This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and s...This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.展开更多
Two-agent single-machine scheduling problem is considered in this paper.Agent A’s goal is to minimize the sum of the total weighted delivery time and the total delivery cost,and agent B has the delivery time window.F...Two-agent single-machine scheduling problem is considered in this paper.Agent A’s goal is to minimize the sum of the total weighted delivery time and the total delivery cost,and agent B has the delivery time window.First,the NP-hardness of the general problem is proved,and then two special cases are considered.One case is that A’s jobs have agreeable ratio and this problem is still NP-hard.A pseudo-polynomial dynamic programming algorithm and a 32-approximation algorithm are designed.In the other case,A’s jobs have agreeable ratio and B’s jobs have deadline at the same time.This problem is polynomial solvable.展开更多
This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable ...This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.展开更多
文摘This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.
基金the National Natural Science Foundation of China(No.11371137)。
文摘Two-agent single-machine scheduling problem is considered in this paper.Agent A’s goal is to minimize the sum of the total weighted delivery time and the total delivery cost,and agent B has the delivery time window.First,the NP-hardness of the general problem is proved,and then two special cases are considered.One case is that A’s jobs have agreeable ratio and this problem is still NP-hard.A pseudo-polynomial dynamic programming algorithm and a 32-approximation algorithm are designed.In the other case,A’s jobs have agreeable ratio and B’s jobs have deadline at the same time.This problem is polynomial solvable.
文摘This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.
