A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage

In this study, a vendor-managed inventory model is developed for a single-vendor multiple-retailer single-warehouse (SV-MR-SV) supply chain problem based on the economic order quantity in which demands are stochastic and follow a uniform probability distribution. In order to reduce holding costs and to help balanced on-hand inventory cost between the vendor and the retailers, it is assumed that all inventory is held at a central warehouse with the lowest cost among the parties. The capacity of the central warehouse is limited. The objective is to find the warehouse replenishment frequency, the vendor's replenishment frequency, the order points, and the order quantities of the retailers such that the total inventory cost of the integrated supply chain is minimized. The proposed model is a mixed integer nonlinear programming problem (MINLP); hence, a genetic algorithm (GA) is utilized to solve this NP-hard problem. The parameters of the GA are calibrated using the Taguchi method to find better solutions. Some numerical illustrations are solved at the end to demonstrate the applicability of the proposed methodology and to evaluate the performance of the solution method.

As replenishment frequencies play an important role in integrated inventory models to reduce the total cost of supply chains, this paper extends a vendor managed inventory (VMI) model for a two-echelon supply chain management, which several retailers are replenished with a different rate for several items by one vendor. While a vendor supplies several products to retailers, the vendor’s warehouse has a capacity constraint. The aim of this paper is to find replenishment frequencies and order quantities to minimize the total inventory cost. The proposed model is a mixed-integer nonlinear programming (MINLP), which means that it is unable to be solved by exact methods in large-scale problems. Thus, a genetic algorithm (GA), as a meta-heuristic optimization, is employed to solve it. The proposed algorithm is verified with an exact solver namely Couenne using GAMS software. Moreover, to improve proposed GA, it is hybridized by a local searcher namely the imperialist competitive algorithm (ICA) and a boundary operator. The Taguchi method in design of experiments tunes parameters of algorithms to improve the performance of the hybrid GA (HGA).

In this paper, for the first time the cross-docking concept is considered in a vendor-managed inventory (VMI)-based supply chain. In this supply chain, and based on the economic order quantity (EOQ) model, a vendor prepares and sends several products to retailers. Then, retailers, based upon customers’ order, send products to the cross-dock to be delivered to the customers. Demands of retailers and customers are deterministic and shortage is acceptable as backorder. Presented mathematical model (of integer nonlinear programming type) seeks to minimize the total cost of the supply chain. Thus, a meta-heuristic algorithm (GA) is used to solve the model. In addition, a number of sample problems are analyzed to evaluate the performance of the solving algorithm. Finally, after the conclusion, some suggestions are provided for future researches.

This paper uses a hybrid genetic learning algorithm to train Pi-sigma neural network and this algorithm was once applied to resolve a function optimizing problem. The hybrid genetic learning algorithm incorporates the stronger global search of genetic algorithm into the stronger local search of flexible polyhedron method, and can search out the global optimum faster than standard genetic algorithm. The experiments show that the hybrid genetic algorithm can achieve better performance. At last, the hybrid genetic algorithm is proved converge to the global optimum with the probability of 1.

Supply in a company asset that is idly invested. How to manage the inventory is the frequent issue arises so that every request can be served, with a minimum cost. PT.XYZ, is a provider of clean water. This research aims to control the supply of chemicals in the production process so that the total cost (TC) can be minimized. The research was conducted by using a multi-item Economic Order Quantity (EOQ). Furthermore, the system will be completed by Genetics Algorithm (GA) to acquire minimum TC of supply and an EOQ for each chemicals. The research outcome indicates that by applying GA the supply TC of Rp.3,995,584,171,8458 is acquired. This is lower comparing to the budget provided by the company i.e. Rp.6,443,800,000,0000. By optimizing, the supply TC can be saved up to 37.9%. The total order of each Chemical, is as follows: 24,875,9615 Kg, 18.8838 Kg, 72.7511 Kg and 452.9790 Kg.

Global Optimization is an active area of research for the variety of optimization problems that are frequently arising in network design and operation, finance, supply chain management, scheduling, and many other areas. In the last few years, different types of evolutionary algorithms (EAs) have been proposed for solving and analyzing the properties of diverse types of optimization problems. EAs work with a set of random solutions called population and find a set of optimal solutions for the problems at hand in a single simulation run opponent to traditional optimization methods. Among the stochastic based algorithms, genetic algorithm (GA) is one of the most popular and frequently used stochastic based meta-heuristic inspired by natural evolution. The premature convergence, genetic drift and trapping in the local basin attraction are their major drawbacks. These issues can be overcome by hybridizing GA with some efficient local search optimizers and different search operators. In this paper, we have proposed hybrid GA by employing the Firefly Algorithm (FA) as search operator aiming at to improve the searching ability of the baseline GA. The performance of the suggested hybrid genetic firefly algorithm (HGFA) is hereby evaluated by using 24 benchmark functions which was designed for the special session of the 2005 IEEE Congress on Evolutionary Computation (CEC'05). The numerical results provided by HGFA are summarized in the numerical form such as best, mean and standard deviation by executing 25 times independently with different random seeds to solve each test problem. The suggested HGFA have tackled most of the used test problems with good convergence speed as compared to the stand alone Genetic Algorithm.

Solving a nonlinear non-convex trim loss problem with a genetic hybrid algorithm

Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. In this paper, a comparison between memetic algorithm and genetic algorithm were made in order to investigate the performance for the cryptanalysis on simplified data encryption standard problems(SDES). The methods were tested and various experimental results show that memetic algorithm performs better than the genetic algorithms for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient memetic algorithm for the cryptanalysis of SDES. This paper proposes the cryptanalysis of simplified encryption standard algorithm using memetic and genetic algorithm. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. Solving such problems requires effort (e.g., time and/or memory requirement) which increases with the size of the problem. Techniques for solving combinatorial problems fall into two broad groups - traditional optimization techniques (exact algorithms) and non traditional optimization techniques (approximate algorithms). A traditional optimization technique guarantees that the optimal solution to the problem will be found. The traditional optimization techniques like branch and bound, simplex method, brute force search algorithm etc methodology is very inefficient for solving combinatorial problem because of their prohibitive complexity (time and memory requirement). Non traditional optimization techniques are employed in an attempt to find an adequate solution to the problem. A non traditional optimization technique - memetic algorithm, genetic algorithm, simulated annealing and tabu search were developed to provide a robust and efficient methodology for cryptanalysis. The aim of these techniques to find sufficient solution efficiently with the characteristics of the problem, instead of the global optimum solution, and thus it also provides attractive alternative for the large scale applications. These nontraditional optimization techniques demonstrate good potential when applied in the field of cryptanalysis and few relevant studies have been recently reported. In 1993 Spillman (16) for the first time presented a genetic algorithm approach for the cryptanalysis of substitution cipher using genetic algorithm. He has explored the possibility of random type search to discover the key (or key space) for a simple substitution cipher. In the same year Mathew (12) used an order based genetic algorithm for cryptanalysis of a transposition cipher. In 1993, Spillman (17), also successfully applied a genetic algorithm approach for the cryptanalysts of a knapsack cipher. It is based on the application of a directed random search algorithm called a genetic algorithm. It is shown that such a algorithm could be used to easily compromise even high density knapsack ciphers. In 1997 Kolodziejczyk (11) presented the application of genetic algorithm in cryptanalysis of knapsack cipher .In 1999 Yaseen (18) presented a genetic algorithm for the cryptanalysis of Chor-Rivest knapsack public key cryptosystem.

Prediction of the amount of production can be done by forecasting demand and using appropriate methods. The supply chain studied at ABC company consists of manufacturing (vendors) and sales offices. Initially forecasting is done at each level of the supply chain with different forecasting methods. Therefore, uniform forecasting methods are needed for each supply chain actor. Based on testing the forecasting method conducted, the Winter’s Method. The model is used to link forecasting and implementation using the Vendor Managed Inventory (VMI) approach. Inventory planning in the supply chain cannot be done individually and must be thought of as a coordinated system. Inventory control is done by using the optimal lot calculation, namely Economic Order Quantity (EOQ). Based on demand forecasting and optimal lot determination, it can be calculated the value of the Bullwhip effect that occurs after the use of VMI in the supply chain has changed from 1.359 to 0.514 at the Manufacturing level.

Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Within a vendor-managed inventory (VMI) agreement, the upstream supply chain member (the vendor) takes responsibility for managing the inventory of the downstream member (the customer) within specific levels previously agreed upon without the need of orders from the customer side to be placed. Therefore, the vendor can focus on optimising production efficiency and capacity planning, while the customer has to improve forecast accuracy. This paper analyses the benefits a VMI agreement could bring for a one-supplier, multiple-customer case through analysing two cases: a supply chain managed in a traditional manner and VMI when both the vendor and the customers belong to the same organisation. The analysis is based on the economic ordering quantity (EOQ) formula and its related total cost, and the novelty is captured by evaluating one vendor, multiple buyers, and multiple product situations. The modelling is done so as to capture the needs and factors which occur within the pharmaceutical industry and a numerical application will be executed with data from one of the main leaders within the pharmaceutical field.

To manage supply chain efficiently and effectively, the members of the supply chain should collaborate and cooperate. It is known that information sharing and visibility are important factors that contribute to supply chain coordination. The visibility in real customer demand can help reduce the bullwhip effect, improve customer service, and reduce costs. Vendor managed inventory (VMI) is an approach that allows suppliers/vendors to access to their customer's inventory and demand information. The benefits of VMI have been reported in many studies. However, the gain that each member of the supply chain realized could be different. Some studies show that upstream members benefit more than the downstream ones, while some studies show the opposite results. Therefore, some members of the supply chain are still reluctant to adopt the VMI practice since they are not sure whether the benefits gain will justify the costs incurred. In this paper, simulation model is used to explore the effect of VMI implementation on supply chain costs both on system-wide level and on member level. It was found that VMI helps reduce total supply chain costs. However, the level of cost reduction differs significantly among members depending on the types of the implementation. As a result, it is important to establish the level of investment required and benefit shared for each member before implementing VMI. The members that may experience less cost reduction should be offered higher benefit share (or required less investment). Otherwise, the whole supply chain may lose the opportunity to gain additional benefits from implementing VMI. In addition, the benefits gained from VMI also vary depending on supply chain environment. Simulation model can be used as a guiding tool for establishing appropriate investment and benefits sharing structure for VMI implementation in different supply chain conditions.

Purpose: The purpose of this study is to compare the performance of two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR), under a price-sensitive uncertain demand environment, and to make the optimal decisions on retail price and order quantity for both mechanisms. Design/ methodology/ approach: Analytical models are first applied to formulate a profit maximization problem; furthermore, by applying simulation optimization solution procedures, the optimal decisions and performance comparisons are accomplished. Findings: The results of the case study supported the widely held view that more advanced coordination mechanisms yield greater supply chain profit than less advanced ones. Information sharing does not only increase the supply chain profit, but also is required for the coordination mechanisms to achieve improved performance. Research limitations/implications: This study considers a single vendor and a single retailer in order to simplify the supply chain structure for modeling. Practical implications: Knowledge obtained from this study about the conditions appropriate for each specific coordination mechanism and the exact functions of coordination programs is critical to managerial decisions for industry practitioners who may apply the coordination mechanisms considered. Originality/value: This study includes the production cost in Economic Order Quantity (EOQ) equations and combines it with price-sensitive demand under stochastic settings while comparing VMI and CPFR supply chain mechanisms and maximizing the total profit. Although many studies have worked on information sharing within the supply chain, determining the performance measures when the demand is price-sensitive and stochastic was not reported by researchers in the past literature.

How to allocate resources and select the type of investment is very important . The optimal allocation, especially in the financial markets of countries that are paced growth factor, is very significance. In this research toward optimizing resource allocation, an innovative learning algorithm will used to select and optimize portfolio in Tehran Stock Exchange. a new method is proposed based on the combination of ICA (Imperial Competitive Algorithm) and GA (Genetic Algorithm) which improves the convergence speed and accuracy of the optimization results. The new algorithm, which is named R-ICGA (Recursive- ICA-GA), runs ICA and GA consecutively. It is shown that a fast decrease occurs while the proposed algorithm switches from ICA to GA. The main goal of the new proposed algorithm is to achieve a faster optimization technique by applying this fast decrease. Moreover, the simple combination of ICA and GA, which is named ICA-GA, is presented in this study. These two combination schemes of ICA and GA are used for comparing with other conventional algorithms. Finally, three fitness functions are used for comparing the suggested algorithms. The obtained results show that compared with the previous method, the proposed algorithms are at least 32% faster in optimization processes; also the variance convergence speed is smaller than the ICA and GA.