Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

Abstract

This paper studies the flexible flow shop scheduling problem with component altering times (FFSP-CAT), which is a specific form of the flexible flow shop scheduling problem with sequence dependent setup times in the practical scenario. Dealing with FFSP-CAT includes the jobs' machine assignment determination and the globe optimisation. We develop six rules for jobs' machine assignment, and since these rules will easily conflict with one another if they are used with the same priorities, we construct a repeated cooperative model and provide a game theoretical analysis, then derive a Nash equilibrium machine assignment scheme (NEMAS) to effectively manage these rules for jobs' machines assignment at each stage. Furthermore, to achieve the global optimum, we design an advanced compact genetic algorithm (ACGA). By combining NEMAS and ACGA, an approach named advanced compact genetic algorithm with Nash equilibrium machine assignment scheme (ACGA-NEMAS) is used for minimising the makespan of FFSP-CAT. Through extensive comparison experiments with different scales of instances, we show that the algorithm with NEMAS acquires 56.85% improvement over the algorithm without NEMAS, and ACGA-NEMAS performs 80.28% better than genetic algorithm.

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

“…The idle time for the machine in Equation (19) represents the time between the start time of the first job and completion time of the last job on each machine. TWT is the sum of idle time for all machines.…”

“…The example j15c5d3 was used to illustrate the orthogonal experiment. Three parameters, each with four levels (see Table 1), were taken for orthogonal experiments with the scale of L 16 (4 3 ) [19]. The algorithm ran 20 times in each group of experiments.…”

“…From the figure we can see that β and NP had a greater impact on the performance of the algorithm, and σ T had the least impact on the performance of the algorithm. The best combination of parameters was: σ T = 10, β = 1.5, NP = 4. levels (see Table 1), were taken for orthogonal experiments with the scale of L16 (4 3 ) [19]. The algorithm ran 20 times in each group of experiments.…”

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

“…By using the cooperative game, Calleja et al [63] studied the single machine job scheduling problem, where clients could have more than one job to be processed and a job could be of interest for different players using cooperative games. Han et al [64] It can be seen from the above literature that there are many studies on scheduling problems from the viewpoint of game theory. However, only few of them use multi-agent technology.…”

Swarm intelligent algorithm for re-entrant hybrid flow shop scheduling problems

“…It leads to instability scheduling problems of reentrant workshop and obstruct, making scheduling problems of reentrant manufacturing workshop more complicated than general issue. Many tasks, various processes, multi work stations, and different processing time of jobs in the same working procedure are features of hybrid flowshop problems (HFSP) (Han et al, 2016;Karimi et al, 2011). If reentrant manufacturing lines appears in the RHFS, more complex production process, sharply increased production loads, added instability and unbalanced equipment loading and other questions greatly result in HFS difficulty.…”