Multi-agent scheduling in a no-wait flow shop system to maximize the weighted number of just-in-time jobs

Dynamic flexible job shop scheduling method based on improved gene expression programming

“…The new recombination rate and transposition rate in GEP are shown in equations (13) and (14), respectively. In equation (13), p r is the recombination rate, p rmin is the minimum recombination rate, p rmax is the maximum recombination rate, f i is the fitness value of individual i, x is a weighting factor between 0 and 1, n is the number of successive iterations that the population is not improved, and n m is the maximum number of successive iterations that the population is not improved.…”

“…Therefore, it is more realistic to consider dynamic events in the scheduling, and dynamic flexible job shop scheduling problem (DFJSP) emerges as a promising problem, which has received increasing research attention in the past years. 1,2 Since the work of Jackson 3 that introduced the dynamic scheduling problem in the 1950s for the first time, the dynamic scheduling problem has gained enormous popularity in the scheduling research community, and fruitful results have been reported in the literature, which can be classified into three main categories, namely, rescheduling, [4][5][6][7][8][9][10] robust scheduling, [11][12][13][14] and online scheduling. [15][16][17][18] Rescheduling is also known as pre-reaction scheduling, which implies to schedule again when new events occur.…”

“…Aside from limited research on multi-agent technology, 14 online scheduling research mainly focuses on designing scheduling rules, also named as heuristic rules or dispatching rules. They are used to select jobs for machines or assign machines to jobs.…”

“…Since the work of Jackson 3 that introduced the dynamic scheduling problem in the 1950s for the first time, the dynamic scheduling problem has gained enormous popularity in the scheduling research community, and fruitful results have been reported in the literature, which can be classified into three main categories, namely, rescheduling, 4–10 robust scheduling, 11–14 and online scheduling. 15–18…”

Two-Agent Pareto-Scheduling of Minimizing Total Weighted Completion Time and Total Weighted Late Work

“…Yin et al [16] also considered similar problems in the setting of a two-machine flow shop, and provided two pseudo-polynomial-time exact algorithms to find the Pareto frontier. Chen et al [17] studied a multi-agent Pareto-scheduling problem in a no-wait flow shop setting, in which each agent's criterion is to maximize its own weighted number of JIT jobs. They showed that it is unary NP-hard when the number of agents is arbitrary, and presented pseudo-polynomial time algorithms and an (1, 1 − , .…”