Pickup And Delivery Problem With Time Windows Research Articles

Routing Autonomous Vehicles in the Improving Initial Task Assignment and Avoiding Deadlock Method

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP is a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). In our former study, a cooperative algorithm, called the triple loop method, with autonomous distributed agents has been proposed. The Improving initial Task Assignment and Avoiding Deadlock method (ITAAD) is a faster algorithm than the triple loop method. In this paper, we propose a new vehicle routing method for the ITAAD. Results of computational experiments show effectiveness of the proposed routing method.

An Initial Assignment Method for Tasks Assignment and Routing Problem of Autonomous Distributed AGVs

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). As an approach to this problem, a cooperative algorithm with autonomous distributed agents has been proposed. The algorithm is able to plan deadlock-free routes even though the buffer capacity is less, but includes reformability at the point that computation time of that case increases drastically. This paper proposes an initial task assignment method to reduce computation time on planning routes. Results of computational experiments show effectiveness of the proposed method.

HYBRID ISLAND MODEL OF EVOLUTIONARY ALGORITHM FOR PICKUP AND DELIVERY PROBLEM WITH TIME WINDOWS

In this paper we discuss the possibilities of applying evolutionary algorithms to Pickup and Delivery Problem with Time Windows (PDPTW). Two variants of evolutionary algorithms are considered: with direct and indirect representation. Their pros and cons are illustrated by the results of performed experiments. The cooperation of these algorithms in hybrid island model indicates how they can help each other in achieving better solutions. Experimental study of such a system concludes this work.

Models and branch-and-cut algorithms for pickup and delivery problems with time windows

In the pickup and delivery problem with time windows (PDPTW), capacitated vehicles must be routed to satisfy a set of transportation requests between given origins and destinations. In addition to ...

Models and branch‐and‐cut algorithms for pickup and delivery problems with time windows

AbstractIn the pickup and delivery problem with time windows (PDPTW), capacitated vehicles must be routed to satisfy a set of transportation requests between given origins and destinations. In addition to capacity and time window constraints, vehicle routes must also satisfy pairing and precedence constraints on pickups and deliveries. This paper introduces two new formulations for the PDPTW and the closely related dial‐a‐ride problem (DARP) in which a limit is imposed on the elapsed time between the pickup and the delivery of a request. Several families of valid inequalities are introduced to strengthen these two formulations. These inequalities are used within branch‐and‐cut algorithms which have been tested on several instance sets for both the PDPTW and the DARP. Instances with up to eight vehicles and 96 requests (194 nodes) have been solved to optimality. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 49(4), 258–272 2007

A Cooperative Algorithm for Autonomous Distributed Vehicle Systems with Finite Buffer Capacity

Recently, there are so many researches on Autonomous Distributed Manufacturing Systems (ADMSs), where cooperation among agents is used to solve problems, such as the scheduling problem and the vehicle routing problem. We target ADMSs where an ADMS consists of two sub-systems: a Production System (PS) and an Autonomous Transportation System (ATS). This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for ATSs under conditions of given production schedule and finite buffer capacity. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW), and this paper gives a mathematical formulation of the problem. This paper, also, proposes a cooperative algorithm to obtain suboptimal solutions in which no deadlocks and buffer overflows occur. By computational experiments, we will examine the effectiveness of the proposed algorithm. Computational experiments show that the proposed algorithm is able to obtain efficient and deadlock-free routes even though the buffer capacity is less.

A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows

The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). Since it models several typical planning situations in operational transportation logistics and public transit, the PDPTW has attracted growing interest in recent years. This paper proposes a Grouping Genetic Algorithm (GGA) for solving the PDPTW which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request. The GGA is subject to a comparative test on the basis of two publicly available benchmark problem sets that comprise 9 and 56 PDPTW instances, respectively. The results show that the proposed GGA is competitive.

A Tabu Search Heuristic for a Full-Load, Multi-Terminal, Vehicle Scheduling Problem with Backhauling and Time Windows

The problem considered is the full-load pickup and delivery problem with time windows (PDPTW), and heterogeneous products and vehicles, where the assignment of pickup points to requests is not predetermined. Elements associated with tabu search, such as diversification by reversion to ‘junctions’ and the use of soft aspiration criteria, are embedded into our tabu search implementation. This metaheuristic is evaluated using random instances and selected data from a construction company in the U.K. The obtained results are compared against lower bounds from LP relaxation and also solutions from an existing multi-level heuristic.

Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows

The dynamic Pickup and Delivery Problem with Time Windows (PDPTW) is faced by courier companies serving same-day pickup and delivery requests for the transport of letters and small parcels. This article focuses on the dynamic PDPTW for which future requests are not stochastically modelled or predicted. The standard solution methodology for the dynamic PDPTW is the use of a rolling time horizon as proposed by Psaraftis. When assigning a new request to a vehicle it may be preferable to consider the impact of a decision both on a short-term and on a long-term horizon. In particular, better managing slack time in the distant future may help reduce routing cost. This paper describes double-horizon based heuristics for the dynamic PDPTW. Computational results show the advantage of using a double-horizon in conjunction with insertion and improvement heuristics.

Exact and heuristic methods for a full-load, multi-terminal, vehicle scheduling problem with backhauling and time windows

The problem considered is the full-load pickup and delivery problem with time windows (PDPTW), and heterogeneous products and vehicles, where the assignment of pickup points to requests is not predetermined. The problem is first formulated as a 0-1 LP, then a hybrid algorithm is developed, which chooses dynamically between a Greedy heuristic and one based on Regret costs. A multi-level constructive heuristic that consists of three post-optimizers is presented. Two lower bounds are used to evaluate the performance of the proposed heuristics when tested on random instances and selected data from a construction company.

PICKUP AND DELIVERY WITH TIME WINDOWS: ALGORITHMS AND TEST CASE GENERATION

In the pickup and delivery problem with time windows (PDPTW), vehicles have to transport loads from origins to destinations respecting capacity and time constraints. In this paper, we present a two-phase method to solve the PDPTW. In the first phase, we apply a novel construction heuristics to generate an initial solution. In the second phase, a tabu search method is proposed to improve the solution. Another contribution of this paper is a strategy to generate good problem instances and benchmarking solutions for PDPTW, based on Solomon's benchmark test cases for VRPTW. Experimental results show that our approach yields very good solutions when compared with the benchmarking solutions.