We propose in this paper an effective heuristic algorithm to solve a scheduling problem that comes from industry. The workshop is an hybrid flow shop with recirculation and the problem is to perform jobs between a release date and a due date, in order to minimize the weighted number of tardy jobs. Firstly, an integer linear programming formulation of the problem is proposed, then a lower bound, a greedy algorithm and a genetic algorithm are described as approximate methods. To evaluate these heuristics, experiences on instances like industrial ones are computed, and show the efficiency of the genetic algorithm.

A genetic algorithm for an industrial multiprocessor flow-shop scheduling problem with recirculation

The implementation of lean manufacturing in the furniture industry: A review and analysis on the motives, barriers, challenges, and the applications

This research presents an alternative heuristic algorithm to solve the vendor management inventory system with multi-product and multi-constraint based on EOQ with backorders considering two classical backorders costs: linear and fixed. For this type of inventory system, the optimization problem is a nonlinear integer programming (NLIP). Several numerical examples are given to demonstrate that the proposed heuristic algorithm is better than the previous genetic algorithm published based on three aspects: the total cost, the number of evaluations of the total cost function and computational time. Furthermore, the proposed algorithm is simpler and can be implemented by any people.

A simple and better algorithm to solve the vendor managed inventory control system of multi-product multi-constraint economic order quantity model

Energy management systems are essential and indispensable for the secure and optimal operation of autonomous polygeneration microgrids which include distributed energy technologies and multiple electrical loads. In this paper, a multi-agent decentralized energy management system was designed. In particular, the devices of the microgrid under study were controlled as interactive agents. The energy management problem was formulated here through the application of game theory, in order to model the set of strategies between two players/agents, as a non-cooperative power control game or a cooperative one, according to the level of the energy produced by the renewable energy sources and the energy stored in the battery bank, for the purpose of accomplishing optimal energy management and control of the microgrid operation. The Nash equilibrium was used to compromise the possible diverging goals of the agents by maximizing their preferences. The proposed energy management system was then compared with a multi-agent decentralized energy management system where all the agents were assumed to be cooperative and employed agent coordination through Fuzzy Cognitive Maps. The results obtained from this comparison, demonstrate that the application of game theory based control, in autonomous polygeneration microgrids, can ensure operational and financial benefits over known energy management approaches incorporating distributed intelligence.

https://www.mdpi.com/1996-1073/10/11/1756/pdf

A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids

Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm

Finding a probabilistic approach to analyze lean manufacturing

Research highlights? An efficient algorithm is presented to solve flexible flow-shop problems using fuzzy approach. ? The goal is to minimize the total job tardiness. We assume parallel machines with different operation times. ? In this algorithm, parameters like ''due date'' and ''operation time'' follow a triangular fuzzy number. We used data mining technique as a facilitator to help in finding a better solution in such combined optimization problems. ? Therefore, using a combination of genetic algorithm and an attribute-deductive tool such as data mining, a near optimal solution can be achieved. In this paper, an efficient algorithm is presented to solve flexible flow-shop problems using fuzzy approach. The goal is to minimize the total job tardiness. We assume parallel machines with different operation times. In this algorithm, parameters like "due date" and "operation time" follow a triangular fuzzy number. We used data mining technique as a facilitator to help in finding a better solution in such combined optimization problems. Therefore, using a combination of genetic algorithm and an attribute-deductive tool such as data mining, a near optimal solution can be achieved. According to the structure of the presented algorithm, all of the feasible solutions for the flexible flow-shop problem are considered as a database. Via data mining and attribute-driven deduction algorithm, hidden relationships among reserved solutions in the database are extracted. Then, genetic algorithm can use them to seek an optimum solution. Since there are inherited properties in the solutions provided by genetic algorithm, future generation should have the same behavioral models more than preliminary ones. Data mining can significantly improve the performance of the genetic algorithm through analysis of near-optimal scheduling programs and exploration of hidden relationships among pre-reached solutions.

Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining

In this paper a meta-heuristic approach for lot-size determination problems in a complex multi-stage production scheduling problems with production capacity constraint has been developed. This type of problem has multiple products with sequential production processes which are manufactured in different periods to meet customer's demand. By determining the decision variables, machinery production capacity and customer's demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is has been provided. In the first step, the original problem is converted to several individual problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each individual problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the individual problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product's lot-size is determined through several steps. We have verified our results through several empirical experiments.

Combination of genetic algorithm with Lagrange multipliers for lot-size determination in multi-stage production scheduling problems

a r t i c l e i n f o a b s t r a c t Competition among the industrial and service organizations to provide their clients with financial and credit requirements through the banking facilities has considerably increased. On the other hand, the challenge facing these financial and credit resources is that they are limited. Therefore, the optimal allocation of these limited financial resources with the aim of maximizing the investment value is of a great priority for banks and other financial institutes. In this study, first the credit criteria for the applicants for bank facilities have been identified and then based on the improved Data Envelopment Analysis (DEA) technique, an effective method has been proposed for the client clustering. The improved DEA method which is called Golden DEA reduces the calculation time and increases the decision-making operations that ultimately lead to the improvement of the existing method. Also, the improved DEA model provides a short, dynamic and straight path in order to achieve greater efficiency for every institution. The priority provided by the improved DEA method has been compatible with the priority given by the existing DEA method for all of the understudied cases.

http://www.journal-aprie.com/article_43017_f5fb9a969ee045f89aa3f65e21180b22.pdf

Adaptive DEA for clustering of credit clients

Making decisions about the economic selection of the customers plays a significant role in the sale and logistic management of the companies. Furthermore, another issue affecting the relationship between the suppliers and the customers is the proper and timely distribution of the products as well as the optimum mixing of the distribution routes to reduce transportation costs. In this paper, the issues of the integration of routing, and the economic selection of customers has been explored aiming at minimizing the transportation, maintenance, and discount costs and maximizing the products selling profits. In addition to the amount of customer's purchasing, costumers' collection period is effective to offer them discounts. The economic customers are selected due to three factors: the discount on the price of the product, the marginal profit of the requested products, and the distance from the supplier's warehouse. Games software was used for the exact solution of the model, and Simulated annealing algorithm was used to solve the model in larger dimension. The efficiency and applicability of the proposed model was approved comparing the optimum results with a high preciseness as 98.91 percent. Moreover, in the case study of Kalleh Company, the results revealed 14% increase in pure profits.

https://www.ije.ir/article_72844_1272993bfd06bceabb1af6cd52d0a42f.pdf

Integration of Multi-period Vehicle Routing Problem and Economic Selection of Customers with the Objective of Optimizing Distribution, Sales and Discounts Planning

H. Khademi Zare

Papers

Finding a probabilistic approach to analyze lean manufacturing

Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining

Combination of genetic algorithm with Lagrange multipliers for lot-size determination in multi-stage production scheduling problems

Adaptive DEA for clustering of credit clients

Integration of Multi-period Vehicle Routing Problem and Economic Selection of Customers with the Objective of Optimizing Distribution, Sales and Discounts Planning