A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach

Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty

“…Park et al (2007) designed the multi-product multi-period PDP model consisting of supplier, plant, and distribution center (DC) to minimize the total cost and presented a genetic algorithm (GA) to solve the problem. Kazemi et al (2009) developed a multi-echelon supply chain-based scenarios for production-distribution problem. They applied a multi-factor system based on GA as the solution algorithm.…”

Agent teams and evolutionary computation: Optimizing semi-parametric spatial autoregressive models

“…By applying this methodology to the problem they demonstrated that the solutions provided by ATeams offer more reachability and can solve the problem in a more efficient manner, than more conventional methods. Other applications of similarly structured asynchronous MAS can be found in the supplychain literature [4,1] where they are still popular. We are not aware of any other applications of ATeams in the field applied spatial econometrics.…”

Research and Application of Supply Chain Distribution Optimization Model based on Improved Genetic Algorithm

“…It can be found out that the initial population includes a lot of tour paths, while this result in practical distribution is not reasonable and not realistic in distribution activities. For example, in K initial 1 = [9,3,9,11,7,17,14,8,9,3,3,1,2,4,1,13,1,17,11,16], path [9 3 9] is a tour path, which is not allowed in operation. Design pretreatment operator and deal with the repeated path and tour path in the path.…”

“…The number of distribution center is 0, the number closest to distribution center is 1, and so on. Form initial individual code string through random combination, such as 32 40 22 34 35 6 3 16 11 30 33 7 38 28 17 14 8 36 29 21 25 37 31 27 26 19 15 1 36 23 2 4 18 24 39 13 9 20 10 12. Select V-1 random numbers between 2 and 39 (V is the number of vehicle) and divide one chromosome into V chromosomes randomly [8]. For example, V=5 indicates that 5 vehicles will make the delivery, producing breakpoints 7 12 20 32.…”

“…Deal with illegal chromosome through pretreatment operator. Find two endpoints 10 and 17 of overload path on overload path D bk3 = [10,13,18,8,9,1,5,17] and adjust chromosome through moving breakpoint location. After finding out endpoints 10 and 17, determine to move the 10 point to the left or move the 17 point to the right through comparing the vehicle load capacity of the path D bk3 = [14,15] on the left of 10 and path D bk3 = [11,7,6] on the right of 17.…”