The Experts below are selected from a list of 44616 Experts worldwide ranked by ideXlab platform
Emmett J. Lodree - One of the best experts on this subject based on the ideXlab platform.
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Pre‐storm emergency supplies Inventory Planning
Journal of Humanitarian Logistics and Supply Chain Management, 2011Co-Authors: Emmett J. LodreeAbstract:Purpose – Planning inventories for emergency supplies such as bottled water, non‐perishable foods, batteries, and flashlights can be challenging for retailers situated within the projected path of a severe storm. The retailer's Inventory decisions are complicated by the inherent volatility of storm forecasts and the corresponding demand predictions. The purpose of this paper is to explore both proactive and reactive Inventory control policies within the context of probable pre‐storm demand surge for a fast‐moving emergency supply item, and identify the conditions that are most conducive to each strategy according to the minimax decision criterion.Design/methodology/approach – The Inventory system is formulated based on an underlying economic order quantity framework. Minimax decision rules are developed analytically. Sensitivity analysis is facilitated by both analytic and numerical methods.Findings – The conditions that are conducive to a proactive ordering strategy are limited supplier flexibility, acut...
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pre storm emergency supplies Inventory Planning
Journal of Humanitarian Logistics and Supply Chain Management, 2011Co-Authors: Emmett J. LodreeAbstract:Purpose – Planning inventories for emergency supplies such as bottled water, non‐perishable foods, batteries, and flashlights can be challenging for retailers situated within the projected path of a severe storm. The retailer's Inventory decisions are complicated by the inherent volatility of storm forecasts and the corresponding demand predictions. The purpose of this paper is to explore both proactive and reactive Inventory control policies within the context of probable pre‐storm demand surge for a fast‐moving emergency supply item, and identify the conditions that are most conducive to each strategy according to the minimax decision criterion.Design/methodology/approach – The Inventory system is formulated based on an underlying economic order quantity framework. Minimax decision rules are developed analytically. Sensitivity analysis is facilitated by both analytic and numerical methods.Findings – The conditions that are conducive to a proactive ordering strategy are limited supplier flexibility, acut...
Srikanta Routroy - One of the best experts on this subject based on the ideXlab platform.
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Traditional Inventory Planning to Multi-Echelon Supply Chain Inventory Planning: A Critical Review
2010Co-Authors: Srikanta RoutroyAbstract:The Inventory Planning problem was first addressed with assumptions of a single echelon and a single non-perishable product with deterministic independent demand without any constraints; but now, the research has shifted to a multi-echelon supply chain Inventory Planning with many constraints arising from supply chain members at different stages with stochastic behavior in terms of demand and lead time for both perishable and non-perishable products. Multi-echelon Inventory Planning has been a particularly difficult problem to solve in supply chain and not much progress has been made in this context to be useful to managers. The different issues of traditional Inventory systems and multi-echelon supply chain Inventory systems are discussed in detail.
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Multi-echelon Supply Chain Inventory Planning with demand and leadtime uncertainty
International Journal of Operational Research, 2009Co-Authors: Srikanta Routroy, Krishna Chaitanya MaddalaAbstract:The paper discusses a generic model for Multi-echelon Supply Chain Inventory Planning (MSCIP) with demand and leadtime uncertanity considering the Total Supply Chain (TSC) cost, which consists of Supply Chain Inventory Capital (SCIC), Supply Chain Ordering/set-up Cost (SCOC) and Supply Chain Stock-out Cost (SCSC) for a Maximum Allowable Supply Chain Inventory (MASCI). The Differential Evolution (DE/best/1/bin) strategy is used to solve MSCIP problem to determine ordering/production quantity and service level that should be maintained by each member of the supply chain. One case situation is elucidated to reinforce the salient features of the concept. A sensitivity analysis is carried out to know how the TSC is varying along MASCI in different environments.
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Inventory Planning for a multi-echelon supply chain
International Journal of Operational Research, 2007Co-Authors: Srikanta Routroy, Prasad SanisettyAbstract:Inventory Planning is the significant operational issue in supply chain management. In this paper, a mathematical model is developed for multi-echelon supply chain Inventory Planning to determine ordering/production quantity and reorder point while minimising the Total Supply Chain Cost (TSCC) (i.e., supply chain Inventory capital, supply chain ordering/set-up cost and supply chain Inventory stock out cost). Differential Evolution algorithm is used for multi-echelon supply chain Inventory Planning. One case situation is elucidated in order to reinforce the salient features of the concept. A sensitivity analysis is also carried out to show the effect of input parameters on the TSCC.
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Inventory Planning for a multi-echelon supply chain
International Journal of Operational Research, 2007Co-Authors: Srikanta Routroy, Prasad SanisettyAbstract:Inventory Planning is the significant operational issue in supply chain management. In this paper, a mathematical model is developed for multi-echelon supply chain Inventory Planning to determine ordering/production quantity and reorder point while minimising the Total Supply Chain Cost (TSCC) (i.e., supply chain Inventory capital, supply chain ordering/set-up cost and supply chain Inventory stock out cost). Differential Evolution algorithm is used for multi-echelon supply chain Inventory Planning. One case situation is elucidated in order to reinforce the salient features of the concept. A sensitivity analysis is also carried out to show the effect of input parameters on the TSCC.
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Differential evolution algorithm for supply chain Inventory Planning
Journal of Manufacturing Technology Management, 2005Co-Authors: Srikanta Routroy, Rambabu KodaliAbstract:Purpose – This paper discusses the Inventory Planning of a supply chain, which consists of a manufacturer, distributor and retailer.Design/methodology/approach – The differential evolution algorithm is developed to minimize the total system‐wide cost, which consists of supply chain Inventory capital, supply chain ordering/set‐up cost and supply chain stock‐out cost.Findings – The differential evolution algorithm helps in determining ordering/production quantity and Inventory/service level that should be maintained by each member of the supply chain.Originality/value – The algorithm developed is useful in increasing the customer service level and in decreasing the Inventory level throughout the supply chain.
Fangruo Chen - One of the best experts on this subject based on the ideXlab platform.
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salesforce incentives market information and production Inventory Planning
Management Science, 2005Co-Authors: Fangruo ChenAbstract:Salespeople are the eyes and ears of the firms they serve. They possess market knowledge that is critical for a wide range of decisions. A key question is how a firm can provide incentives to its salesforce so that it is in their interest to truthfully disclose their information about the market and to work hard. Many people have considered this question and provided solutions. Perhaps the most well-known solution is due to Gonik (1978), who proposed and implemented a clever scheme designed to elicit market information and encourage hard work. The purpose of this paper is to study Gonik's scheme and compare it with a menu of linear contracts--a solution often used in the agency literature--in a model where the market information possessed by the salesforce is important for the firm's production and Inventory-Planning decisions.
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Salesforce Incentives, Market Information, and Production/Inventory Planning
Management Science, 2005Co-Authors: Fangruo ChenAbstract:Salespeople are the eyes and ears of the firms they serve. They possess market knowledge that is critical for a wide range of decisions. A key question is how a firm can provide incentives to its salesforce so that it is in their interest to truthfully disclose their information about the market and to work hard. Many people have considered this question and provided solutions. Perhaps the most well-known solution is due to Gonik (1978), who proposed and implemented a clever scheme designed to elicit market information and encourage hard work. The purpose of this paper is to study Gonik's scheme and compare it with a menu of linear contracts--a solution often used in the agency literature--in a model where the market information possessed by the salesforce is important for the firm's production and Inventory-Planning decisions.
Tongdan Jin - One of the best experts on this subject based on the ideXlab platform.
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multistage stochastic optimization for production Inventory Planning with intermittent renewable energy
Production and Operations Management, 2017Co-Authors: Mehdi Golari, Neng Fan, Tongdan JinAbstract:A growing number of companies install wind and solar generators in their energy-intensive facilities to attain low-carbon manufacturing. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This paper presents a multi-period, production-Inventory Planning model in a multi-plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic Planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high-level renewables penetration in large manufacturing system is discussed and justified. This article is protected by copyright. All rights reserved.
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Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy
Production and Operations Management, 2016Co-Authors: Mehdi Golari, Neng Fan, Tongdan JinAbstract:A growing number of companies install wind and solar generators in their energy-intensive facilities to attain low-carbon manufacturing. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This paper presents a multi-period, production-Inventory Planning model in a multi-plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic Planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high-level renewables penetration in large manufacturing system is discussed and justified. This article is protected by copyright. All rights reserved.
Robert John - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective optimisation in Inventory Planning with supplier selection
Expert Systems with Applications, 2017Co-Authors: Seda Trk, Ender Zcan, Robert JohnAbstract:Proposed a two-stage integrated approach for rating suppliers and allocating orders.It is the first time, MOEAs are used to solve an integrated two stage fuzzy SCM problem.NSGA-II, SPEA-II and IBEA are evaluated.NSGA-II performance is better than other algorithms over 24 instances. Supplier selection and Inventory Planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we present a two-stage integrated approach to the supplier selection and Inventory Planning. In the first stage, suppliers are ranked based on various criteria, including cost, delivery, service and product quality using Interval Type-2 Fuzzy Sets (IT2FS)s. In the following stage, an Inventory model is created. Then, an Multi-objective Evolutionary Algorithm (MOEA) is utilised simultaneously minimising the conflicting objectives of supply chain operation cost and supplier risk. We evaluated the performance of three MOEAs with tuned parameter settings, namely NSGA-II, SPEA2 and IBEA on a total of twenty four synthetic and real world problem instances. The empirical results show that in the overall, NSGA-II is the best performing MOEA producing high quality trade-off solutions to the integrated problem of supplier selection and Inventory Planning.
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CEC - A simulated annealing approach to supplier selection aware Inventory Planning
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Seda Turk, Simon Miller, Ender Özcan, Robert JohnAbstract:Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. Also, appropriate Inventory management is critical to the success of a supply chain operation. In recent years, there has been a growing interest in the area of selection of an appropriate vendor and creating good Inventory Planning using supplier selection information. In this paper, we consider both of these tasks in a two-stage approach employing Interval Type-2 Fuzzy Sets (IT2FS) and Simulated Annealing (SA). In the first stage, the supplier selection problem is solved by using IT2FS for ranking the suppliers. We present an Inventory model incorporating information from the first stage that captures the influence of supplier risk on the total cost of supply chain operation. In the second stage, SA is used for solving the Inventory Planning problem based on this model improving on both supply chain operation cost and supplier risk. In this study, we evaluated our approach using different scenarios and scalarisation techniques for SA to handle two objectives, simultaneously.
