Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain...
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Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes. We have formulated the problem as a mixedintegerprogramming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, we address a service provider's product line pricing problem for substitutable products in services, such as concerts, sporting events, or online advertisements. For each product, a static price is ...
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In this paper, we address a service provider's product line pricing problem for substitutable products in services, such as concerts, sporting events, or online advertisements. For each product, a static price is selected from a pre-defined set such that the total revenue is maximised. The products are differentiated by some of their attributes, and their availability is restricted due to individual capacity constraints. Furthermore, they are simultaneously sold during a common selling period at the end of which the service is delivered. Consumers differ from one another with respect to their willingness to pay, and, hence, their reservation prices vary depending on the product. In the event of a purchase, they choose the product that maximises their consumer surplus. Even if the number of consumers, the sequence of their arrival, and their product-specific reservation prices are known, the selection of optimal prices is computationally expensive. Due to capacity constraints, products can be sold out early during the selling period, which causes spill and recapture effects by dynamic substitution. In other words, consumers can switch from their preferred products to other products. To model the resulting choice process and optimise total revenue, we propose a linear mixed-integer program. For the solution of this program, we present a branch and bound procedure as well as several heuristic algorithms and evaluate their performances in computational experiments. (C) 2011 Elsevier B.V. All rights reserved.
Let (MQP) be a general mixedinteger quadratic program that consists of minimizing a quadratic function subject to linear constraints. In this paper, we present a convex reformulation of (MQP), i.e. we reformulate (MQ...
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Let (MQP) be a general mixedinteger quadratic program that consists of minimizing a quadratic function subject to linear constraints. In this paper, we present a convex reformulation of (MQP), i.e. we reformulate (MQP) into an equivalent program, with a convex objective function. Such a reformulation can be solved by a standard solver that uses a branch and bound algorithm. We prove that our reformulation is the best one within a convex reformulation scheme, from the continuous relaxation point of view. This reformulation, that we call MIQCR (mixedinteger Quadratic Convex Reformulation), is based on the solution of an SDP relaxation of (MQP). Computational experiences are carried out with instances of (MQP) including one equality constraint or one inequality constraint. The results show that most of the considered instances with up to 40 variables can be solved in 1 h of CPU time by a standard solver.
In recent years, load management (LM) programs are introduced as an impressive option in all energy policy decisions. Under deregulation, the scope of LM programs has considerably been expanded to include demand respo...
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In recent years, load management (LM) programs are introduced as an impressive option in all energy policy decisions. Under deregulation, the scope of LM programs has considerably been expanded to include demand response programs (DRPs). Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate (TBR) programs. In this paper, an economic model of responsive loads is derived based upon price elasticity of demand and customers' benefit function. In order to investigate the economic-driven and environmental-driven measures of demand response programs, a new linearized formulation of cost-emission based unit commitment problem associated with DRPs (UCDR) is presented. Here, UCDR is modeled as a mixed-integer programming (MIP) problem. The proposed model is applied to determine loads provided by DRPs and schedule commitment status of generating units. Moreover, the optimum value of incentive as a crucial issue for implementing DRPs is derived. Several analyses are conducted to investigate the impact of some important factors such as elasticity on the UCDR problem. The strategy success index (SSI) is employed to prioritize DRPs from the independent system operator (ISO) perspective. The conventional 10-unit test system is used to demonstrate effectiveness of the proposed methodology.
A two-level supply chain with multiple items, production sites and client areas and a discrete time horizon is considered. After introducing different mixedintegerprogramming formulations, including an initial formu...
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A two-level supply chain with multiple items, production sites and client areas and a discrete time horizon is considered. After introducing different mixedintegerprogramming formulations, including an initial formulation that is small but provides weak bounds and a multi-commodity extended formulation that provides much improved bounds but is of large size, we develop a hybrid heuristic that uses the strong formulation to provide a good dual bound and suggest certain variable fixing, and the initial formulation restricted by the variable fixing to then provide the heuristic solution. For different classes of medium-sized instances, we show that the hybrid heuristic provides solutions of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. (c) 2012 Elsevier Ltd. All rights reserved.
This paper considers a scheduling problem with component availability constraints in a supply chain consisting of two manufacturing facilities and a merge-in-transit facility. Three mixed-integer programming (MIP) mod...
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This paper considers a scheduling problem with component availability constraints in a supply chain consisting of two manufacturing facilities and a merge-in-transit facility. Three mixed-integer programming (MIP) models and a constraint programming (CP) model are compared in an extensive numerical study. Results show that when there are no components shared among the two manufacturers, the MIP model based on time-index variables is the best for proving optimality for problems with short processing times whereas the CP model tends to perform better than the others for problems with a large range of processing times. When shared components are added, the performance of all models deteriorates, with the time-indexed MIP providing the best results. By explicitly modelling the dependence of scheduling decisions on the availability of component parts, we contribute to the literature on the integration of inventory and scheduling decisions, which is necessary for solving realistic supply chain problems. (C) 2012 Elsevier Ltd. All rights reserved.
This paper presents a formulation of security-constrained unit commitment (SCUC) problem based on mixedintegerprogramming (MIP) method with considering prohibited operating zone limits of thermal and hydro units. Th...
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ISBN:
(纸本)9781424420292
This paper presents a formulation of security-constrained unit commitment (SCUC) problem based on mixedintegerprogramming (MIP) method with considering prohibited operating zone limits of thermal and hydro units. The objective of SCUC problem is to obtain minimum system operating cost while maintaining the system security. Thermal and hydro units in power system studies are assumed to possess smooth and convex input-output (IO) curves. Practically, not all the operating zones of generation units are available for load allocation due to some physical operation limits. This may cause to have a generator with nonsmooth IO curve with equality and inequality constraints which may not be solved easily by conventional mathematical methods. In this paper, non-convex characteristic of generator cost function, representing prohibited operating zones is considered in SCUC. Test results with an eight-bus system show the accuracy of the model and formulations.
Within the context of build-to-order or assembly-to-order production strategy, an end product satisfying the requirements of a customer often comprises various components or parts that may be provided by the partnerin...
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Within the context of build-to-order or assembly-to-order production strategy, an end product satisfying the requirements of a customer often comprises various components or parts that may be provided by the partnering suppliers. More probably, there exist a numerous number of alternative components or parts to be chosen when an end product is configured. Meanwhile, structures and constraints within the configurable product must be observed. To represent structures of products and constraints, object-oriented modeling method is employed to enable the share and reuse of configuration knowledge. To derive optimal configuration, product configuration problem with object-oriented configuration knowledge is formalized as a mixed-integer programming problem. The algorithm for product configuration optimization is implemented using Java CPLEX. Experiments are conducted to demonstrate and compare the performance of the algorithm. The results show that the computational time is reasonable and acceptable for medium-sized configuration problems.
This paper is concerned with the problem of assigning employees to gas stations owned by the Kuwait National Petroleum Corporation (KNPC), which hires a firm to prepare schedules for assigning employees to about 86 st...
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This paper is concerned with the problem of assigning employees to gas stations owned by the Kuwait National Petroleum Corporation (KNPC), which hires a firm to prepare schedules for assigning employees to about 86 stations distributed all over Kuwait. Although similar employee scheduling problems have been addressed in the literature, certain peculiarities of the problem require novel mathematical models and algorithms to deal with the specific nature and size of this problem. The problem is modeled as a mixed-integer program, and a problem size analysis based on real data reveals that the formulation is too complex to solve directly. Hence, a two-stage approach is proposed, where the first stage assigns employees to stations, and the second stage specifies shifts and off-days for each employee. Computational results related to solving the two-stage models directly via CPLEX and by specialized heuristics are reported. The two-stage approach provides daily schedules for employees for a given time horizon in a timely fashion, taking into consideration the employees' expressed preferences. This proposed modeling approach can be incorporated within a decision support system to replace the current manual scheduling practice that is often chaotic and has led to feelings of bias and job dissatisfaction among employees.
We study the problem of computing the lower hedging price of an American contingent claim in a finite-state discrete-time market setting under proportional transaction costs. We derive a new mixed-integer linear progr...
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We study the problem of computing the lower hedging price of an American contingent claim in a finite-state discrete-time market setting under proportional transaction costs. We derive a new mixed-integer linear programming formulation for calculating the lower hedging price. The linear programming relaxation of the formulation is exact in frictionless markets. Our results imply that it might be optimal for the holder of several identical American claims to exercise portions of the portfolio at different time points in the presence of proportional transaction costs while this incentive disappears in their absence.
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