## 4 Conflict Resolution in the Actual Scheduling Process
Regarding time horizon planning, it was generally observed that as the time horizon of the planning of both original and controversial activities increases (i.e., more are planned), activities are likely to be slightly changed. The duration of the performance is the final characteristic of the activity. There is a noticeably clear pattern of gradually decreasing the deletion activities and increasing the modification of activities with increasing duration. For example, only one-third of short initial sessions (duration < 2 h) were modified compared to two-thirds of long sessions (>6 h); whereas one-third of short-term activities were deleted, compared with half of the long-term activities. This was expected because long-duration trips are also priority activities that cannot be easily carried over to the next day or missed. The tendency is less clear for conflict activities but is also much higher for the observed modification of exceptionally long conflict activities compared to deletion. Frequency distributions of discriminatory strategies show some common patterns of interest, but interactions between variables and their relative importance cannot be determined in this manner. For resolutions of represented conflicts, some cases were analyzed. The model is expected to show similar trends as those noted above, but will capture the impact of each variable in more detail, in addition to the driver and time flexibility of the variables and their presence, which have not been considered before. The scheduling of vehicles has been a subject of research for the past four decades. Many authors have investigated routing and planning aspects. Although many surveys have been conducted on the topic of vehicle planning, none of these consider the most recent research on modelling and decision methods. Because a large amount of new research has been undertaken in recent years, the authors provide an overview of the recent models. These models can be sub-divided into:  
* Models that take into consideration relevant metrics, targets, or combinations of targets;
* Models that follow the integrative optimization of routes for further business decisions;
* Models that investigate more accurate and more pronounced aspects. Most often, scheduling models consider costs as the main objective; however, these models overlook other important criteria, which are additional objectives. These criteria can be categorized into categories as follows:  
* Performance: level of services, quality, penalty, navigation; congestions;
* Planning: work and drive balance, number of drivers, timing, duration of operations, number of uncertainties;
* Routing: locations, the quality of infrastructure, speed, type of freight, road taxes;
* Reliability: the probability of failure, truck readiness, driver behavior;
* Sustainable and environmental factors: fuel consumption, emissions, safety criteria. The mentioned criteria may appear as the objectives (in a weighted, hierarchical, or multifunctional formulation) or as separate variables. Description of Algorithms for Recognition of Priorities of Contextual Data Provision during Cargo Transportation Processes  
For the cargo transportation process, it is necessary to build the services with adaptation properties. The intelligent adaptable smart service delivery system (IASDS) under development can be constructed for the autonomous provision of these services for drivers. Algorithms must be constructed to define the priority of service provision when it is at the right time and the right place. The system is aware of the surrounding environment and can help to identify the current, past, and likely future situations throughout the trip's duration, and help to choose the right direction. The infrastructure of road equipment is supported with a variety of sensors. The information (data) is provided from a wide spectrum RSU and can help in monitoring transport and other types of contextual data throughout the journey. Sensor data can be used for situational awareness.