An efficient heuristic for the multi-vehicle one-to-one pickup and delivery problem with split loads

In this study, we consider the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple vehicles as well as multiple stops by the same vehicle. In practice, split deliveries is a viable option in many settings where the load can be physically split, such as courier services of third party logistics operators. We propose an efficient heuristic that combines the strengths of Tabu Search and Simulated Annealing for the solution of MPDPSL. Results from experiments on two problems sets in the literature indicate that the heuristic is capable of producing good quality solutions in reasonable time. The experiments also demonstrate that up to 33\% savings can be obtained by allowing split loads; however, the magnitude of savings is dependent largely on the spatial distribution of the pickup and delivery points

Transit coordination with heterogeneous headways

We consider the transit coordination problem with heterogeneous headways. Timetables with heterogeneous headways improve coordination between transit lines and reduce transfer time for connecting passengers. Unfortunately, deviating from homogeneous headways impacts adversely on initial waiting times experienced prior to embarking on the initial vehicle of a trip. We focus on this trade-off between transfer waiting time and initial waiting time, which has not been explored previously, and develop a mathematical model to quantify the benefits of heterogeneous headways. We also propose a genetic algorithm (GA) to solve the transit coordination problem with heterogeneous headways and demonstrate the benefit of heterogeneous headways based on two examples from the literature and one real-life example based on the rail transit network of Istanbul. Computational results suggest that the GA solves the transit coordination problem within a reasonable time and significant benefits can be achieved by adopting timetables with heterogeneous headways

Dynamic programming and mixed integer programming based algorithms for the online glass cutting problem with defects and production targets

<p>In flat glass manufacturing, glass products of various dimensions are cut from a glass ribbon that runs continuously on a conveyor belt. Placement of glass products on the glass ribbon is restricted by the defects of varying severity located on the ribbon as well as the quality grades of the products to be cut. In addition to cutting products, a common practice is to remove defective parts of the glass ribbon as scrap glass. As the glass ribbon moves continuously, cutting decisions need to be made within seconds, which makes this online problem very challenging. A simplifying assumption is to limit scrap cuts to those made immediately behind a defect (a cut-behind-fault or CBF). We propose an online algorithm for the glass cutting problem that solves a series of static cutting problems over a rolling horizon. We solve the static problem using two methods: a dynamic programming algorithm (DP) that utilises the CBF assumption and a mixed integer programming (MIP) formulation with no CBF restriction. While both methods improve the process yield substantially, the results indicate that MIP significantly outperforms DP, which suggests that the computational benefit of the CBF assumption comes at a cost of inferior solution quality.</p

A branch and cut algorithm for the multi-vehicle one-to-one pickup and delivery problem with split loads

In this work we deal with the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple stops by the same vehicle. In practice split deliveries is a viable option in many settings such as courier services of third party logistics operators. We propose a branch-and-cut algorithm which employs valid inequalities devised for special cases of the MPDPSL, such as the Dial-a-Ride Problem, PDP and Split Delivery Vehicle Routing Problem. According to our computational experiments with randomly generated test instances, we may claim that the proposed algorithm can be used for small sized instances

An efficient heuristic for the multi-vehicle one-to-one pickup and delivery problem with split loads

In this study, we consider the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple vehicles as well as multiple stops by the same vehicle. In practice, split deliveries is a viable option in many settings where the load can be physically split, such as courier services of third party logistics operators. We propose an efficient heuristic that combines the strengths of Tabu Search and Simulated Annealing for the solution of the MPDPSL. Results from experiments on two problem sets in the literature indicate that the heuristic is capable of producing good quality solutions in reasonable time. The experiments also demonstrate that up to 33% savings can be obtained by allowing split loads; however, the magnitude of savings is dependent largely on the spatial distribution of the pickup and delivery locations