Decentralized planning with an interdependent marketing-production system

Abad, Prakash L.; Sweeney, Dennis J.

doi:10.1016/0305-0483(82)90014-7

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…Essentially, this is an interaction-prediction approach which has been proven to have very good convergence characteristics. Actually this is an extension of Abad's work [4], to our multi-product, distributed time-delay case. One could use the goal-coordination method with bi as the coordination variable but this would lead to a singular solution with respect to zi (zi enters linearly in both the objective function and the state equations).…”

Section: Obimentioning

confidence: 94%

“…In Section 3 the two proposed multilevel control algorithms that solve this problem are presented, of which the first is based on Tamura's results [11] and the second on Abad's approach [4]. Finally Section 4 gives some numerical results derived by applying the two algorithms, illustrating the flexibility of our model.…”

Section: G Kapsiotis and S Tzafestasmentioning

confidence: 98%

“…Thus, following the approach of Abad [4] a decentralized procedure will be given here which permits each of the departments to solve its problem independently, and then, with the coordination of a supreme unit, e.g. general management, to provide the overall solution.…”

Section: Ky: Min {Al(k)(xi(k) -M) + A2(k)(a~(k) -A) + H~(k)mentioning

confidence: 99%

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Hierarchical control approach to a composite inventory-marketing problem

Kapsiotis

Tzafestas

1994

J Intell Robot Syst

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Abstract.A composite inventory-marketing problem is considered and treated for the case where there exist distributed time delays in the supply and/or the advertisement process, as well as product losses at constant rates. Since in a real large-scale situation a centralized solution may be impractical, two hierarchical coordinated control algorithms are presented which are based on a proper decomposition of the overall problem. Backlogging is not permitted and the delays are assumed fixed and known. The first algorithm makes use of the dual optimization technique, whereas the second algorithm is based on a decentralized procedure which allows each of the company's departments to solve its problem individually, and then with the coordination of an upper level to obtain the overall solution. Numerical examples are included which illustrate the effectiveness of the two algorithms.

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Section: Obimentioning

confidence: 94%

Section: G Kapsiotis and S Tzafestasmentioning

confidence: 98%

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Hierarchical control approach to a composite inventory-marketing problem

Kapsiotis

Tzafestas

1994

J Intell Robot Syst

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“…Abad (1982) and Abad and Sweeney (1982) develop optimal control policies for a single product, where price and advertising are the control variables and sales and inventory are state variables. They compare and contrast centralized and decentralized decision making processes and use numerical examples to illustrate situations when coordinated decision making generates higher profits and when the two approaches result in almost same profits.…”

Section: Production and Advertising Modelsmentioning

confidence: 99%

Coordination of pricing, advertising, and production decisions for multiple products

Bajwa

Sox

2015

IJSOM

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This research aims to develop and propose mathematical models that can be used to facilitate cross-functional coordination between operations and marketing. We consider a dynamic problem of joint pricing, advertising, and production decisions for a profit maximizing firm that produces multiple products. We assume the firm operates in monopolistic environment, where demand for its products is a function of price and advertising expenditure.We model the problem as a mixed integer nonlinear program, incorporating capacity constraints, setup costs, and demand seasonality. We first model and solve the pricing problem without advertising. Later, we extend the model to include advertising as decision variables. The demand for each product is assumed to be continuous, differentiable, strictly decreasing in price and concave in advertising. We present a solution approach which can be used for constant pricing, as well as dynamic pricing strategies. Furthermore, the solution approach is more general and applicable for linear as well as nonlinear demand functions.Using real world data from a manufacturer, we create problem instances, for different demand scenarios at different capacities, and solve for optimal prices for each strategy.We present analytical results that provide managerial insights on how the optimal prices change for different production plans and at different capacities. We compare the firm's profitability for the two pricing strategies, and show that dynamic pricing is valuable at low capacities and when at least one of the products has peak demand in the beginning of the planning horizon.We show that the optimal allocation of advertising budget across products does not change with budget changes. Moreover, the change is minimal with changes in demand seasonality. It is optimal to increase advertising in periods of higher demand and decrease in periods of lower demand. Hence, firms can use rules of thumb and need not to frequently review the allocation. Numerical results show that the proposed algorithms ii have good convergence properties.Finally, as it is clear from review of academic literature; there are no decision support systems that truly integrate the production/inventory and pricing decisions -specially for multi-product problems. We believe, this work makes valuable contributions in developing solution methodologies that can be incorporated in such decision support systems.iii DEDICATION I dedicate this dissertation to; my mother -who always made my education her first priority, my wife Ayesha -for her unconditional love, support, and care, and my children Raiha, Raza, and Rafay for providing an endless supply of hugs and kisses, and for making every day of my life so delightful.iv

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“…Ar,,-{^|||^-^o||<4, (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) where e > 0 is a small positive real number. We shall occasionally make use of the so-caUed "little-o" notation o{z), A fimction F{z) : E'^ -^ E^ is said to be of the order o{z), if Urn 1^ = 0.…”

Section: Product Rule For Differentiationmentioning

confidence: 99%

Optimal Control Theory

2000

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Decentralized planning with an interdependent marketing-production system

Cited by 21 publications

References 27 publications

Hierarchical control approach to a composite inventory-marketing problem

Hierarchical control approach to a composite inventory-marketing problem

Coordination of pricing, advertising, and production decisions for multiple products

Optimal Control Theory

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