquisition process.

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The Acquisition of Automation Subject to Diminishing Returns

Cited by 16 publications

References 4 publications

An Algorithm for the Manufacturing Equipment Selection Problem

An Algorithm for the Manufacturing Equipment Selection Problem

Optimal Control Theory

Optimal Acquisition of Automated Flexible Manufacturing Processes

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