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1547-5816
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1553-166X
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Journal of Industrial & Management Optimization
October 2013 , Volume 9 , Issue 4
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2013, 9(4): 723-741
doi: 10.3934/jimo.2013.9.723
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Abstract:
In this paper, we propose a primal-dual approach for solving the generalized fractional programming problem. The outer iteration of the algorithm is a variant of interval-type Dinkelbach algorithm, while the augmented Lagrange method is adopted for solving the inner min-max subproblems. This is indeed a very unique feature of the paper because almost all Dinkelbach-type algorithms in the literature addressed only the outer iteration, while leaving the issue of how to practically solve a sequence of min-max subproblems untouched. The augmented Lagrange method attaches a set of artificial variables as well as their corresponding Lagrange multipliers to the min-max subproblem. As a result, both the primal and the dual information is available for updating the iterate points and the min-max subproblem is then reduced to a sequence of minimization problems. Numerical experiments show that the primal-dual approach can achieve a better precision in fewer iterations.
In this paper, we propose a primal-dual approach for solving the generalized fractional programming problem. The outer iteration of the algorithm is a variant of interval-type Dinkelbach algorithm, while the augmented Lagrange method is adopted for solving the inner min-max subproblems. This is indeed a very unique feature of the paper because almost all Dinkelbach-type algorithms in the literature addressed only the outer iteration, while leaving the issue of how to practically solve a sequence of min-max subproblems untouched. The augmented Lagrange method attaches a set of artificial variables as well as their corresponding Lagrange multipliers to the min-max subproblem. As a result, both the primal and the dual information is available for updating the iterate points and the min-max subproblem is then reduced to a sequence of minimization problems. Numerical experiments show that the primal-dual approach can achieve a better precision in fewer iterations.
2013, 9(4): 743-768
doi: 10.3934/jimo.2013.9.743
+[Abstract](2783)
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Abstract:
In this paper, we consider an optimal investment-consumption problem subject to a closed convex constraint. In the problem, a constraint is imposed on both the investment and the consumption strategy, rather than just on the investment. The existence of solution is established by using the Martingale technique and convex duality. In addition to investment, our technique embeds also the consumption into a family of fictitious markets. However, with the addition of consumption, it leads to nonreflexive dual spaces. This difficulty is overcome by employing the so-called technique of ``relaxation-projection" to establish the existence of solution to the problem. Furthermore, if the solution to the dual problem is obtained, then the solution to the primal problem can be found by using the characterization of the solution. An illustrative example is given with a dynamic risk constraint to demonstrate the method.
In this paper, we consider an optimal investment-consumption problem subject to a closed convex constraint. In the problem, a constraint is imposed on both the investment and the consumption strategy, rather than just on the investment. The existence of solution is established by using the Martingale technique and convex duality. In addition to investment, our technique embeds also the consumption into a family of fictitious markets. However, with the addition of consumption, it leads to nonreflexive dual spaces. This difficulty is overcome by employing the so-called technique of ``relaxation-projection" to establish the existence of solution to the problem. Furthermore, if the solution to the dual problem is obtained, then the solution to the primal problem can be found by using the characterization of the solution. An illustrative example is given with a dynamic risk constraint to demonstrate the method.
2013, 9(4): 769-787
doi: 10.3934/jimo.2013.9.769
+[Abstract](3159)
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Abstract:
Due to globalization and technological advances, increasing competition and falling prices have forced enterprises to reduce cost; this poses new challenges in pricing and replenishment strategy. The study develops a piecewise production-inventory model for a multi-market deteriorating product with time-varying and price-sensitive demand. Optimal product pricing and material replenishment strategy is derived to optimize the manufacturer's total profit. Sensitivity analyses of how the major parameters affect the decision variables were carried out. Finally, the single production cycle is extended to multiple production cycles. We find that the total profit for multiple production cycle increases 5.77/100 when compared with the single production cycle.
Due to globalization and technological advances, increasing competition and falling prices have forced enterprises to reduce cost; this poses new challenges in pricing and replenishment strategy. The study develops a piecewise production-inventory model for a multi-market deteriorating product with time-varying and price-sensitive demand. Optimal product pricing and material replenishment strategy is derived to optimize the manufacturer's total profit. Sensitivity analyses of how the major parameters affect the decision variables were carried out. Finally, the single production cycle is extended to multiple production cycles. We find that the total profit for multiple production cycle increases 5.77/100 when compared with the single production cycle.
2013, 9(4): 789-798
doi: 10.3934/jimo.2013.9.789
+[Abstract](3067)
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Abstract:
The system of absolute value equations $Ax+B|x|=b$, denoted by AVEs, is proved to be NP-hard, where $A, B$ are arbitrary given $n\times n$ real matrices and $b$ is arbitrary given $n$-dimensional vector. In this paper, we reformulate AVEs as a family of parameterized smooth equations and propose a smoothing-type algorithm to solve AVEs. Under the assumption that the minimal singular value of the matrix $A$ is strictly greater than the maximal singular value of the matrix $B$, we prove that the algorithm is well-defined. In particular, we show that the algorithm is globally convergent and the convergence rate is quadratic without any additional assumption. The preliminary numerical results are reported, which show the effectiveness of the algorithm.
The system of absolute value equations $Ax+B|x|=b$, denoted by AVEs, is proved to be NP-hard, where $A, B$ are arbitrary given $n\times n$ real matrices and $b$ is arbitrary given $n$-dimensional vector. In this paper, we reformulate AVEs as a family of parameterized smooth equations and propose a smoothing-type algorithm to solve AVEs. Under the assumption that the minimal singular value of the matrix $A$ is strictly greater than the maximal singular value of the matrix $B$, we prove that the algorithm is well-defined. In particular, we show that the algorithm is globally convergent and the convergence rate is quadratic without any additional assumption. The preliminary numerical results are reported, which show the effectiveness of the algorithm.
2013, 9(4): 799-825
doi: 10.3934/jimo.2013.9.799
+[Abstract](2611)
+[PDF](643.8KB)
Abstract:
We examine the problem of optimal capacity reservation policy on innovative product in a setting of one supplier and one retailer. The parameters of capacity reservation policy are two dimensional: reservation price and excess capacity that the supplier will have in additional to the reservation amount. The above problem is analyzed using a two-stage Stackelberg game. In the first stage, the supplier announces the capacity reservation policy. The retailer forecasts the future demand and then determines the reservation amount. After receiving the reservation amount, the supplier expands the capacity. In the second stage, the uncertainty in demand is resolved and the retailer places a firm order. The supplier salvages the excess capacity and the associated payments are made.
In the paper, with exogenous reservation price or exogenous excess capacity level, we study the optimal expansion policy and then investigate the impacts of reservation price or excess capacity level on the optimal strategies. Finally, we characterize Nash Equilibrium and derive the optimal capacity reservation policy, in which the supplier will adopt exact capacity expansion policy.
We examine the problem of optimal capacity reservation policy on innovative product in a setting of one supplier and one retailer. The parameters of capacity reservation policy are two dimensional: reservation price and excess capacity that the supplier will have in additional to the reservation amount. The above problem is analyzed using a two-stage Stackelberg game. In the first stage, the supplier announces the capacity reservation policy. The retailer forecasts the future demand and then determines the reservation amount. After receiving the reservation amount, the supplier expands the capacity. In the second stage, the uncertainty in demand is resolved and the retailer places a firm order. The supplier salvages the excess capacity and the associated payments are made.
In the paper, with exogenous reservation price or exogenous excess capacity level, we study the optimal expansion policy and then investigate the impacts of reservation price or excess capacity level on the optimal strategies. Finally, we characterize Nash Equilibrium and derive the optimal capacity reservation policy, in which the supplier will adopt exact capacity expansion policy.
2013, 9(4): 827-853
doi: 10.3934/jimo.2013.9.827
+[Abstract](3085)
+[PDF](718.5KB)
Abstract:
This paper develops three (re)ordering models of a supply chain consisting of one risk-neutral manufacturer and one loss-averse retailer to study the coordination mechanism and the effects of the reordering policy on the coordination mechanism. The three (re)ordering policies are twice ordering policy with break-even quantity, twice ordering policy without break-even quantity and once ordering policy, respectively. We design a buyback-setup-cost-sharing mechanism to coordinate the supply chain for each policy, and Pareto analysis indicates that both the manufacturer and the retailer will realize a 'win-win' situation. By comparing the models, we find that twice ordering policy with break-even quantity is absolutely dominant for both the retailer and the supply chain. However, only if the break-even quantity is less than the mean quantity to failure, twice ordering policy without break-even quantity is dominant over the once ordering policy. The higher marginal revenue can induce more order quantity of the retailer under both twice ordering policy with break-even quantity and once ordering policy. However, it is interesting that it has no effect on the order plan of centralized decision-maker in twice ordering policy without break-even quantity.
This paper develops three (re)ordering models of a supply chain consisting of one risk-neutral manufacturer and one loss-averse retailer to study the coordination mechanism and the effects of the reordering policy on the coordination mechanism. The three (re)ordering policies are twice ordering policy with break-even quantity, twice ordering policy without break-even quantity and once ordering policy, respectively. We design a buyback-setup-cost-sharing mechanism to coordinate the supply chain for each policy, and Pareto analysis indicates that both the manufacturer and the retailer will realize a 'win-win' situation. By comparing the models, we find that twice ordering policy with break-even quantity is absolutely dominant for both the retailer and the supply chain. However, only if the break-even quantity is less than the mean quantity to failure, twice ordering policy without break-even quantity is dominant over the once ordering policy. The higher marginal revenue can induce more order quantity of the retailer under both twice ordering policy with break-even quantity and once ordering policy. However, it is interesting that it has no effect on the order plan of centralized decision-maker in twice ordering policy without break-even quantity.
2013, 9(4): 855-883
doi: 10.3934/jimo.2013.9.855
+[Abstract](3050)
+[PDF](538.1KB)
Abstract:
In today's business environment, there are various reasons, namely, bulk purchase discounts, seasonality of products, re-order costs, etc., which force the buyer to order more than the warehouse capacity (owned warehouse). Such reasons call for additional storage space to store the excess units purchased. This additional storage space is typically a rented warehouse. It is known that the demand of seasonal products increases at the beginning of the season up to a certain moment and then is stabilized to a constant rate for the remaining time of the season (ramp type demand rate). As a result, the buyer prefers to keep a higher inventory at the beginning of the season and so more units than can be stored in owned warehouse may be purchased. The excess quantities need additional storage space, which is facilitated by a rented warehouse.
In this study an order level two-warehouse inventory model for deteriorating seasonal products is studied. Shortages at the owned warehouse are allowed subject to partial backlogging. This two-warehouse inventory model is studied under two different policies. The first policy starts with an instant replenishment and ends with shortages and the second policy starts with shortages and ends without shortages. For each of the models, conditions for the existence and uniqueness of the optimal solution are derived and a simple procedure is developed to obtain the overall optimal replenishment policy. The dynamics of the model and the solution procedure have been illustrated with the help of a numerical example and a comprehensive sensitivity analysis, with respect to the most important parameters of the model, is considered.
In today's business environment, there are various reasons, namely, bulk purchase discounts, seasonality of products, re-order costs, etc., which force the buyer to order more than the warehouse capacity (owned warehouse). Such reasons call for additional storage space to store the excess units purchased. This additional storage space is typically a rented warehouse. It is known that the demand of seasonal products increases at the beginning of the season up to a certain moment and then is stabilized to a constant rate for the remaining time of the season (ramp type demand rate). As a result, the buyer prefers to keep a higher inventory at the beginning of the season and so more units than can be stored in owned warehouse may be purchased. The excess quantities need additional storage space, which is facilitated by a rented warehouse.
In this study an order level two-warehouse inventory model for deteriorating seasonal products is studied. Shortages at the owned warehouse are allowed subject to partial backlogging. This two-warehouse inventory model is studied under two different policies. The first policy starts with an instant replenishment and ends with shortages and the second policy starts with shortages and ends without shortages. For each of the models, conditions for the existence and uniqueness of the optimal solution are derived and a simple procedure is developed to obtain the overall optimal replenishment policy. The dynamics of the model and the solution procedure have been illustrated with the help of a numerical example and a comprehensive sensitivity analysis, with respect to the most important parameters of the model, is considered.
2013, 9(4): 885-892
doi: 10.3934/jimo.2013.9.885
+[Abstract](2247)
+[PDF](360.4KB)
Abstract:
In the framework of multi-choice games, we propose a specific reduction to construct a dynamic process for the multi-choice Shapley value introduced by Nouweland et al. [8].
In the framework of multi-choice games, we propose a specific reduction to construct a dynamic process for the multi-choice Shapley value introduced by Nouweland et al. [8].
2013, 9(4): 893-899
doi: 10.3934/jimo.2013.9.893
+[Abstract](2568)
+[PDF](315.2KB)
Abstract:
In [8], Zhang et al. proposed a modified three-term HS (MTTHS) conjugate gradient method and proved that this method converges globally for nonconvex minimization in the sense that $\liminf_{k\to\infty}\|\nabla f(x_k)\|=0$ when the Armijo or Wolfe line search is used. In this paper, we further study the convergence property of the MTTHS method. We show that the MTTHS method has strongly global convergence property (i.e., $\lim_{k\to\infty}\|\nabla f(x_k)\|=0$) for nonconvex optimization by the use of the backtracking type line search in [7]. Some preliminary numerical results are reported.
In [8], Zhang et al. proposed a modified three-term HS (MTTHS) conjugate gradient method and proved that this method converges globally for nonconvex minimization in the sense that $\liminf_{k\to\infty}\|\nabla f(x_k)\|=0$ when the Armijo or Wolfe line search is used. In this paper, we further study the convergence property of the MTTHS method. We show that the MTTHS method has strongly global convergence property (i.e., $\lim_{k\to\infty}\|\nabla f(x_k)\|=0$) for nonconvex optimization by the use of the backtracking type line search in [7]. Some preliminary numerical results are reported.
2013, 9(4): 901-917
doi: 10.3934/jimo.2013.9.901
+[Abstract](2597)
+[PDF](390.1KB)
Abstract:
This paper analyzes an M/G/1 queue with general setup times from an economical point of view. In such a queue whenever the system becomes empty, the server is turned off. A new customer's arrival will turn the server on after a setup period. Upon arrival, the customers decide whether to join or balk the queue based on observation of the queue length and the status of the server, along with the reward-cost structure of the system. For the observable and almost observable cases, the equilibrium joining strategies of customers who wish to maximize their expected net benefit are obtained. Two numerical examples are presented to illustrate the equilibrium joining probabilities for these cases under some specific distribution functions of service times and setup times.
This paper analyzes an M/G/1 queue with general setup times from an economical point of view. In such a queue whenever the system becomes empty, the server is turned off. A new customer's arrival will turn the server on after a setup period. Upon arrival, the customers decide whether to join or balk the queue based on observation of the queue length and the status of the server, along with the reward-cost structure of the system. For the observable and almost observable cases, the equilibrium joining strategies of customers who wish to maximize their expected net benefit are obtained. Two numerical examples are presented to illustrate the equilibrium joining probabilities for these cases under some specific distribution functions of service times and setup times.
2013, 9(4): 919-944
doi: 10.3934/jimo.2013.9.919
+[Abstract](3111)
+[PDF](524.4KB)
Abstract:
In this paper, a new non-monotone trust-region algorithm is proposed for solving unconstrained nonlinear optimization problems. We modify the retrospective ratio which is introduced by Bastin et al. [Math. Program., Ser. A (2010) 123: 395-418] to form a convex combination ratio for updating the trust-region radius. Then we combine the non-monotone technique with this new framework of trust-region algorithm. The new algorithm is shown to be globally convergent to a first-order critical point. Numerical experiments on CUTEr problems indicate that it is competitive with both the original retrospective trust-region algorithm and the classical trust-region algorithms.
In this paper, a new non-monotone trust-region algorithm is proposed for solving unconstrained nonlinear optimization problems. We modify the retrospective ratio which is introduced by Bastin et al. [Math. Program., Ser. A (2010) 123: 395-418] to form a convex combination ratio for updating the trust-region radius. Then we combine the non-monotone technique with this new framework of trust-region algorithm. The new algorithm is shown to be globally convergent to a first-order critical point. Numerical experiments on CUTEr problems indicate that it is competitive with both the original retrospective trust-region algorithm and the classical trust-region algorithms.
2013, 9(4): 945-965
doi: 10.3934/jimo.2013.9.945
+[Abstract](2889)
+[PDF](503.6KB)
Abstract:
The aim of this paper is to develop an improved inventory model which helps the enterprises to advance their profit increasing and cost reduction in a single vendor-single buyer environment with permissible delay in payments depending on the ordering quantity and imperfect production. Through this study, some numerical examples available in the literature are provided herein to apply the permissible delay in payments depending on the ordering quantity strategy. Furthermore, imperfect products will cause the cost and increase number of lots through the whole model. Therefore, for more closely conforming to the actual inventories and responding to the factors that contribute to inventory costs, our proposed model can be the references to the business applications. Finally, results of this study showed applying the permissible delay in payments can promote the cost reduction; and also showed a longer trade credit term can decrease costs for the complete supply chain.
The aim of this paper is to develop an improved inventory model which helps the enterprises to advance their profit increasing and cost reduction in a single vendor-single buyer environment with permissible delay in payments depending on the ordering quantity and imperfect production. Through this study, some numerical examples available in the literature are provided herein to apply the permissible delay in payments depending on the ordering quantity strategy. Furthermore, imperfect products will cause the cost and increase number of lots through the whole model. Therefore, for more closely conforming to the actual inventories and responding to the factors that contribute to inventory costs, our proposed model can be the references to the business applications. Finally, results of this study showed applying the permissible delay in payments can promote the cost reduction; and also showed a longer trade credit term can decrease costs for the complete supply chain.
2013, 9(4): 967-982
doi: 10.3934/jimo.2013.9.967
+[Abstract](3410)
+[PDF](360.2KB)
Abstract:
Channel coordination is an optimal state with operation of channel. For achieving channel coordination, we present a quantity discount mechanism based on a fairness preference theory. Game models of the channel discount mechanism are constructed based on the entirely rationality and self-interest. The study shows that as long as the degree of attention (parameters) of retailer to manufacturer's profit and the fairness preference coefficients (parameters) of retailers satisfy certain conditions, channel coordination can be achieved by setting a simple wholesale price and fixed costs. We also discuss the allocation method of channel coordination profit, the allocation method ensure that retailer's profit is equal to the profit of independent decision-making, and manufacturer's profit is raised.
Channel coordination is an optimal state with operation of channel. For achieving channel coordination, we present a quantity discount mechanism based on a fairness preference theory. Game models of the channel discount mechanism are constructed based on the entirely rationality and self-interest. The study shows that as long as the degree of attention (parameters) of retailer to manufacturer's profit and the fairness preference coefficients (parameters) of retailers satisfy certain conditions, channel coordination can be achieved by setting a simple wholesale price and fixed costs. We also discuss the allocation method of channel coordination profit, the allocation method ensure that retailer's profit is equal to the profit of independent decision-making, and manufacturer's profit is raised.
2013, 9(4): 983-1001
doi: 10.3934/jimo.2013.9.983
+[Abstract](2894)
+[PDF](478.7KB)
Abstract:
Constraint qualification (CQ) is an important concept in nonlinear programming. This paper investigates the motivation of introducing constraint qualifications in developing KKT conditions for solving nonlinear programs and provides a geometric meaning of constraint qualifications. A unified framework of designing constraint qualifications by imposing conditions to equate the so-called ``locally constrained directions" to certain subsets of ``tangent directions" is proposed. Based on the inclusion relations of the cones of tangent directions, attainable directions, feasible directions and interior constrained directions, constraint qualifications are categorized into four levels by their relative strengths. This paper reviews most, if not all, of the commonly seen constraint qualifications in the literature, identifies the categories they belong to, and summarizes the inter-relationship among them. The proposed framework also helps design new constraint qualifications of readers' specific interests.
Constraint qualification (CQ) is an important concept in nonlinear programming. This paper investigates the motivation of introducing constraint qualifications in developing KKT conditions for solving nonlinear programs and provides a geometric meaning of constraint qualifications. A unified framework of designing constraint qualifications by imposing conditions to equate the so-called ``locally constrained directions" to certain subsets of ``tangent directions" is proposed. Based on the inclusion relations of the cones of tangent directions, attainable directions, feasible directions and interior constrained directions, constraint qualifications are categorized into four levels by their relative strengths. This paper reviews most, if not all, of the commonly seen constraint qualifications in the literature, identifies the categories they belong to, and summarizes the inter-relationship among them. The proposed framework also helps design new constraint qualifications of readers' specific interests.
2020
Impact Factor: 1.801
5 Year Impact Factor: 1.688
2020 CiteScore: 1.8
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