Single Vehicle Scheduling Research Articles

Marginal Effect-aware Multiple-Vehicle Scheduling for Road Data Collection: A Near-optimal Result

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers’ collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers’ data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/ e ) ¼ -approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines.

Improved algorithms for single vehicle scheduling on tree/cycle networks

The single vehicle scheduling problems based on tree/cycle networks are studied in this paper. Each customer, assumed as a vertex on the given network, has a release time and a service time requirement. The single vehicle starts from the depot and aims to serve all the customers. The objective of the problem is to find the optimal routing schedule so as to minimize the makespan. We provide a $$\frac{16}{9}$$ -approximation algorithm and a $$\frac{48}{25}$$ -approximation algorithm for the tour-version and path-version of single vehicle scheduling problem on a tree, respectively. For the tour-version and path-version of single vehicle scheduling problem on a cycle, we present a $$\frac{5}{3}$$ -approximation algorithm and a $$\frac{29}{17}$$ -approximation algorithm, respectively.

Approximation algorithms for single vehicle scheduling problems with release and service times on a tree or cycle

We consider the single vehicle scheduling problem in which the customers are located at the vertices of a tree or a cycle, and have release and service time requirements. The objective is to minimize the makespan. In the tour-version the makespan means the time when the vehicle returns to its initial location after serving all customers. While in the path-version the makespan refers to the maximum completion time of all customers. For the problem on a tree, we present a 9/5-approximation algorithm for the tour-version and a 27/14-approximation algorithm for the path-version. For the problem on a cycle, we present 12/7-approximation algorithms for both versions.

JIT Single Vehicle Scheduling in a Mixed-Model Assembly Line

This paper focus on single vehicle scheduling problem minimizing the line-side inventory of mixed-model assembly lines, considering the assembly sequence as an input.An exact algorithm is proposed based on three newly introduced concepts: conflicting, tight subset and complete conflicting subset, which divide the transportation operations into three classes.The solution generated by the algorithm is proved to be optimal. The algorithm is compared with the most popular FCFS (first-come, first-served) rule, and results shows the advantages of the algorithm, especially for large instances of the problem.

Better approximation ratios for the single‐vehicle scheduling problems on line‐shaped networks

AbstractWe consider two variants of the single‐vehicle scheduling problem on line‐shaped networks. Let L = (V, E) be a line, where V = {v1, v2, … , vn} is a set of n vertices and E = {{vi, vi+1}|i = 1, 2, … , n − 1} is a set of edges. The travel times w(u, v) and w(v, u) are associated with each edge {u, v} ∈ E, and each job, which is also denoted as v and is located at vertex v ∈ V, has release time r(v) and handling time h(v). There is a single vehicle, which is initially situated at v1 ∈ V, and visits all vertices to process the jobs before it returns back to v1. The first problem asks to find an optimal routing schedule of the vehicle that minimizes the completion time. This is NP‐hard [21], and there exists an approximate algorithm with the approximation ratio of 2 [12]. In this paper, we improve this ratio to 1.5. On the other hand, the second problem minimizes the maximum lateness, under the assumption that all release times r(v) are zero, but there are due times d(v) for v ∈ V and d(vn+1) for the vehicle. This problem is also NP‐hard [13]. We improve the previous best‐known approximation ratio of 2, which was obtained in [11], to 1.5. © 2002 Wiley Periodicals, Inc.

Shipping problems with body clock constraints

Time window has been a common form of time constraint considered in the literature. Basically, a time window is a time period, defined by the earliest and latest times, when a node is available for traveling through. Although many variants of transportation problem in time-window networks have been proposed, no one consider the possibility that time windows may be associated with the moving travelers or vehicles so that they can travel only in these time periods. In this paper, this new variant of time-window constraint, called body clock constraint, is proposed. We assume that each vehicle has its own body clock and a capacity limitation for carrying goods, and we are requested to determine a minimal time schedule for sending a certain amount of goods from source to destination in a time-window network. Specifically, the problem is studied in two cases, where the first case considers single-vehicle scheduling and the second multiple vehicles. For both of them, algorithms are presented to find the optimum schedule. Scope and purpose Time window has been a common form of time constraint considered in the literature. Basically, a time window is a time period, defined by the earliest and latest times, when a node is available for traveling through. There are many practical situations, where time windows can be used to describe the time constraints associated with the nodes and arcs on a network. For example, a time window in a transportation network may be the time period that a service or transition facility is available for the traveler to pass through. Similarly, a time window associated with an arc may be the time that a transportation channel is open. In vehicle routing problems and traveling salesman problem, a time window may represent the time period that a customer needs to be visited. Although there are many researches on the transportation problem in time-window networks, no previous researches consider the situation that time windows may be associated with the travelers or vehicles. In this paper we consider this new variant of time-window constraints, called body clock constraint. The body clock comes from the daily activities of people habits and the operation cycles of equipments. For example, a driver must temporarily stop his/her journey to have a meal or to rest at night. Likewise, a machine may need to be cooled to prevent from damage and to be maintained periodically. In this paper, we use the body clock constraint to model this kind of periodicity, and some shipping problems with body clock constraints are studied.

Single-vehicle scheduling with time window constraints

In the single-vehicle scheduling problem with time window constraints, a vehicle has to visit a set of sites on a graph, and each site must be visited after its ready time but no later than its deadline. The goal is to minimize the total time taken to visit all sites. We prove the conjecture proposed by Psaraftis et al.: if the topological graph is a straight line, the problems are NP-hard for both part and tour version. In addition, we give an O(n2) algorithm to solve a special case where all n sites have a common ready time. This algorithm illustrates a duality relationship between the vehicle scheduling problems with arbitrary ready times and that with arbitrary deadlines on a straight line. Copyright © 1999 John Wiley & Sons, Ltd.

Vehicle scheduling on a tree with release and handling times

In this paper, we consider a single-vehicle scheduling problem on a tree-shaped road net-work. Let T =(V,E) be a tree, where V is a set of n vertices and E is a set of edges. A task is located at each vertex v, which is also denoted as v. Each task v has release time r(v) and handling time h(v). The travel times c(u, v) and c(v, u) are associated with each edge (u, v) of E. The vehicle starts from an initial vertex v_0 of V, visits all tasks v in V for their processing, and returns to v_0 . The objective is to find a routing schedule of the vehicle that minimizes the completion time, denoted as C (i.e., the time to return to v_0 after processing all tasks). We call this problem TREE-VSP(C). We first prove that TREE-VSP(C) is NP-hard. However, we then show that TREE-VSP(C) with a depth-first routing constraint can be exactly solved in O(n log n) time. Moreover, we show that, if this exact algorithm is used as an approximate algorithm for the original TREE-VSP(C), its worst-case performance ratio is at most two.

VEHICLE SCHEDULING ON A TREE TO MINIMIZE MAXIMUM LATENESS

In this paper we deal with a single-vehicle scheduling problem on a tree-shaped road network. Let T = (V, E) be a tree, where V is a set of n vertices and E is a set of edges. A task is located at each vertex v E V, which is also denoted as v. Each task v has its due date d(v) and processing time p(v). The travel times w(u, v) and w(v, U,) are associated with each edge (U, v) E E. The vehicle starts from an initial vertex VQ E V, visits all tasks v E V for their processing (no preemption is allowed) and returns to VQ. The problem asks to find a routing schedule of the vehicle that minimizes the maximum lateness from the due dates of tasks. This problem is called TREE-VSP(Lmax). We prove that TREE-VSP(Lmax) is strongly NP-hard in general, but show that it can be solved in polynomial time if only depth-first routing is allowed for the vehicle.