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With the expansion of the agricultural machinery operation scope and the increase of the number of harvesters, there were fierce competition in the trans-regional operation market of combine in recent years. And the scheduling problem of combine was growing one of the main factors affecting the economic benefits of trans-region operation (Liao et al., 2008). Due to the lack of meticulous and systematic planning in current trans-regional operations. there were still many problems, such as unreasonable sequence of operations on plots, low work efficiency, and high operating costs;unreasonable resource allocation for harvesters, resulting in waste of resources;Coupled with roads and weather and other factors, the harvester cannot arrive on time, thus delaying the farming time.
The essence of trans-regional scheduling of combine was a resource scheduling problem between agricultural machinery and farmland with time window and space distance constraint.In the past ten years, many scholars have applied a variety of planning methods and intelligent optimization algorithm to carry out a large number of research, and achieved many results (Foulds et al.,2005,& Zhang,2006,& Dionysis Bochtis et al.,2007,& Ferrer et al.,2008,& Li et al.,2008,& Guan et al.,2009,& Wang et al.,2010).Zhang (2012) studied the of agricultural machinery scheduling based on owner selection. Wu (2013) established a time-window constrained scheduling model for agricultural machinery and solved the it with dynamic programming. Wang (2013) studied the scheduling of cotton pickers as VRP-TW problem. Giovanni (2018) solved the problems of long round-trip time and long waiting time of agricultural machinery material filling based on dynamic programming accurate algorithm and branch and bound algorithm. Hasan (2018) obtained a reasonable agricultural machinery operation path planning and scheduling method through improved Clarke Wright algorithm and tabu search algorithm. Wang (2019) established the agricultural machinery scheduling model with time window, and designed an agricultural machinery scheduling method based on genetic algorithm, and verified the model and algorithm through the test data set and the actual case of agricultural machinery scheduling. Zhao (2019) constructed the objective function according to the multi scheduling objectives of agricultural machinery steering gear workshop, and improved the immune clonal algorithm by using multi strategy population initialization and adaptive mutation operator. Wang (2020) simulated and iterated the agricultural machinery and farmland in a region by genetic algorithm, and screened out the best scheme of agricultural machinery scheduling. Wang (2020) combined heuristic rules, genetic algorithm and simulated annealing algorithm to design a three-level hybrid heuristic algorithm solving model for joint optimization of resource allocation and job scheduling of railway container central station.
It can be found that most of the current researches on combine scheduling transformed it into a VRP-TW problem or a transportation problem in operational research through the literature analysis, the scheduling was considered as only one agricultural machinery point and one type agricultural machinery, most of the modeling aimed at the lowest cost that ignored the indicators such as punctuality of service for farmland and utilization rate of harvesters. Based on the above research, this paper discussed the resource allocation of harvesters with multiple agricultural machinery points, multiple types, operation time window, space distance constraints in trans-regional operations, constructed a practical mathematical model of multi-objective programming, and used the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) to solve the problem. According to the obtained optimal scheme, combine could be effectively scheduled and reasonably distributed, and the purpose of maximizing the benefits of the machine owner, the farmer, and the trans-regional organization was finally achieved.