The Experts below are selected from a list of 144495 Experts worldwide ranked by ideXlab platform
Patroklos Georgiadis - One of the best experts on this subject based on the ideXlab platform.
-
an integrated system dynamics model for strategic Capacity Planning in closed loop recycling networks a dynamic analysis for the paper industry
Simulation Modelling Practice and Theory, 2013Co-Authors: Patroklos GeorgiadisAbstract:Abstract Recycling activities have demonstrated a remarkable increase over the last decade due to the economic and environmental dimensions of sustainability. In particular, Capacity Planning in production facilities has become a strategic issue of key importance affecting the profitability of the recycling industry. By integrating the simulation discipline and the feedback control theory into a dynamic consideration of recycling networks, this paper proposes a System Dynamics (SD) model for strategic Capacity Planning in the recycling industry. The decision-making process is based on a balanced tradeoff between profit and Capacity utilization for a single producer with closed-loop recycling activities. The SD model captures physical stocks and flows apparent in real-world recycling networks and includes the feedback mechanisms which regulate these flows. When used as an “experimental tool”, the model tests alternative Capacity Planning policies and demonstrates policy suggestions for the forward and reverse channels, which maximize profitability over a strategic Planning horizon. This experimentation is illustrated by using data from a paper producer with recycling activities, as a real-world test case. Extensive simulation runs, investigate the efficiency of a wide range Capacity acquisition decisions, using total company profit as the measure of performance. Although such an analysis may differ from one recycling network to another, it has been kept as generic as possible to facilitate its applicability to a wide-spectrum of real-world local, regional or global networks.
-
the impact of product lifecycle on Capacity Planning of closed loop supply chains with remanufacturing
Production and Operations Management, 2009Co-Authors: Patroklos Georgiadis, Dimitrios Vlachos, George TagarasAbstract:Product recovery operations in reverse supply chains face rapidly changing demand due to the increasing number of product offerings with reduced lifecycles. Therefore, Capacity Planning becomes a strategic issue of major importance for the profitability of closed-loop supply chains. This work studies a closed-loop supply chain with remanufacturing and presents dynamic Capacity Planning policies developed through the methodology of System Dynamics. The key issue of the paper is how the lifecycles and return patterns of various products affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The model can be used to identify effective policies, to conduct various “what-if” analyses, and to answer questions about the long-term profitability of reverse supply chains with remanufacturing. The results of numerical examples with quite different lifecycle and return patterns show how the optimal collection expansion/contraction and remanufacturing contraction policies depend on the lifecycle type and the average usage time of the product, while the remanufacturing Capacity expansion policy is not significantly affected by these factors. The results also show that the collection and remanufacturing Capacity policies are insensitive to the total product demand. The insensitivity of the optimal policies to total demand is a particularly appealing feature of the proposed model, given the difficulty in obtaining accurate demand forecasts.
-
a system dynamics model for dynamic Capacity Planning of remanufacturing in closed loop supply chains
Computers & Operations Research, 2007Co-Authors: Dimitrios Vlachos, Patroklos Georgiadis, Eleftherios IakovouAbstract:Abstract Product recovery operations in reverse supply chains face continually and rapidly changing product demand characterized by an ever increasing number of product offerings with reduced lifecycles due to both technological advancements and environmental concerns. Capacity Planning is a strategic issue of increased complexity importance for the profitability of reverse supply chains due to their highly variable return flows. In this work we tackle the development of efficient Capacity Planning policies for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues, such as the take-back obligation imposed by legislation and the “green image” effect on customer demand. The behavior of the generic system under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model provides an experimental tool, which can be used to evaluate alternative long-term Capacity Planning policies (“what-if” analysis) using total supply chain profit as measure of policy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights.
-
a system dynamics model for dynamic Capacity Planning of remanufacturing in closed loop supply chains
Computers & Operations Research, 2007Co-Authors: Dimitrios Vlachos, Patroklos Georgiadis, Eleftherios IakovouAbstract:Product recovery operations in reverse supply chains face continually and rapidly changing product demand characterized by an ever increasing number of product offerings with reduced lifecycles due to both technological advancements and environmental concerns. Capacity Planning is a strategic issue of increased complexity importance for the profitability of reverse supply chains due to their highly variable return flows. In this work we tackle the development of efficient Capacity Planning policies for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues, such as the take-back obligation imposed by legislation and the “green image” effect on customer demand. The behavior of the generic system under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model provides an experimental tool, which can be used to evaluate alternative long-term Capacity Planning policies (“what-if” analysis) using total supply chain profit as measure of policy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights. 2005 Elsevier Ltd. All rights reserved.
Lazaros G Papageorgiou - One of the best experts on this subject based on the ideXlab platform.
-
fast genetic algorithm approaches to solving discrete time mixed integer linear programming problems of Capacity Planning and scheduling of biopharmaceutical manufacture
Computers & Chemical Engineering, 2019Co-Authors: Karolis Jankauskas, Lazaros G Papageorgiou, Suzanne S FaridAbstract:Abstract The previous research work in the literature for Capacity Planning and scheduling of biopharmaceutical manufacture focused mostly on the use of mixed integer linear programming (MILP). This paper presents fast genetic algorithm (GA) approaches for solving discrete-time MILP problems of Capacity Planning and scheduling in the biopharmaceutical industry. The proposed approach is validated on two case studies from the literature and compared with MILP models. In case study 1, a medium-term Capacity Planning problem of a single-site, multi-suite, multi-product biopharmaceutical manufacture is presented. The GA is shown to achieve the global optimum on average 3.6 times faster than a MILP model. In case study 2, a larger long-term Planning problem of multi-site, multi-product bio-manufacture is solved. Using the rolling horizon strategy, the GA is demonstrated to achieve near-optimal solutions (1% away from the global optimum) as fast as a MILP model.
-
multiobjective optimisation of production distribution and Capacity Planning of global supply chains in the process industry
Omega-international Journal of Management Science, 2013Co-Authors: Songsong Liu, Lazaros G PapageorgiouAbstract:The performance of a supply chain should usually be measured by multiple criteria. We address production, distribution and Capacity Planning of global supply chains considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed-integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. Also, two strategies to expand the formulation plants' capacities are considered in the model. The e-constraint method and lexicographic minimax method are used as solution approaches to tackle the multiobjective problem. Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches. © 2012 Elsevier Ltd.
-
multiobjective optimisation of production distribution and Capacity Planning of global supply chains in the process industry
Omega-international Journal of Management Science, 2013Co-Authors: Songsong Liu, Lazaros G PapageorgiouAbstract:The performance of a supply chain should usually be measured by multiple criteria. We address production, distribution and Capacity Planning of global supply chains considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed-integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. Also, two strategies to expand the formulation plants’ capacities are considered in the model. The e-constraint method and lexicographic minimax method are used as solution approaches to tackle the multiobjective problem. Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches.
Tzu-li Chen - One of the best experts on this subject based on the ideXlab platform.
-
Medium-term multi-plant Capacity Planning problems considering auxiliary tools for the semiconductor foundry
The International Journal of Advanced Manufacturing Technology, 2013Co-Authors: Yin-yann Chen, Tzu-li Chen, Cheng-dar LiouAbstract:The foundry is an industry whose demand varies rapidly and whose manufacturing process is quite complicated. This paper explores issues on mid-term Capacity Planning in the foundry. First, issues on Capacity Planning of the foundry are categorized. Second, focusing on multi-site Planning, an increment strategy for the number of auxiliary tools—“photo mask”—is proposed to increase the flexibility of production. The related decisions include how to allocate appropriately the forecast demands of products among multiple sites and how to decide on the production quantities of products in each site after receiving customer-confirmed orders. By constructing the mathematical programming model of Capacity Planning, the rates of Capacity utilization and customer order fulfillment are found to be effectively enhanced by adding new masks to increase production flexibility. Furthermore, from the sensitivity analysis, the importance of customers is shown to influence significantly the amount of reserved Capacity of customers. Increasing the number of certified factories is also an indirect way to increase Capacity. For long life cycle products, the verification of products in multiple factories in order to increase Capacity utilization rate effectively is suggested.
-
stochastic multi site Capacity Planning of tft lcd manufacturing using expected shadow price based decomposition
Applied Mathematical Modelling, 2012Co-Authors: Tzu-li Chen, Haochun LuAbstract:Abstract This paper presents a stochastic optimization model and efficient decomposition algorithm for multi-site Capacity Planning under the uncertainty of the TFT-LCD industry. The objective of the stochastic Capacity Planning is to determine a robust Capacity allocation and expansion policy hedged against demand uncertainties because the demand forecasts faced by TFT-LCD manufacturers are usually inaccurate and vary rapidly over time. A two-stage scenario-based stochastic mixed integer programming model that extends the deterministic multi-site Capacity Planning model proposed by Chen et al. (2010) [1] is developed to discuss the multi-site Capacity Planning problem in the face of uncertain demands. In addition a three-step methodology is proposed to generate discrete demand scenarios within the stochastic optimization model by approximating the stochastic continuous demand process fitted from the historical data. An expected shadow-price based decomposition, a novel algorithm for the stage decomposition approach, is developed to obtain a near-optimal solution efficiently through iterative procedures and parallel computing. Preliminary computational study shows that the proposed decomposition algorithm successfully addresses the large-scale stochastic Capacity Planning model in terms of solution quality and computation time. The proposed algorithm also outperforms the plain use of the CPLEX MIP solver as the problem size becomes larger and the number of demand scenarios increases.
-
a stochastic programming model for strategic Capacity Planning in thin film transistor liquid crystal display tft lcd industry
Computers & Operations Research, 2011Co-Authors: James T Lin, Tzu-li Chen, Shinhui ShihAbstract:This paper studies strategic Capacity Planning problems under demand uncertainties in thin film transistor-liquid crystal display (TFT-LCD) industry. Due to the following trends, Capacity Planning has become a critical strategic issue in TFT-LCD industry: (1) complex product hierarchy and product types caused by a wide range of product applications; (2) coexistence of multiple generation of manufacturing technologies in a multi-site production system; and (3) rapid growing and changing market demand derived by the needs for replacing traditional cathode ray tube (CRT) display. Furthermore, demand forecasts are usually inaccurate and vary rapidly over time. Our research objective is to seek a Capacity allocation and expansion policy that is robust to demand uncertainties. We consider special characteristics of TFT-LCD manufacturing systems such as demand uncertainties, limited configuration flexibility, and cutting ratios. This paper proposes a scenario-based two-stage stochastic programming model for strategic Capacity Planning under demand uncertainties. Comparing to the deterministic approach, our stochastic model significantly improve system robustness under demand uncertainties.
-
a shadow price based heuristic for Capacity Planning of tft lcd manufacturing
Journal of Industrial and Management Optimization, 2009Co-Authors: Tzu-li Chen, Shucherng FangAbstract:This paper studies the Capacity Planning and expansion for the thin film transistor - liquid crystal display (TFT-LCD) manufacturing. Capacity Planning is critical to TFT-LCD industry due to its complex product hierarchy and increasing product types; the coexistence of multiple generations of manufacturing technologies in a multi-site production environment; and the rapidly growing market demands. One key purpose of Capacity Planning is to simultaneously determine the profitable "product mix" and "production quantities" of each product group across various generation sites in a particular period and the optimal "Capacity expansion quantity" of specific product groups at a certain site to improve the limited flexibility configurations through the acquisition of new auxiliary tools. This paper proposes a mixed integer linear programming model for Capacity Planning that incorporates practical characteristics and constraints in TFT-LCD manufacturing. A shadow-price based heuristic is developed to find a near-optimal solution. Preliminary computational study shows that the proposed heuristic provides good quality solutions in a reasonable amount of time. The proposed heuristic outperforms the traditional branch and bound method as the data size becomes large.
Haiqing Song - One of the best experts on this subject based on the ideXlab platform.
-
simman a simulation model for workforce Capacity Planning
Computers & Operations Research, 2009Co-Authors: Huei Chuen Huang, Haiqing Song, Loo Hay Lee, Brian T EckAbstract:Motivated by the difficulty in predicting the actual performance of workforce Capacity Planning solutions obtained from mathematical models, we develop a simulator, SimMan, to serve as a test bed for evaluating the effectiveness and robustness of different Planning options and assignment rules. To facilitate the ease of use, the simulator is developed in C++ language with a modular structure so that different users or planners can customize the modules according to their specifications. We further develop a mathematical model for the workforce Capacity Planning problem, where the uncertainty of the demand is handled by the safety stock concepts. We also suggest a number of practical Planning alternatives and assignment rules based on information from IBM. These rules are implemented as modules in SimMan and tested numerically to provide managerial insights.
-
a successive convex approximation method for multistage workforce Capacity Planning problem with turnover
European Journal of Operational Research, 2008Co-Authors: Haiqing Song, Huei Chuen HuangAbstract:Workforce Capacity Planning in human resource management is a critical and essential component of the services supply chain management. In this paper, we consider the Planning problem of transferring, hiring, or firing employees among different departments or branches of an organization under an environment of uncertain workforce demands and turnover, with the objective of minimizing the expected cost over a finite Planning horizon. We model the problem as a multistage stochastic program and propose a successive convex approximation method which solves the problem in stages and iteratively. An advantage of the method is that it can handle problems of large size where normally solving the problems by equivalent deterministic linear programs is considered to be computationally infeasible. Numerical experiments indicate that solutions obtained by the proposed method have expected costs near optimal.
George Tagaras - One of the best experts on this subject based on the ideXlab platform.
-
the impact of product lifecycle on Capacity Planning of closed loop supply chains with remanufacturing
Production and Operations Management, 2009Co-Authors: Patroklos Georgiadis, Dimitrios Vlachos, George TagarasAbstract:Product recovery operations in reverse supply chains face rapidly changing demand due to the increasing number of product offerings with reduced lifecycles. Therefore, Capacity Planning becomes a strategic issue of major importance for the profitability of closed-loop supply chains. This work studies a closed-loop supply chain with remanufacturing and presents dynamic Capacity Planning policies developed through the methodology of System Dynamics. The key issue of the paper is how the lifecycles and return patterns of various products affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The model can be used to identify effective policies, to conduct various “what-if” analyses, and to answer questions about the long-term profitability of reverse supply chains with remanufacturing. The results of numerical examples with quite different lifecycle and return patterns show how the optimal collection expansion/contraction and remanufacturing contraction policies depend on the lifecycle type and the average usage time of the product, while the remanufacturing Capacity expansion policy is not significantly affected by these factors. The results also show that the collection and remanufacturing Capacity policies are insensitive to the total product demand. The insensitivity of the optimal policies to total demand is a particularly appealing feature of the proposed model, given the difficulty in obtaining accurate demand forecasts.
