Solving Delivery Problems in Distribution Systems

The subject matter of this article is the optimization of delivery problems (Vehicle Routing Problems – VRP) with regard to distribution systems. These issues belong to the class of NP-hard problems. Current solutions to various delivery problems (VRP, TSP, MTSP, VRPTW, RDPTW) were analysed. Two examples were presented. In a transport and production task, a marginal cost equalling (MCE) algorithm was used. In the case of a Multi Depot Vehicle Routing Problem (MDVRP), an evolutionary algorithm was used

A solution approach for multi-trip vehicle routing problems with time windows, fleet sizing, and depot location

RÉSUMÉ: We present a solution approach for a multi-trip vehicle routing problem with time windows in which the locations of a prescribed number of depots and the fleet sizes must also be optimized. Given the complexity of the task, we divide the problem into subproblems that are solved sequentially. First, we address strategic decisions, which are solved once and remain constant thereafter. Depots are allocated by solving a p-median problem and fleet sizes are determined by identifying the vehicle requirements of several worst-case demand instances. Then, we address the operational planning aspect: optimizing the vehicle routes on a daily basis to satisfy the fluctuating customer demand. We assign customers to depots based on distance and “routing effort,” and for the routing problem we combine a tailor-made branch-and-cut algorithm with a heuristic consisting of a route construction phase and packing of routes into vehicle trips. Our strategic decision models are robust in the sense that when applied to unseen data, all customers could be visited with the allocated fleet sizes and depot locations. Our operational routing methods are both time and cost-effective. The exact method yields acceptable optimality gaps in 20 min and the heuristic runs in less than 2min, finding optimal or near-optimal solutions for small instances. Finally, we explore the trade-off between depot and fleet costs, and routing costs to make recommendations on the optimal number of depots. Our solution approach was entered into the 12th AIMMS-MOPTA Optimization Modeling Competition and was awarded the first prize

Utilizing ant colony optimization and intelligent water drop for solving multi depot vehicle routing problem

Multi-depot vehicle routing problem (MDVRP) is a real-world variant of the vehicle routing problem (VRP). MDVRP falls under NP-hard problem where trouble in identifying the routes for the vehicles from multiple depots to the customers and then, returning to the similar depot. The challenging task in solving MDVRP is to identify optimal routes for the fleet of vehicles located at the depots to transport customers' demand efficiently. In this paper, two metaheuristic methods have been tested for MDVRP which are Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD). The proposed algorithms are validated using six MDVRP Cordeau's data sets which are P01, P03, P07, P10, P15 and P21 with 50, 75, 100, 249, 160 and 360 customers, respectively. Thus, the results using the proposed algorithm solving MDVRP, five out of six problem data sets showed that IWD is more capable and efficient compared to ACO algorithm

Dynamic approach to solve the daily drayage problem with travel time uncertainty

The intermodal transport chain can become more e cient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the di erent vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

A satellite navigation system to improve the management of intermodal drayage

The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

Given a set of timetabled tasks, the multi-depot vehicle scheduling problemis a well-known problem that consists of determining least-cost schedulesfor vehicles assigned to several depots such that each task is accomplishedexactly once by a vehicle. In this paper, we propose to compare theperformance of five different heuristic approaches for this problem,namely, a heuristic \\mip solver, a Lagrangian heuristic, a columngeneration heuristic, a large neighborhood search heuristic using columngeneration for neighborhood evaluation, and a tabu search heuristic. Thefirst three methods are adaptations of existing methods, while the last twoare novel approaches for this problem. Computational results on randomlygenerated instances show that the column generation heuristic performs thebest when enough computational time is available and stability is required,while the large neighborhood search method is the best alternative whenlooking for a compromise between computational time and solution quality.tabu search;column generation;vehicle scheduling;heuristics;Lagrangian heuristic;large neighborhood search;multiple depot

Warehouse Storing and Collecting of Parts

This report deals with reducing the high costs resulting from the wear and tear of the fork-lifts used to store or collect items in a warehouse. Two problems were identified and addressed separately. One concerns the way items should be stored or collected at storage locations on the shelves of one corridor. The other problem seeks for an efficient way to define which fork-lift should operate on each corridor, and the order by which the fork-lifts should visit the corridors. We give to both problems formulations that fit in the framework of combinatorial optimization