DWSRP Data sets
DWSRP instance represents the problem to be optimized during reoptimization. To distinguish the instance that spans an entire working day, we refer to it as a DWSRP super instance. An arbitrary DWSRP super-instance, designated as I(N, K, δ), encompasses a total of N tasks scheduled throughout the entire working day, involving K teams, and embodies a specific degree of dynamism quantified by δ. Our aim is to create instances that manifest diverse degrees of dynamism, precisely δ = 0.2, 0.4, 0.6, 0.8, while varying the total task counts and team numbers, represented as (N, K) = (30, 2), (40, 3), (50, 4), (60, 5), (75, 5). The process of generating an instance denoted as I(N, K, δ) unfolds as follows. Considering a standard working day spanning 9 hours, equivalent to 540 minutes, we set τ max = 540. To ensure a gradual distribution of new task arrivals across the work period, we divide the day, denoted as (0, τ max], into d time intervals. At the start of the day, ⌊N/d⌋ tasks are assumed to be predetermined, thereby establishing the degree of dynamism, δ, for the given instance as δ = (N − ⌊N/d⌋)/N . The arrival time ai and the earliest start time ei for these static tasks are initialized to 0. For each interval t = 1, ..., d − 1, corresponding to the time interval ( τ max d (t − 1), τ max d t], ⌊N/d⌋ tasks are generated. The remaining (N − ⌊N/d⌋(d − 1)) tasks are allocated to the final time interval, i.e., time interval t = d, corresponding to ( τ max d (d − 1), τ max]. In the case of a task i generated for a time interval t, its arrival time ai is determined as a random value within the boundaries of the corresponding interval, while the earliest start time ei is set to match ai. The latest start time li for task i is calculated as the minimum of either ai + Uniform(10, 50) or τ max. Furthermore, both the processing time pi and the priority wi are established using uniform distributions of Uniform(5,25) and Uniform(1,5), respectively. Additionally, each task is assigned a random location within a rectangular metric space measuring 25 km by 25 km. We assume a traveling speed of 30 km per hour for each team, utilizing rectilinear distances to generate the distance matrix. We consider a skill set comprising five distinct skills, denoted as |Q| = 5. To determine the skill requirements for each task-skill pair (i, q), we employ a random number generator that generates values between 0 and 1. Specifically, we set uiq to 1 if the randomly generated number falls within the range of 0 to 0.5. In cases where a task i does not initially require any skill, i.e., uiq = 0 for all q ∈ Q, we then randomly select a skill to be associated with the task. A similar process is applied to determine the skill capabilities of the teams, resulting in the [vkq] matrix. In this manner, we have generated twenty DWSRP super-instances set, referred to as tight instances. Second set, referred to as loose instances, uniformly assigns task deadlines at τ max.