One of the significant effects of the implementation of an open-door policy in China is that many Hong Kong-based manufacturers' production lines have been moved to China to take advantage of the lower production ...
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One of the significant effects of the implementation of an open-door policy in China is that many Hong Kong-based manufacturers' production lines have been moved to China to take advantage of the lower production costs, lower wages and lower rental costs, but as a consequence the finished products must be delivered from China to Hong Kong. It has been discovered that, given a noisy set of data, distribution management cannot determine an appropriate strategy, and hence unnecessarily high expenditure is being incurred. In this paper, a stochastic linear programming model is developed to solve cross-border distribution problems in an environment of uncertainty. Under different economic growth scenarios., decision-makers can determine a long-term distribution strategy, including the optimal delivery routes and the optimal vehicle fleet composition. A set of data from a Hong Kong-based manufacturing company is used to demonstrate the robustness and effectiveness of our model. The analysis of two possible changes in distribution strategies is also considered. The proposed model can provide appropriate distribution strategy with fleet management in an uncertain environment.
Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan - consisting of the quantity of production and the workforce level...
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Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan - consisting of the quantity of production and the workforce level at each production plant - to fulfil market demand. This paper addresses the production planning problem with additional constraints, such as production plant preference selection. To deal with the uncertain demand data, a stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model. An analysis of the probability distribution of economic demand assumptions is performed. The impact of unit shortage costs on the total cost is also analysed.
The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast....
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The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast. Achieving these goals is complicated because of uncertainties in projects' returns and durations. We develop new disjunctive stochastic programming models that capture the above-mentioned NPD goals. The first stochastic model is static, representing the traditional waterfall product development process, whereas the second one is dynamic, representing the agile product development process. We design a reformulation method and a decomposition algorithm to solve a problem encountered by a U.S.-based software start-up company. Our results indicate counter-intuitively that high reliability in attaining a targeted return may be achieved by investing in projects with a longer development time and higher risk. Furthermore, we show that if the capability to make dynamic decisions is overlooked while available, the time to attain the targeted return is overestimated.
This research studies the impact of demand uncertainty to the supply chain design by using a stochastic programming approach. Each potential location has two modes of supply: long response time used before the demand ...
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This research studies the impact of demand uncertainty to the supply chain design by using a stochastic programming approach. Each potential location has two modes of supply: long response time used before the demand is realized and short response lead time mode (with higher cost) used after the demand is realized. The capacity of each model will be optimally determined from the model. This means that each location allow the manufacturer to install machines to produce in a large quantity at low cost (due to economy of scale) and keep in the internal warehouse or to install flexible rapid response machines to produce with short lead time at high cost after the demand shortage is expected. Moreover, the model allows different production cost functions which the unit cost could be different when production quantity is different using piecewise function. A stochastic programming model is developed to handle the situation explained. We have conducted 4 experiments to test the model in different perspective; (1) Sampling test – to test the effect of the sample size to the result, (2) Parameter variation – to test how each variable affect the results, (3) Cost function testing – to reveal the property of different cost function and (4) Pair-T test to prove how short LT response facility could improve certain situation and mitigate the effect of demand uncertainty. Furthermore, for heuristic part, we applied linear relaxation and decomposition method on two binary variables to separate the model into two phases; (1) Location decision and (2) Segmentation decision. For the result, heuristic model affords to contribute the optimal result for about 26 out of 32 instances or 81.25% of the total number of computable instances deriving from every cost function. The average overall total profit gap is 0.32%. The average computational time reduction is 71.65%. Moreover, our heuristics model is able to solve big size problems which the optimal model failed to do within acceptable ti
The fleet assignment model assigns a fleet of aircraft types to the scheduled flight legs in an airline timetable published six to twelve weeks prior to the departure of the aircraft. The objective is to maximize prof...
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The fleet assignment model assigns a fleet of aircraft types to the scheduled flight legs in an airline timetable published six to twelve weeks prior to the departure of the aircraft. The objective is to maximize profit. While costs associated with assigning a particular fleet type to a leg are easy to estimate, the revenues are based upon demand, which is realized close to departure. The uncertainty in demand makes it challenging to assign the right type of aircraft to each flight leg based on forecasts taken six to twelve weeks prior to departure. Therefore, in this paper, a two-stage stochastic programming framework has been developed to model the uncertainty in demand, along with the Boeing concept of demand driven dispatch to reallocate aircraft closer to the departure of the aircraft. Traditionally, two-stage stochastic programming problems are solved using the L-shaped method. Due to the slow convergence of the L-shaped method, a novel multivariate adaptive regression splines cutting plane method has been developed. The results obtained from our approach are compared to that of the L-shaped method, and the value of demand-driven dispatch is estimated. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
Day-ahead scheduling of electricity generation or unit commitment is an important and challenging optimization problem in power systems. Variability in net load arising from the increasing penetration of renewable tec...
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Day-ahead scheduling of electricity generation or unit commitment is an important and challenging optimization problem in power systems. Variability in net load arising from the increasing penetration of renewable technologies has motivated study of various classes of stochastic unit commitment models. In two-stage models, the generation schedule for the entire day is fixed while the dispatch is adapted to the uncertainty, whereas in multi-stage models the generation schedule is also allowed to dynamically adapt to the uncertainty realization. Multi-stage models provide more flexibility in the generation schedule;however, they require significantly higher computational effort than two-stage models. To justify this additional computational effort, we provide theoretical and empirical analyses of the value of multi-stage solution for risk-averse multi-stage stochastic unit commitment models. The value of multi-stage solution measures the relative advantage of multi-stage solutions over their two-stage counterparts. Our results indicate that, for unit commitment models, the value of multi-stage solution increases with the level of uncertainty and number of periods, and decreases with the degree of risk aversion of the decision maker.
Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times...
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Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions. (C) 2011 Elsevier B.V. All rights reserved.
We study the decision-making problem in cybersecurity risk planning concerning resource allocation strategies by government and firms. Aiming to minimize the social costs incurred due to cyberattacks, we consider not ...
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We study the decision-making problem in cybersecurity risk planning concerning resource allocation strategies by government and firms. Aiming to minimize the social costs incurred due to cyberattacks, we consider not only the monetary investment costs but also the deprivation costs due to detection and containment delays. We also consider the effect of positive externalities of the overall cybersecurity investment on an individual firm's resource allocation attitude. The optimal decision guides the firms on the countermeasure portfolio mix (detection vs. prevention vs. containment) and government intelligence investments while accounting for actions of a strategic attacker and firm budgetary limitations. We accomplish this via a two-stage stochastic programming model. In the first stage, firms decide on prevention and detection investments aided by government intelligence investments that improve detection effectiveness. In the second stage, once the attacker's actions are realized, firms decide on containment investments after evaluating the cyberattacks. We demonstrate the applicability of our model via a case study. We find that externality can reduce the government's intelligence investment and that the firm's detection investment receives priority over containment. We also note that while prevention effectiveness has a decreasing impact on intelligence, it is beneficial to spend more on intelligence given its increasing returns to the reduction of social costs related to cybersecurity. (C) 2020 Elsevier B.V. All rights reserved.
A stochastic programming model for designing logistics networks integrating reverse logistics into current supply chains is proposed in this paper. It aims at evaluating impacts of randomness related to recovery, proc...
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A stochastic programming model for designing logistics networks integrating reverse logistics into current supply chains is proposed in this paper. It aims at evaluating impacts of randomness related to recovery, processing and demand volumes on the design decisions. These decisions deal with the location of service and processing centres and warehouses, regarding processed products with reuse potential, the definition of mission of sites and, consequently, the product accessibility. Flows of recovered products may be directed toward one or a number of processing alternatives, according to the different states of the recovered products as well as to the network conditions, which relate to recovery and demand volumes with respect to capacity constraints and operating costs. Notably, recovered products may be repaired, disassembled for part refurbishing or disposed. Such products are indicated here as valorized products and represent an economical supply alternative, which meets lower quality standards in comparison with new products. Portions of needs fulfilled by valorized products are defined according to the requirements of end-users, as well as management policies and strategies. The model aims at improving valorized product accessibility, while reducing the total operating costs of such a network. A heuristics based on the sample average approximation, involving the Monte Carlo sampling methods, is proposed to solve the problem. (C) 2008 Elsevier B.V. All rights reserved.
We show that a Riccati-based Multistage stochastic programming solver for problems with separable convex linear/nonlinear objective developed in previous papers can be extended to solve more general stochastic Program...
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We show that a Riccati-based Multistage stochastic programming solver for problems with separable convex linear/nonlinear objective developed in previous papers can be extended to solve more general stochastic programming problems. With a Lagrangean relaxation approach, also local and global equality constraints can be handled by the Riccati-based primal interior point solver. The efficiency of the approach is demonstrated on a 10 staged stochastic programming problem containing both local and global equality constraints. The problem has 1.9 million scenarios, 67 million variables and 119 million constraints, and was solved in 97 min on a 32 node PC cluster.
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