The Experts below are selected from a list of 13533 Experts worldwide ranked by ideXlab platform
U V Shenoy - One of the best experts on this subject based on the ideXlab platform.
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pinch analysis for Aggregate production Planning in supply chains
Computers & Chemical Engineering, 2004Co-Authors: A Singhvi, K P Madhavan, U V ShenoyAbstract:Global competition has made it imperative for the process industries to manage their supply chains optimally. The complexity of the supply chain processes coupled with large computational times often makes effective supply chain management (SCM) difficult. Production system is an important component of a supply chain. This paper introduces a novel approach for Aggregate Planning of production in supply chains. The approach derives inspiration from pinch analysis, which has been extensively used in heat and mass exchanger network synthesis. By representing demand and supply data as composites, it gives planners greater insight into the SCM process and thus facilitates re-Planning and quick decision-making. Two case studies are solved, one involving a single product and another involving multiple products on a single processor. For the first case study, optimal production plans are obtained and matched with the results obtained by solving equivalent optimization problems in GAMS®. For the second case study, an algorithm is proposed to determine the sequence of production of the multiple products. The initial guess obtained by following the algorithm reduces the computational time to one-sixth of the time otherwise taken by the solver. It may be concluded that plans obtained by pinch analysis provide either the best Aggregate plans or excellent starting points to reduce the computational time for solutions by mixed integer programming formulations.
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Aggregate Planning in supply chains by pinch analysis
Chemical Engineering Research & Design, 2002Co-Authors: A Singhvi, U V ShenoyAbstract:This work presents a novel extension of the targeting methods from pinch analysis to Aggregate Planning in supply chains. Aggregate Planning aims at meeting demand over a specified time horizon in a way that maximizes profit through optimal levels of production, capacity, subcontracting, inventory, and stockouts. It is demonstrated how minimum production rates for a given demand forecast may be targeted through composite curves on a time versus material quantity plot. The grand composite curve (GCC) representation may be conveniently used to depict how surpluses and shortages in inventory fluctuate over time. The pinch corresponds to the point of minimum lead time and zero inventory. An example problem is used to illustrate the approach. The initial Aggregate plan from pinch analysis exactly matches the solution reported in literature obtained by solving a linear programming formulation. On the other hand, the final Aggregate plans from the pinch targeting method are superior to the solution in literature, as they are more realistic. It may be concluded that the production composite determined by the pinch targeting method provides either the best Aggregate plan or an excellent starting point to reduce computational time for a solution by mixed integer linear programming.
A Singhvi - One of the best experts on this subject based on the ideXlab platform.
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pinch analysis for Aggregate production Planning in supply chains
Computers & Chemical Engineering, 2004Co-Authors: A Singhvi, K P Madhavan, U V ShenoyAbstract:Global competition has made it imperative for the process industries to manage their supply chains optimally. The complexity of the supply chain processes coupled with large computational times often makes effective supply chain management (SCM) difficult. Production system is an important component of a supply chain. This paper introduces a novel approach for Aggregate Planning of production in supply chains. The approach derives inspiration from pinch analysis, which has been extensively used in heat and mass exchanger network synthesis. By representing demand and supply data as composites, it gives planners greater insight into the SCM process and thus facilitates re-Planning and quick decision-making. Two case studies are solved, one involving a single product and another involving multiple products on a single processor. For the first case study, optimal production plans are obtained and matched with the results obtained by solving equivalent optimization problems in GAMS®. For the second case study, an algorithm is proposed to determine the sequence of production of the multiple products. The initial guess obtained by following the algorithm reduces the computational time to one-sixth of the time otherwise taken by the solver. It may be concluded that plans obtained by pinch analysis provide either the best Aggregate plans or excellent starting points to reduce the computational time for solutions by mixed integer programming formulations.
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Aggregate Planning in supply chains by pinch analysis
Chemical Engineering Research & Design, 2002Co-Authors: A Singhvi, U V ShenoyAbstract:This work presents a novel extension of the targeting methods from pinch analysis to Aggregate Planning in supply chains. Aggregate Planning aims at meeting demand over a specified time horizon in a way that maximizes profit through optimal levels of production, capacity, subcontracting, inventory, and stockouts. It is demonstrated how minimum production rates for a given demand forecast may be targeted through composite curves on a time versus material quantity plot. The grand composite curve (GCC) representation may be conveniently used to depict how surpluses and shortages in inventory fluctuate over time. The pinch corresponds to the point of minimum lead time and zero inventory. An example problem is used to illustrate the approach. The initial Aggregate plan from pinch analysis exactly matches the solution reported in literature obtained by solving a linear programming formulation. On the other hand, the final Aggregate plans from the pinch targeting method are superior to the solution in literature, as they are more realistic. It may be concluded that the production composite determined by the pinch targeting method provides either the best Aggregate plan or an excellent starting point to reduce computational time for a solution by mixed integer linear programming.
Dominic C. Y. Foo - One of the best experts on this subject based on the ideXlab platform.
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Automated targeting model for Aggregate Planning in production and energy supply chains
Clean Technologies and Environmental Policy, 2016Co-Authors: Dominic C. Y. FooAbstract:Aggregate Planning aims to maximize profit for a supply chain while satisfying its demand. In this work, the automated targeting model (ATM) that was originally developed for resource conservation network is extended for use in Aggregate Planning for production and energy supply chains. The ATM is an optimization framework that is based on the insight-based technique of pinch analysis, with the philosophy in setting target(s) ahead of detailed Planning. Being an optimization framework, the ATM offers other advantages than conventional pinch analysis technique by incorporating more case-specific constraints, and is able to handle more complex optimization problems. Three literature examples and an industrial polymer production case study are solved to show the robustness of ATM.
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a heuristic based algebraic targeting technique for Aggregate Planning in supply chains
Computers & Chemical Engineering, 2008Co-Authors: Dominic C. Y. Foo, Mike B L Ooi, Raymond R Tan, Jenny S TanAbstract:Process integration techniques have seen its establishment in many non-conventional applications in the last decade. One of the newest applications of process integration technique is in the area of supply chain management. The well-established pinch analysis tools of composite curves and grand composite curves have been demonstrated their adaptability in this new area. Although the graphical tools provide many important insights for production planners, the common limitations of these graphical tools such as inaccuracy and being cumbersome need to be overcome. This calls for an algebraic targeting approach presented in this paper, known as the supply chain cascade analysis to supplement the various graphical tools. The cascade analysis technique sets targets for a supply chain. Besides, other new insights such as minimum and maximum inventory as well as the scheduling of process shut down are being introduced in this paper. Two industrial case studies are presented to illustrate the proposed method.
Thomas E Morton - One of the best experts on this subject based on the ideXlab platform.
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a periodic review production Planning model with uncertain capacity and uncertain demand optimality of extended myopic policies
Management Science, 1994Co-Authors: Frank W Ciarallo, Ramakrishna Akella, Thomas E MortonAbstract:Increasing product complexity, manufacturing environment complexity and an increased emphasis on product quality are all factors leading to uncertainties in production processes. These uncertainties are in the form of unplanned machine maintenance, varying production yields and rework, among others. In Planning for production, an adequate model must incorporate these uncertainties into the representation of the production process. This paper treats the Aggregate Planning problem for a single product with random demand and random capacity. In the single-period problem, random capacity does not affect the optimal policy but results in a unimodal, nonconvex cost function. In the multiple-period and infinite-horizon settings order-up-to policies that are dependent on the distribution of capacity are shown to be optimal in spite of a nonconvex cost. In the infinite-horizon setting an intuitive description of the situation leads to the notion of a class of extended myopic policies, requiring the consideration o...
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a periodic review production Planning model with uncertain capacity and uncertain demand optimality of extended myopic policies
Management Science, 1994Co-Authors: Frank W Ciarallo, Ramakrishna Akella, Thomas E MortonAbstract:Increasing product complexity, manufacturing environment complexity and an increased emphasis on product quality are all factors leading to uncertainties in production processes. These uncertainties are in the form of unplanned machine maintenance, varying production yields and rework, among others. In Planning for production, an adequate model must incorporate these uncertainties into the representation of the production process. This paper treats the Aggregate Planning problem for a single product with random demand and random capacity. In the single-period problem, random capacity does not affect the optimal policy but results in a unimodal, nonconvex cost function. In the multiple-period and infinite-horizon settings order-up-to policies that are dependent on the distribution of capacity are shown to be optimal in spite of a nonconvex cost. In the infinite-horizon setting an intuitive description of the situation leads to the notion of a class of extended myopic policies, requiring the consideration of review periods of uncertain length.
Metin Turkay - One of the best experts on this subject based on the ideXlab platform.
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mathematical models for Aggregate Planning
2021Co-Authors: Seyyed Amir Babak Rasmi, Metin TurkayAbstract:In this chapter, we construct three mathematical models for Aggregate Planning. We start with a basic model that includes workforce requirements, inventory levels, and stock-out. Then, we spread the model such that the fixed cost of manufacturing products in a period is included. We also change the model to include promotion programs. Hence, a manufacturer may invest in these programs which result in a demand increase. Moreover, backlogs are considerable instead of lost sales (stock-out). Next, we state the importance of sustainability in supply chain management and Aggregate Planning, and propose some indicators which represent sustainability considerations in an Aggregate plan. Thus, the third model is a sustainable Aggregate plan that includes three objective functions.
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introduction to Aggregate Planning and strategies
2021Co-Authors: Seyyed Amir Babak Rasmi, Metin TurkayAbstract:In this chapter, we discuss what Aggregate Planning is and how managers exploit various strategies in Aggregate Planning. A demand prediction for the Planning time frame (usually 2–18 months) is required to establish an Aggregate plan. Then, this Aggregate plan provides an intermediate-term plan that determines the production rate, subcontracting/outsourcing, inventory, backlogs, and promotions in the supply chain for the next 2–18 months. Different strategies can be used in Aggregate Planning: constant workforce, constant production rate, or chase strategy. A planner decides between several pure strategies and mixed strategies. Moreover, an Aggregate plan results in an optimal solution that may be against environmental and social concerns. Aggregate Planning possesses sufficient flexibility to be improved such that some sustainability considerations can be taken into account.
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solution methods for Aggregate Planning problems using python
2021Co-Authors: Seyyed Amir Babak Rasmi, Metin TurkayAbstract:There are several programming languages that can be used for solving the models presented in the previous chapter. For single objective optimization models such as BM1 and BM2, we use the Pulp optimzer in Python; for the sustainable Aggregate Planning problem, a scalarization technique is implemented in Python to generate a large number of non-dominated points.
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sustainability in supply chain management Aggregate Planning from sustainability perspective
PLOS ONE, 2016Co-Authors: Metin Turkay, Ozturk Saracoglu, Mehmet Can ArslanAbstract:Supply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate Planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the Aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard Aggregate Planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in Aggregate Planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.