The Experts below are selected from a list of 30174 Experts worldwide ranked by ideXlab platform
Manas Kumar Maiti - One of the best experts on this subject based on the ideXlab platform.
-
a fuzzy genetic algorithm with varying population size to solve an inventory model with credit linked promotional demand in an imprecise Planning Horizon
European Journal of Operational Research, 2011Co-Authors: Manas Kumar MaitiAbstract:A genetic algorithm (GA) with varying population size is developed where crossover probability is a function of parents' age-type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and possibility theory. It is an improved GA where a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set. This GA is used to make managerial decision for an inventory model of a newly launched product. It is assumed that lifetime of the product is finite and imprecise (fuzzy) in nature. Here wholesaler/producer offers a delay period of payment to its retailers to capture the market. Due to this facility retailer also offers a fixed credit-period to its customers for some cycles to boost the demand. During these cycles demand of the item increases with time at a decreasing rate depending upon the duration of customers' credit-period. Models are formulated for both the crisp and fuzzy inventory parameters to maximize the present value of total possible profit from the whole Planning Horizon under inflation and time value of money. Fuzzy models are transferred to deterministic ones following possibility/necessity measure on fuzzy goal and necessity measure on imprecise constraints. Finally optimal decision is made using above mentioned GA. Performance of the proposed GA on the model with respect to some other GAs are compared.
-
a production inventory model with stock dependent demand incorporating learning and inflationary effect in a random Planning Horizon a fuzzy genetic algorithm with varying population size approach
Computers & Industrial Engineering, 2009Co-Authors: Arindam Roy, Sova Pal, Manas Kumar MaitiAbstract:A production inventory model for a newly launched product is developed incorporating inflation and time value of money. It is assumed that demand of the item is displayed stock dependent and lifetime of the product is random in nature and follows exponential distribution with a known mean. Here learning effect on production and setup cost is incorporated. Model is formulated to maximize the expected profit from the whole Planning Horizon. Following [Last, M. & Eyal, S. (2005). A fuzzy-based lifetime extension of genetic algorithms. Fuzzy Sets and Systems, 149, 131-147], a genetic algorithm (GA) with varying population size is used to solve the model where crossover probability is a function of parent's age-type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and possibility theory. In this GA a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set. This GA is named fuzzy genetic algorithm (FGA) and is used to make decision for above production inventory model in different cases. The model is illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Performance of this GA with respect to some other GAs are compared.
-
an epq model with price discounted promotional demand in an imprecise Planning Horizon via genetic algorithm
Computers & Industrial Engineering, 2009Co-Authors: Sova Pal, Manas Kumar Maiti, M MaitiAbstract:An economic production quantity (EPQ) model for a newly launched product is developed in an imprecise Planning Horizon, i.e., lifetime of the product is fuzzy in nature. At the beginning of each cycle price discount is offered to boost the demand. Demand depends on time and price during the price discount period. After withdrawal of price discount, demand depends on price only. Here, learning effect on production and set-up cost is incorporated. Models are formulated for both the crisp and fuzzy inventory parameters. Fuzzy models are transferred to deterministic ones following possibility/necessity measure on fuzzy goal and necessity measure on imprecise constraints. Finally optimal decision is made using Genetic Algorithm (GA).
-
an inventory model for a deteriorating item with displayed stock dependent demand under fuzzy inflation and time discounting over a random Planning Horizon
Applied Mathematical Modelling, 2009Co-Authors: Manas Kumar Maiti, M MaitiAbstract:An inventory model for a deteriorating item (seasonal product) with linearly displayed stock dependent demand is developed in imprecise environment (involving both fuzzy and random parameters) under inflation and time value of money. It is assumed that time Horizon, i.e., period of business is random and follows exponential distribution with a known mean. The resultant effect of inflation and time value of money is assumed as fuzzy in nature. The particular case, when resultant effect of inflation and time value is crisp in nature, is also analyzed. A genetic algorithm (GA) is developed with roulette wheel selection, arithmetic crossover, random mutation. For crisp inflation effect, the total expected profit for the Planning Horizon is maximized using the above GA to derive optimal inventory decision. On the other hand when inflationary effect is fuzzy then the above expected profit is fuzzy in nature too. Since optimization of fuzzy objective is not well defined, the optimistic/pessimistic return of the expected profit is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to determine this optimistic/pessimistic return. Finally a fuzzy simulation based GA is developed and is used to maximize the above optimistic/pessimistic return to get optimal decision. The models are illustrated with some numerical examples and some sensitivity analyses have been presented.
-
two storage inventory model with fuzzy deterioration over a random Planning Horizon
Mathematical and Computer Modelling, 2007Co-Authors: Arindam Roy, Manas Kumar Maiti, Samarjit Kar, M MaitiAbstract:An inventory model for a deteriorating item with stock dependent demand is developed under two storage facilities over a random Planning Horizon, which is assumed to follow exponential distribution with known parameter. For crisp deterioration rate, the expected profit is derived and maximized via genetic algorithm (GA). On the other hand, when deterioration rate is imprecise then optimistic/pessimistic equivalent of fuzzy objective function is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to maximize the optimistic/pessimistic return and finally fuzzy simulation-based GA is developed to solve the model. The models are illustrated with some numerical data. Sensitivity analyses on expected profit function with respect to distribution parameter @l and confidence levels @a"1 and @a"2 are also presented.
M Maiti - One of the best experts on this subject based on the ideXlab platform.
-
an epq model with price discounted promotional demand in an imprecise Planning Horizon via genetic algorithm
Computers & Industrial Engineering, 2009Co-Authors: Sova Pal, Manas Kumar Maiti, M MaitiAbstract:An economic production quantity (EPQ) model for a newly launched product is developed in an imprecise Planning Horizon, i.e., lifetime of the product is fuzzy in nature. At the beginning of each cycle price discount is offered to boost the demand. Demand depends on time and price during the price discount period. After withdrawal of price discount, demand depends on price only. Here, learning effect on production and set-up cost is incorporated. Models are formulated for both the crisp and fuzzy inventory parameters. Fuzzy models are transferred to deterministic ones following possibility/necessity measure on fuzzy goal and necessity measure on imprecise constraints. Finally optimal decision is made using Genetic Algorithm (GA).
-
an inventory model for a deteriorating item with displayed stock dependent demand under fuzzy inflation and time discounting over a random Planning Horizon
Applied Mathematical Modelling, 2009Co-Authors: Manas Kumar Maiti, M MaitiAbstract:An inventory model for a deteriorating item (seasonal product) with linearly displayed stock dependent demand is developed in imprecise environment (involving both fuzzy and random parameters) under inflation and time value of money. It is assumed that time Horizon, i.e., period of business is random and follows exponential distribution with a known mean. The resultant effect of inflation and time value of money is assumed as fuzzy in nature. The particular case, when resultant effect of inflation and time value is crisp in nature, is also analyzed. A genetic algorithm (GA) is developed with roulette wheel selection, arithmetic crossover, random mutation. For crisp inflation effect, the total expected profit for the Planning Horizon is maximized using the above GA to derive optimal inventory decision. On the other hand when inflationary effect is fuzzy then the above expected profit is fuzzy in nature too. Since optimization of fuzzy objective is not well defined, the optimistic/pessimistic return of the expected profit is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to determine this optimistic/pessimistic return. Finally a fuzzy simulation based GA is developed and is used to maximize the above optimistic/pessimistic return to get optimal decision. The models are illustrated with some numerical examples and some sensitivity analyses have been presented.
-
two storage inventory model with fuzzy deterioration over a random Planning Horizon
Mathematical and Computer Modelling, 2007Co-Authors: Arindam Roy, Manas Kumar Maiti, Samarjit Kar, M MaitiAbstract:An inventory model for a deteriorating item with stock dependent demand is developed under two storage facilities over a random Planning Horizon, which is assumed to follow exponential distribution with known parameter. For crisp deterioration rate, the expected profit is derived and maximized via genetic algorithm (GA). On the other hand, when deterioration rate is imprecise then optimistic/pessimistic equivalent of fuzzy objective function is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to maximize the optimistic/pessimistic return and finally fuzzy simulation-based GA is developed to solve the model. The models are illustrated with some numerical data. Sensitivity analyses on expected profit function with respect to distribution parameter @l and confidence levels @a"1 and @a"2 are also presented.
-
Two storage inventory model with random Planning Horizon
Applied Mathematics and Computation, 2006Co-Authors: A. K. Maiti, Manas Kumar Maiti, M MaitiAbstract:Abstract An inventory model with stock-dependent demand and two storage facilities under inflation and time value of money is developed where the Planning Horizon is stochastic in nature and follows exponential distribution with a known mean. The model is a order-quantity reorder-point problem where shortages are not allowed. Two rented storehouses are used for storage – one (say RW 1 ) at the heart of the market place and the other (say RW 2 ) little away from the market place. At the beginning, the item is stored at both RW 1 and RW 2 . The item is sold from RW 1 and as the demand is stock-dependent, the units are continuously released from RW 2 to RW 1 . Replacement of the item occurs when its inventory level reaches its reorder point ( Q r ). The model is formulated to maximize the total expected proceeds out of the system from the Planning Horizon. A genetic algorithm (GA) is developed based on entropy theory where region of search space is gradually decreases to a small neighborhood of the optima. This is named as region reducing genetic algorithm (RRGA) and is used to solve the model. The model is illustrated with some numerical examples and some sensitivity analyses have been done.
Johanna Törnquist - One of the best experts on this subject based on the ideXlab platform.
-
Railway traffic disturbance management — An experimental analysis of disturbance complexity, management objectives and limitations in Planning Horizon
Transportation Research Part A-policy and Practice, 2007Co-Authors: Johanna TörnquistAbstract:With the increasing traffic volumes in European railway networks and reports on capacity deficiencies that cause reliability problems, the need for efficient disturbance management becomes evident. This paper presents a heuristic approach for railway traffic re-scheduling during disturbances and a performance evaluation for various disturbance settings using data for a large part of the Swedish railway network that currently experiences capacity deficiencies. The significance of applying certain re-scheduling objectives and their correlation with performance measures are also investigated. The analysis shows e.g. that a minimisation of accumulated delays has a tendency to delay more trains than a minimisation of total final delay or total delay costs. An experimental study of how the choice of Planning Horizon in the re-scheduling process affects the network on longer-term is finally presented. The results indicate that solutions which are good on longer-term can be achieved despite the use of a limited Planning Horizon. A 60Â min long Planning Horizon was sufficient for the scenarios in the experiments.
-
railway traffic disturbance management an experimental analysis of disturbance complexity management objectives and limitations in Planning Horizon
Transportation Research Part A-policy and Practice, 2007Co-Authors: Johanna TörnquistAbstract:With the increasing traffic volumes in European railway networks and reports on capacity deficiencies that cause reliability problems, the need for efficient disturbance management becomes evident. This paper presents a heuristic approach for railway traffic re-scheduling during disturbances and a performance evaluation for various disturbance settings using data for a large part of the Swedish railway network that currently experiences capacity deficiencies. The significance of applying certain re-scheduling objectives and their correlation with performance measures are also investigated. The analysis shows e.g. that a minimisation of accumulated delays has a tendency to delay more trains than a minimisation of total final delay or total delay costs. An experimental study of how the choice of Planning Horizon in the re-scheduling process affects the network on longer-term is finally presented. The results indicate that solutions which are good on longer-term can be achieved despite the use of a limited Planning Horizon. A 60Â min long Planning Horizon was sufficient for the scenarios in the experiments.
Bhaba R. Sarker - One of the best experts on this subject based on the ideXlab platform.
-
Manufacturing lot-sizing, procurement and delivery schedules over a finite Planning Horizon
International Journal of Production Research, 2009Co-Authors: Deniz Mungan, Bhaba R. SarkerAbstract:In this study, an integrated manufacturing system for technology-related companies whose products are experiencing continuous price decrease during the life cycle is studied for optimal procurement, production and delivery schedules over a finite Planning Horizon. The model considers the inventory cost both at manufacturing and at delivery from supplier. Since the price is continuously decreasing, a manufacturing firm delivers the finished goods in small quantities frequently. Frequent deliveries in small lots are effective to reduce the total cost of the supply chain. The key for high-tech industries is to reduce the inventory holding time since the component prices are continuously decreasing, and this can only be achieved by implementing an efficient supply chain. Therefore, the main purpose of this paper is to develop an integrated inventory model for high-tech industries in JIT environment under continuous price decrease over finite Planning Horizon while effectively and successfully accomplishing supply chain integration so that the total cost of the system is minimal. An efficient algorithm is developed to determine the optimal or near-optimal lot sizes for raw material procurement, and manufacturing batch under a finite Planning Horizon. Finally, the solution technique developed for the model is illustrated with numerical examples.
-
optimal production plans and shipment schedules in a supply chain system with multiple suppliers and multiple buyers
European Journal of Operational Research, 2009Co-Authors: Bhaba R. Sarker, Ahmad DiponegoroAbstract:This research addresses an optimal policy for production and procurement in a supply-chain system with multiple non-competing suppliers, a manufacturer and multiple non-identical buyers. The manufacturer procures raw materials from suppliers, converts them to finished products and ships the products to each buyer at a fixed-interval of time over a finite Planning Horizon. The demand of finished product is given by buyers and the shipment size to each buyer is fixed. The problem is to determine the production start time, the initial and ending inventory, the cycle beginning and ending time, the number of orders of raw materials in each cycle, and the number of cycles for a finite Planning Horizon so as to minimize the system cost. A surrogate network representation of the problem developed to obtain an efficient, optimal solution to determine the production cycle and cycle costs with predetermined shipment schedules in the Planning Horizon. This research prescribes optimal policies for a multi-stage production and procurements for all shipments scheduled over the Planning Horizon. Numerical examples are also provided to illustrate the system.
Jeanpierre Villeneuve - One of the best experts on this subject based on the ideXlab platform.
-
using evolutionary optimization techniques for scheduling water pipe renewal considering a short Planning Horizon
Computer-aided Civil and Infrastructure Engineering, 2008Co-Authors: Leila Dridi, Marc Parizeau, Alain Mailhot, Jeanpierre VilleneuveAbstract:: The maintenance and management of underground infrastructures is a growing problem for a majority of municipalities. The maintenance costs are increasing while the financial resources of municipalities remain limited. Water distribution system (WDS) managers therefore need tools to assist them in the elaboration of pipe renewal management plans. In this article, results of a newly developed strategy for pipe renewal based on a cost function are presented. The strategy allows the minimization of a cost function while also considering hydraulic criterion. This strategy was tested on a short Planning Horizon of five years. The pipe number to be replaced and the optimal moment for renewal are identified using three different optimization techniques: IGA (Island Genetic Algorithm), NPGA-2 (Niched Pareto Genetic Algorithm 2), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II). The proposed approach has five distinctive features: (1) it is coupled with a flexible evolutionary framework that allows the user to select any type of operator for IGA or any kind of multiobjective genetic algorithm; (2) it uses the hydraulic simulator Epanet2.0 which allows steady state or dynamic simulations; (3) it considers a probabilistic break model to evaluate the structural deterioration of pipes; (4) it integrates a Bayesian approach for the estimation of the pipe break model parameters that take into account the influence of inherent uncertainties related to the quality of data during the decision-making process; and (5) it simulates the variation of the pipe's roughness over the years. The developed strategy/model is explained using an example that allows us to elucidate its most important components. Simulation experiments on a small network (100 pipes) are presented. A comparison of three evolutionary algorithm results is provided. Tests showed that IGA performs well, but for networks of larger sizes, we recommend increasing the number of demes to reach better solutions. Higher quality results were achieved with NSGA-II than NPGA-2 on differently sized networks. We recommend the use the NSGA-II to optimize large WDS. Future developments for this strategy are also discussed.
