Weber and Stidham (1987) used submodularity to establish transition monotonicity (a service completion at one station cannot reduce the service rate at another station) for Markovian queueing networks that meet certain regularity conditions and are controlled to minimize service and queueing costs. We give an extension of monotonicity to other directions in the state space, such as arrival transitions, and to arrival routing problems. The conditions used to establish monotonicity, which deal with the boundary of the state space, are easily verified for many queueing systems. We also show that, without service costs, transition-monotone controls can be described by simple control regions and switching functions, extending earlier results. The theory is applied to production/inventory systems with holding costs at each stage and finished goods backorder costs.

Weber and Stidham (1987) used submodularity to establish transition monotonicity (a service completion at one station cannot reduce the service rate at another station) for Markovian queueing networks that meet certain regularity conditions and are controlled to minimize service and queueing costs. We give an extension of monotonicity to other directions in the state space, such as arrival transitions, and to arrival routing problems. The conditions used to establish monotonicity, which deal with the boundary of the state space, are easily verified for many queueing systems. We also show that, without service costs, transition-monotone controls can be described by simple control regions and switching functions, extending earlier results. The theory is applied to production/inventory systems with holding costs at each stage and finished goods backorder costs.

In this research, we consider production/inventory management decisions of a rmthat sells its product in two market segments during a nite planning horizon. In thebeginning of each period, the rm makes a decision on how much to produce basedon the production capacity and the current on-hand inventory available. After theproduction is made at the beginning of the period, the rm rst satises the stochasticdemand from customers in its primary market. Any primary market demand thatcannot be satised is lost. After satisfying the demand from the primary market, ifthere is still inventory on hand, all or part of the remaining products can be sold ina secondary market with ample demand at a lower price. Hence, the second decisionthat the rm makes in each period is how much to sell in the secondary market, orequivalently, how much inventory to carry to the next period.The objective is to maximize the expected net revenue during a nite planninghorizon by determining the optimal production quantity in each period, and theoptimal inventory amount to carry to the next period after the sales in primary andsecondary markets. We term the optimal inventory amount to be carried to the nextperiod as \economic retention quantity". We model this problem as a nite horizonstochastic dynamic program. Our focus is to characterize the structure of the optimalpolicy and to analyze the system under dierent parameter settings. Conditioning on given parameter set, we establish lower and upper bounds on the optimal policyparameters. Furthermore, we provide computational tools to determine the optimalpolicy parameters. Results of the numerical analysis are used to provide furtherinsights into the problem from a managerial perspective.

In this research, we consider production/inventory management decisions of a rmthat sells its product in two market segments during a nite planning horizon. In thebeginning of each period, the rm makes a decision on how much to produce basedon the production capacity and the current on-hand inventory available. After theproduction is made at the beginning of the period, the rm rst satises the stochasticdemand from customers in its primary market. Any primary market demand thatcannot be satised is lost. After satisfying the demand from the primary market, ifthere is still inventory on hand, all or part of the remaining products can be sold ina secondary market with ample demand at a lower price. Hence, the second decisionthat the rm makes in each period is how much to sell in the secondary market, orequivalently, how much inventory to carry to the next period.The objective is to maximize the expected net revenue during a nite planninghorizon by determining the optimal production quantity in each period, and theoptimal inventory amount to carry to the next period after the sales in primary andsecondary markets. We term the optimal inventory amount to be carried to the nextperiod as \economic retention quantity". We model this problem as a nite horizonstochastic dynamic program. Our focus is to characterize the structure of the optimalpolicy and to analyze the system under dierent parameter settings. Conditioning on given parameter set, we establish lower and upper bounds on the optimal policyparameters. Furthermore, we provide computational tools to determine the optimalpolicy parameters. Results of the numerical analysis are used to provide furtherinsights into the problem from a managerial perspective.

Este trabalho tem por objetivo propor um método eficiente para avaliar gestão de estoque, aplicando conjuntamente a Teoria de Controle Ótimo (OCT), para obter funções de estocagem dinamicamente ótimas, e Análise Envoltória de Dados (DEA), para calcular as eficiências relativas. Tendo em vista esse objetivo foi desenvolvido um modelo integrado DEA-OCT para calcular a eficiência de custo otimizada ao longo do tempo, quando o sistema possui variáveis relacionadas entre si, como no caso de sistemas de controle de estoque, e para analisar produção e demanda (assim como a variável estoque, oriunda dessa relação), estendendo o modelo variacional. Este trabalho aplica o modelo proposto a 647 empresas das Américas do Sul e do Norte, depois faz uma comparação entre Brasil e Chile (países emergentes economicamente), posteriormente focando no setor de comércio, considerando seus sistemas produção-estoque com dados de variáveis contábeis. Os modelos minimizam os custos de produção e de estoque para calcular a eficiência de custo ao longo do tempo. O output (produto, ou variável de saída) é a demanda; o input (insumo, ou variável de entrada) é a produção, e o intermediate (variável intermediária) é o estoque. Seus custos são considerados na função objetivo. É acrescentada uma restrição variacional da OCT para descrever a relação entre demanda, produção e estoque. Em resumo, o modelo é relevante por calcular eficiência prevenindo a possibilidade de obter uma projeção que ignora a relação entre as variáveis, uma vez que essa relação sempre ocorre, na prática, em sistemas de controle de estoque. As principais contribuições são: possibilitar o uso de OCT como a ferramenta de benchmarking DEA no contexto de eficiência dinâmica, estender o modelo DEA variacional de Sengupta (1995), incluindo restrições de modelos mais recentes e possibilitar o cálculo de eficiência quando há relação entre as variáveis. / This work aims to propose an efficient method to evaluate inventory management, jointly applying optimal control theory (OCT), obtaining dynamically optimal production and inventory functions, and data envelopment analysis (DEA), calculating the relative efficiencies. With this objective in mind, it was developed a DEA-OCT integrated model to calculate allocative efficiency optimized over time, when systems have variable with relationship among themselves, like in the case of inventory control systems, and for analyzing production and demand (as the inventory variable obtained from this relationship), extending the variational model. This paper applies the proposed model to 647 companies from South and North America, after that it was made a comparison between Brazil and Chile (economically emerging countries), then focusing on the commercial sector, considering its production-inventory systems and data from accounting variables. The model minimizes the inventory and production costs to calculate the allocative efficiency over time. The output is demand; the input is production, and the intermediate variable is inventory. Their costs are considered in the objective function. A variational constraint OCT is added to describe the relationship among demand, production, and inventory. In summary, the model is relevant to calculate efficiency by preventing the possibility of finding a projection that ignores the relationship among variables, since this relationship always occur in practice in inventory control systems. The main contributions are: using OCT as the benchmarking tool DEA in the context of dynamic efficiency, extending the Sengupta (1995) variational DEA model, including constraints from recent model and making it possible to calculate efficiency when there is a relationship among variables.

Este trabalho tem por objetivo propor um método eficiente para avaliar gestão de estoque, aplicando conjuntamente a Teoria de Controle Ótimo (OCT), para obter funções de estocagem dinamicamente ótimas, e Análise Envoltória de Dados (DEA), para calcular as eficiências relativas. Tendo em vista esse objetivo foi desenvolvido um modelo integrado DEA-OCT para calcular a eficiência de custo otimizada ao longo do tempo, quando o sistema possui variáveis relacionadas entre si, como no caso de sistemas de controle de estoque, e para analisar produção e demanda (assim como a variável estoque, oriunda dessa relação), estendendo o modelo variacional. Este trabalho aplica o modelo proposto a 647 empresas das Américas do Sul e do Norte, depois faz uma comparação entre Brasil e Chile (países emergentes economicamente), posteriormente focando no setor de comércio, considerando seus sistemas produção-estoque com dados de variáveis contábeis. Os modelos minimizam os custos de produção e de estoque para calcular a eficiência de custo ao longo do tempo. O output (produto, ou variável de saída) é a demanda; o input (insumo, ou variável de entrada) é a produção, e o intermediate (variável intermediária) é o estoque. Seus custos são considerados na função objetivo. É acrescentada uma restrição variacional da OCT para descrever a relação entre demanda, produção e estoque. Em resumo, o modelo é relevante por calcular eficiência prevenindo a possibilidade de obter uma projeção que ignora a relação entre as variáveis, uma vez que essa relação sempre ocorre, na prática, em sistemas de controle de estoque. As principais contribuições são: possibilitar o uso de OCT como a ferramenta de benchmarking DEA no contexto de eficiência dinâmica, estender o modelo DEA variacional de Sengupta (1995), incluindo restrições de modelos mais recentes e possibilitar o cálculo de eficiência quando há relação entre as variáveis. / This work aims to propose an efficient method to evaluate inventory management, jointly applying optimal control theory (OCT), obtaining dynamically optimal production and inventory functions, and data envelopment analysis (DEA), calculating the relative efficiencies. With this objective in mind, it was developed a DEA-OCT integrated model to calculate allocative efficiency optimized over time, when systems have variable with relationship among themselves, like in the case of inventory control systems, and for analyzing production and demand (as the inventory variable obtained from this relationship), extending the variational model. This paper applies the proposed model to 647 companies from South and North America, after that it was made a comparison between Brazil and Chile (economically emerging countries), then focusing on the commercial sector, considering its production-inventory systems and data from accounting variables. The model minimizes the inventory and production costs to calculate the allocative efficiency over time. The output is demand; the input is production, and the intermediate variable is inventory. Their costs are considered in the objective function. A variational constraint OCT is added to describe the relationship among demand, production, and inventory. In summary, the model is relevant to calculate efficiency by preventing the possibility of finding a projection that ignores the relationship among variables, since this relationship always occur in practice in inventory control systems. The main contributions are: using OCT as the benchmarking tool DEA in the context of dynamic efficiency, extending the Sengupta (1995) variational DEA model, including constraints from recent model and making it possible to calculate efficiency when there is a relationship among variables.