The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ghasemy R Yaghin - One of the best experts on this subject based on the ideXlab platform.
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enhancing supply chain Production marketing planning with geometric multivariate demand function a case study of textile industry
Computers & Industrial Engineering, 2020Co-Authors: Ghasemy R YaghinAbstract:Abstract In this paper, a multi-period, multi-product, multi-site, multi-sales channel Aggregate Production planning problem including ordering preferences is presented in an integrated two-echelon supply chain to avoid the sub-optimality caused by separate, sequential decisions of Production and the marketing/retailing chain. Each customer demand class is affected by price, marketing expenditures and product quality involving customer willingness-to-pay. In addition, the immigration of customers between submarkets (i.e. cannibalization) is considered in the market-segmented environment due to imperfect segmentation. This research develops a geometric programming model to formulate the issue of joint price differentiation and multi-site Aggregate Production planning decisions by maximizing the total profit of the supply chain. To tackle the model and obtain solutions, we tailor an efficient analytical solution procedure to convert the original highly non-linear programming model into a convex programming equivalent. Finally, a numerical study of garment supply chain is presented to demonstrate the performance of the model and solution approaches. The research findings indicate a positive relationship between the scaling constant of price-dependent demand and the total profit rate. Moreover, as price gaps grow, the utility of price differentiation is decreased.
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integrated multi site Aggregate Production pricing planning in a two echelon supply chain with multiple demand classes
Applied Mathematical Modelling, 2018Co-Authors: Ghasemy R YaghinAbstract:Abstract This paper proposes a Production and differential pricing decision model in a two-echelon supply chain that involves a demand from two or more market segments. In this framework, the retailer is allowed to set different prices during the planning horizon. While integrated Production-marketing management has been a key research issue in supply chain management for a long time, little attention has been given to set prices and marketing expenditures in integrated multi-site (parallel) manufacturing systems and multiple demand classes. Generally, the presence of multiple demand classes induced by different market segments may impose demand leakage and then change Production plan and ordering policies throughout the supply chain system. To tackle this problem, this paper develops a novel approach in order to provide an optimal Aggregate Production and marketing plan by interconnecting the sales channels of the retailer and demand. A non-linear model is established to determine optimal price differentiation, marketing expenditures and Production plans of manufacturing sites in a multi-period, multi-product and multi-sale channels Production planning problem by maximizing total profit of the supply chain. To handle the model and obtain solutions, we propose an efficient analytical model based upon convex hulls. Finally, we apply the proposed procedure to a clothing company in order to show usefulness and significance of the model and solution method.
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integrated markdown pricing and Aggregate Production planning in a two echelon supply chain a hybrid fuzzy multiple objective approach
Applied Mathematical Modelling, 2012Co-Authors: Ghasemy R Yaghin, S A Torabi, S Fatemi M T GhomiAbstract:Given high variability of demands for short life cycle products, a retailer has to decide about the products’ prices and order quantities from a manufacturer. In the meantime, the manufacturer has to determine an Aggregate Production plan involving for example, Production, inventory and work force levels in a multi period, multi product environment. Due to imprecise and fuzzy nature of products’ parameters such as unit Production and replenishment costs, a hybrid fuzzy multi-objective programming model including both quantative and qualitative constraints and objectives is proposed to determine the optimalprice markdown policy and Aggregate Production planning in a two echelon supply chain. The model aims to maximize the total profit of manufacturer, the total profit of retailer and improving service aspects of retailing simultaneously. After applying appropriate strategies to defuzzify the original model, the equivalent multi-objective crisp model is then solved by a fuzzy goal programming method. An illustrative example is also provided to show the applicability and usefulness of the proposed model and solution method.
Zeinab Sazvar - One of the best experts on this subject based on the ideXlab platform.
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a stochastic Aggregate Production planning model in a green supply chain considering flexible lead times nonlinear purchase and shortage cost functions
European Journal of Operational Research, 2013Co-Authors: S Mirzapour M J Alehashem, Armand Baboli, Zeinab SazvarAbstract:In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site Aggregate Production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model.
S Mirzapour M J Alehashem - One of the best experts on this subject based on the ideXlab platform.
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a stochastic Aggregate Production planning model in a green supply chain considering flexible lead times nonlinear purchase and shortage cost functions
European Journal of Operational Research, 2013Co-Authors: S Mirzapour M J Alehashem, Armand Baboli, Zeinab SazvarAbstract:In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site Aggregate Production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model.
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a multi objective robust optimization model for multi product multi site Aggregate Production planning in a supply chain under uncertainty
International Journal of Production Economics, 2011Co-Authors: S Mirzapour M J Alehashem, H Malekly, M B AryanezhadAbstract:Abstract Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product Aggregate Production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including Production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient Production planning in a supply chain.
M B Aryanezhad - One of the best experts on this subject based on the ideXlab platform.
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an efficient algorithm to solve a multi objective robust Aggregate Production planning in an uncertain environment
The International Journal of Advanced Manufacturing Technology, 2012Co-Authors: Seyed Mohamad Javad Mirzapour Alehashem, M B Aryanezhad, Seyed Jafar SadjadiAbstract:Risk is inherent in most economic activities. This is especially true of Production activities where results of decisions made today may have many possible different outcomes depending on future events. Since companies cannot usually protect themselves completely against risk, they have to manage it. In this paper, we present a multi-objective model to deal with a multi-period multi-product multi-site Aggregate Production planning problem for a medium-term planning horizon under uncertainty. The first objective function attempts to minimize sum of the expected value and the variability of total costs with reference to inventory levels, regular, overtime and subcontracting levels, backordering levels, and labor, machine and warehouse capacities. The second objective function highlighted the concept of customer service level through minimizing the expected value of maximum shortages among all customers’ zones from which the variability of that is conducted. The last objective function aims to maximize workers productivity, a weighted average of productivity levels in all factories and in all periods which is weighted by the number of k-level labors. Then, we use an efficient algorithm that is a combination of an augmented e-constraint method and genetic algorithm to solve our proposed model. The results demonstrate the practicability of the proposed multi-objective stochastic model as well as the proposed algorithm.
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a multi objective robust optimization model for multi product multi site Aggregate Production planning in a supply chain under uncertainty
International Journal of Production Economics, 2011Co-Authors: S Mirzapour M J Alehashem, H Malekly, M B AryanezhadAbstract:Abstract Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product Aggregate Production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including Production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient Production planning in a supply chain.
Adil Baykasoglu - One of the best experts on this subject based on the ideXlab platform.
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multi objective Aggregate Production planning with fuzzy parameters
Advances in Engineering Software, 2010Co-Authors: Adil Baykasoglu, Tolunay GockenAbstract:In this paper, a direct solution method that is based on ranking methods of fuzzy numbers and tabu search is proposed to solve fuzzy multi-objective Aggregate Production planning problem. The parameters of the problem are defined as triangular fuzzy numbers. During problem solution four different fuzzy ranking methods are employed/tested. One of the primary objectives of this study is to show that how a multi-objective Aggregate Production planning problem which is stated as a fuzzy mathematical programming model can also be solved directly (without needing a transformation process) by employing fuzzy ranking methods and a metaheuristic algorithm. The results show that this can be easily achieved.
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moapps 1 0 Aggregate Production planning using the multiple objective tabu search
International Journal of Production Research, 2001Co-Authors: Adil BaykasogluAbstract:In recent years, there has been a trend in the research community to solve large-scale complex planning and design problems using the modern heuristics optimization techniques (i.e. tabu search, genetic algorithms, etc.). This is mainly due to unsuitability of the classical solution techniques in many circumstances. Depending upon the assumptions made and the modelling approach used, Aggregate Production planning (APP) problems can be quite complex and large scale. Therefore, there is a need to investigate the suitability of modern heuristics for their solution. In this paper, the multiple-objective APP problem is formulated as a pre-emptive goal-programming model and solved by a specially developed multiple-objective tabu search algorithm. The mathematical formulation is built upon Masud and Hwang's model (original model) due to its extensibility characteristics. The present model extents their model by including subcontracting and setup decisions. The multiple-objective tabu search algorithm is applied ...