The Skill Vehicle Routing Problem

Cappanera, Paola; Gouveia, Luís; Scutellà, Maria Grazia

doi:10.1007/978-3-642-21527-8_40

Cited by 23 publications

(34 citation statements)

References 4 publications

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“…The above Skill VRP instances are solved by using our ILS, and the results are compared with the solutions obtained from a basic MIP model of Cappanera et al (2011) that is solved by using CPLEX 12.6. A time limit of 7200 s is imposed on CPLEX, and for instances not solved to optimality, we report the best values of the solutions found within this time limit.…”

Section: Results For Skill Vrp Instancesmentioning

confidence: 99%

“…A variant of the WSRP is the skill vehicle routing problem (Skill VRP), which is introduced by Cappanera et al (2011). The Skill VRP differs from other problems reviewed above in two aspects: (1) tasks do not have associated time windows, and (2) the routing costs depend both on the traveling distance and the technician in such a way that increasing the skill level of the technician causes an increase in costs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the Skill VRP does not involve time window and capacity constraints, we use only the geographical information of Solomon's instances to generate three types of geographical data for Skill VRP instances, namely, R, C and RC, which represent the random, clustered and a mixed of random and clustered geographical setting, respectively. Similar to Cappanera et al (2011) and Schwarze and Voß (2012), we consider a skill set with three levels 1, 2 and 3, where skill 1 denotes the lowest level, and skill 3 the highest. Each task i ∈ C is associated with a skill requirement s i ∈ {1, 2, 3}, which must be fulfilled by a technician k ∈ K having a skill levelŝ k ≥ s i , whereŝ k ∈ {1, 2, 3}.…”

Section: Skill Vrp Instancesmentioning

confidence: 99%

“…Each task i ∈ C is associated with a skill requirement s i ∈ {1, 2, 3}, which must be fulfilled by a technician k ∈ K having a skill levelŝ k ≥ s i , whereŝ k ∈ {1, 2, 3}. The skill data is randomly generated according to the four patterns introduced by Cappanera et al (2011), as given in the Table 7, where each row of values represent a pattern that indicates the distribution of skill requirements over tasks. For example, the first pattern {50, 10, 40} indicates that a task i has a skill requirement s i = 1 with probability 0.5, s i = 2 with probability 0.1 and s i = 3 with probability 0.4.…”

Section: Skill Vrp Instancesmentioning

confidence: 99%

“…The proposed ILS algorithm is also applied to solve a set of Skill VRP instances, which are generated based on the benchmark instances of Solomon (1987) and the skill pattern introduced by Cappanera et al (2011). As the Skill VRP does not involve time window and capacity constraints, we use only the geographical information of Solomon's instances to generate three types of geographical data for Skill VRP instances, namely, R, C and RC, which represent the random, clustered and a mixed of random and clustered geographical setting, respectively.…”

Section: Skill Vrp Instancesmentioning

confidence: 99%

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Iterated local search for workforce scheduling and routing problems

2017

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The integration of scheduling workers to perform tasks with the traditional vehicle routing problem gives rise to the workforce scheduling and routing problems (WSRP). In the WSRP, a number of service technicians with different skills, and tasks at different locations with pre-defined time windows and skill requirements are given. It is required to find an assignment and ordering of technicians to tasks, where each task is performed within its time window by a technician with the required skill, for which the total cost of the routing is minimized. This paper describes an iterated local search (ILS) algorithm for the WSRP. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP. The computational results indicate that the proposed algorithm can produce high-quality solutions in short computation times.

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Section: Results For Skill Vrp Instancesmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Skill Vrp Instancesmentioning

confidence: 99%

Section: Skill Vrp Instancesmentioning

confidence: 99%

Section: Skill Vrp Instancesmentioning

confidence: 99%

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Iterated local search for workforce scheduling and routing problems

2017

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Branch‐and‐price‐and‐cut for the manpower routing problem with synchronization constraints

Luo

Qin

Zhu

et al. 2016

Naval Research Logistics

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Abstract:In this article, we propose a branch-and-price-and-cut (BPC) algorithm to exactly solve the manpower routing problem with synchronization constraints (MRPSC). Compared with the classical vehicle routing problems (VRPs), the defining characteristic of the MRPSC is that multiple workers are required to work together and start at the same time to carry out a job, that is, the routes of the scheduling subjects are dependent. The incorporation of the synchronization constraints increases the difficulty of the MRPSC significantly and makes the existing VRP exact algorithm inapplicable. Although there are many types of valid inequalities for the VRP or its variants, so far we can only adapt the infeasible path elimination inequality and the weak clique inequality to handle the synchronization constraints in our BPC algorithm. The experimental results at the root node of the branch-and-bound tree show that the employed inequalities can effectively improve the lower bound of the problem. Compared with ILOG CPLEX, our BPC algorithm managed to find optimal solutions for more test instances within 1 hour.

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