C Rajendran

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration. © 2011 Elsevier Ltd.

Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.

We consider a static divergent two-stage supply chain with one distributor and many retailers. The unsatisfied demands at the retailers' end are treated as lost sales, whereas the unsatisfied demand is assumed to be backlogged at the distributor. The distributor uses an inventory rationing mechanism to distribute the available on-hand inventory among the retailers, when the sum of demands from the retailers is greater than the on-hand inventory at the distributor. The present study aims at determining the best installation inventory control-policy or order-policy parameters such as the base-stock levels and review periods, and inventory rationing quantities, with the objective of minimizing the total supply chain costs (TSCC) consisting of holding costs, shortage costs and review costs in the supply chain over a finite planning horizon. An exact solution procedure involving a mathematical programming model is developed to determine the optimum TSCC, base-stock levels, review periods and inventory rationing quantities (in the class of periodic review, order-up-to S policy) for the supply chain model under study. On account of the computational complexity involved in optimally solving problems over a large finite time horizon, a genetic algorithm (GA) based heuristic methodology is presented. © 2010 Elsevier Ltd.

This paper addresses the development of an inventory control mechanism and inventory rationing policies in a static divergent two-stage supply chain consisting of one single distributor and several retailers. The unsatisfied demand is assumed to be backlogged at both distributor's and retailers' ends. In the case of shortage at distributor, the available stock on hand is rationed among the retailers. Most of the studies in the literature treat ordering costs as negligible and assume the review period to be one unit of time. However, if there is a significant cost associated with the order placement, then the review period can be greater than one time unit. Hence, in this study, we consider ordering costs for retailers, and present a mathematical programming model which can give optimal base-stock levels and review periods and inventory rationing (in the class of periodic review, order-up-to S policy). A genetic algorithm-based heuristic algorithm is also presented for solving problems with a large time horizon. © 2010 Inderscience Enterprises Ltd.

The problem of family and job scheduling in a flowline-based manufacturing cell is considered with the criterion of minimizing makespan, followed by the criterion of minimizing total flowtime, and finally with the bi-criteria of minimizing makespan and total flowtime. We reckon the presence of missing operations in the manufacturing cell and present the correct problem formulation for time-tabling. We then propose the Genetic Algorithm (GA) for part-family scheduling and then for job scheduling within part-families. The proposed algorithm is compared with the existing algorithm and results are reported. © 1994.

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA. © 2017 Wiley. All rights reserved.

A novel genetic algorithm (GA) is proposed in this paper for solving the machine-part cell formation problem in the presence of alternative process plans. Parent chromosomes with number of genes equal to the number of parts are generated based on the correlation value calculated using a ranking index. Crossover is carried out on the 60% of the parent chromosomes and mutation is carried out at the weakly correlated part (gene) in the chromosomes. Performance of the algorithm was tested using 20 test instances from the literature. The proposed genetic algorithm is superior in terms of solution quality for 10% of the total test instances and equal to the best solution achieved by the other algorithms for the remaining 90% of the test instances.

In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective problem by using a proposed genetic algorithm, called the "non-dominated and normalized distanceranked sorting multi-objective genetic algorithm" (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm (ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial number of non-dominated solutions are contributed by the NDSMGA. © 2006 Springer-Verlag London Limited.

In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective problem by using a proposed genetic algorithm, called the "non-dominated and normalized distance-ranked sorting multi-objective genetic algorithm" (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm (ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial number of non-dominated solutions are contributed by the NDSMGA.

The problem of scheduling in flowshop and flowline-based cellular manufacturing systems (CMS) is considered with the objectives of minimizing makespan, total flowtime and machine idletime. We first discuss the formulation of time-tabling in a flowline-based CMS. A genetic algorithm is then presented for scheduling in a flowshop. The proposed genetic algorithm is compared with the existing multi-criterion heuristic, and results of the computational evaluation are presented. We introduce some modifications in the heuristic seed sequences, while using them to ¡nitialÍ7.e subpopulations in the algorithm for scheduling in a flowline-based CMS. The proposed algorithm is also found to perform well for scheduling in a flowline-based CMS. © 1996 Taylor & Francis Ltd.