DWSRP Data sets

Published: 25 August 2023| Version 1 | DOI: 10.17632/rg9pshry4m.1
Onur Demiray, Eda Yucel, Doruk Tolga


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.



Dynamic Routing, Workforce Planning