A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm

Kar, Mohuya B.; Bera, Shankar; Das, Debasis; Kar, Samarjit

doi:10.1007/s40092-015-0121-y

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…In this study, Matlab simulation platform was used to write algorithm model, and the experiments were carried out on a laboratory server, which was equipped with Windows7 system, I7 processor, and 16G memory. Lead time for transportation between the manufacturer and the supplier (month / times) [4,9] In this study, five sets of examples of different scales were tested. The basic parameters of the five sets of examples are shown in Table 1, and the scale parameters are shown in Table 2.…”

Section: Simulation Example 41 Experimental Environmentmentioning

confidence: 99%

“…With the continuous development of economy, the market competition centering on customer demand becomes more and more intense. Enterprises need to reduce production time, reduce costs and improve the speed of feedback to the demands of users according to their own situations [4]. Therefore, the supply inventory distribution system came into being.…”

Section: Introductionmentioning

confidence: 99%

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Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

2019

IJOMAM

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With the development of China's economy, various manufacturers have formed a supply inventory distribution network to meet more customer demands and improve production efficiency, and the dynamic scheduling of the network is particularly important. However, there are various uncertainties that can affect the stability of operation in the distribution network. In this study, in order to solve the dynamic scheduling problem in uncertain environment, the optimal solution of dynamic scheduling of supply inventory in uncertain environment was calculated based on the adaptive genetic algorithm, the validity of the genetic algorithm and adaptive genetic algorithm was compared and verified by numerical simulation experiment on Matlab, and the influencing factors of cost were analyzed. The results showed that: (1) the adaptive genetic algorithm was more efficient than genetic algorithm in solving the optimal solution of dynamic scheduling; (2) the cost of dynamic scheduling tended to decrease first and then increase with the increase of the maximum lead time of the manufacturers.

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Section: Simulation Example 41 Experimental Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

2019

IJOMAM

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“…A metaheuristic hybridization algorithm was proposed combining the original Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a local search algorithm based on a neighborhood search technique. Kar et al (2015) presented a production-inventory model for deteriorating items with stock-dependent demand under inflation in a random planning horizon. This model is formulated as profit maximization problem with respect to the retailer and solved by two metaheuristics, which are the genetic algorithm and the particle-swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

2017

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The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NPhard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

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“…The basic idea of this multi-model approach is the use of each component model's unique capability to better capture different patterns in the data. Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model (Mousavi et al 2014;Hwang and Oh 2010;Alizadeh et al 2011;Shen et al 2011;Kar et al 2015). Baba et al (2000) applied NNs and GAs to design an intelligent decision support system (DSS) for analyzing the Tokyo Stock Exchange Prices Indexes (TOPIX).…”

Section: Introductionmentioning

confidence: 99%

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

Mehmanpazir

Asadi

2016

J Ind Eng Int

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The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

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A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm

Cited by 10 publications

References 27 publications

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

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