Optimization of Order Allocation to In-Stock Slabs by Genetic Algorithm

Abstract

In the order allocation to in-stock slabs problem, it is necessary to optimize both the selection thick and flat pieces of steel plate, that is called in-stock slabs, for each orders and the order arrangement in hot rolling plates. The optimum solution in this problem is to produce as many high priority orders as possible while using the slabs as efficiently as possible, i.e. reducing waste material as much as possible.We have approached this in-stock allocation problem as a combinatorial optimization problem and have successfully applied a combinatorial optimization method known as the genetic algorithms (GA) to achieve an optimal allocation and arrangement.We present the GA based system in this paper. As the in-stock allocation problem became more complicated (number of constraints included, number of orders, number of slabs, etc.) the calculation time began to exceed the allocated production planning time. In this system we divided the problem into sub problems while maintaining solution quality. By allocating the given calculation time among the small problems we were able to obtain a good solution in a feasible calculation time.In general, it is difficult to solve the problem which includes a vast of array of solution constraints and restrictions using GA. The order allocation problem has many these constraints such as order/slab material composition restriction, machine dictated order arrangement constraints and so on. In the proposed system, by introducing a new coding and decoding method which considers these constraints, it is possible to enable the application of GA.Furthermore, in this problem it is necessary to satisfy the two objectives of order priority and yield rate. By the construction of the evaluation function and the tuning of the weighting coefficients we were able to develop a system which satisfies two objectives.

Order Allocation Research of Logistics Service Supply Chain with Mass Customization Logistics Service

“…To date, most OAPs are solved by using multiobjective linear programming or nonlinear programming methods, which spread around the produce supply chain. References [19,20] studied the application of genetic algorithm for optimizing order allocation. Current studies on customer order allocation mainly consider the service level and minimizing procurement costs [13,19,21].…”