Comparison of Randomized Solutions for Constrained Vehicle Routing Problem

In this short paper, we study the capacity-constrained vehicle routing problem (CVRP) and its solution by randomized Monte Carlo methods. For solving CVRP we use some pseudorandom number generators commonly used in practice. We use linear, multiple-recursive, inversive, and explicit inversive congruential generators and obtain random numbers from each to provide a route for CVRP. Then we compare the performance of pseudorandom number generators with respect to the total time the random route takes. We also constructed an open-source library github.com/iedmrc/binary-cws-mcs on solving CVRP by Monte-Carlo based heuristic methods.Comment: 6 pages, 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE), 12-13 June 2020, Istanbul, Turke

Ant colony optimization approach for the capacitated vehicle routing problem with simultaneous delivery and pick-up

We propose an Ant Colony Optimization (ACO) algorithm to the NPhard Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP). In VRPSDP, commodities are delivered to customers from a single depot utilizing a fleet of identical vehicles and empty packages are collected from the customers and transported back to the depot. The objective is to minimize the total distance traveled. The algorithm is tested with the well-known benchmark problems from the literature. The experimental study indicates that our approach produces comparable results to those of the benchmark problems in the literature

A Heuristic Solution Based on Clarke & Wright's Savings Algorithm for the Optimization of Sludge Hauling: the case of a Portuguese company

Sewage sludge originating from wastewater treatment plants (WWTPs) can be a major cause of environmental pollution and their appropriate management should be viewed as a priority. A critical aspect in sludge management practices is route optimization since significant costs are associated with the transportation of waste. In this work, we propose a heuristic solution based on Clarke-Wright savings method for the sludge collection problem of a Portuguese company within a perspective of reducing collection costs (transportation) and negative impacts on the environment. Two case studies were considered in the methodology: the first one focused on the comparison between the general weekly collection plan presently used by the company and the solution generated by CW algorithm (Case Study A); the second one explored a new hypothetical scenario centered on the expansion of the company’s activities (Case Study B). In general, the application of CW method led to a decrease in traveled distances and transportation costs, as well as carbon dioxide emissions. Specifically, with the adoption of the optimized plan in Case Study A we found that a single vehicle (instead of three) would be capable of performing sludge hauling operations in a given week leading to total weekly savings of 346 km, representing a decrease of almost 40% for both cost and pollutant emissions. Regarding Case Study B, the model suggests that for about 76% of the initial cost, the company would be capable of attending twice the number of customers, i.e., via route optimization, it is possible to expand their client portfolio while still creating savings. Moreover, a sensitivity analysis (SA) was carried out in order to check the robustness of results when undergoing changes in the input parameters. We found that vehicle capacity and fuel price are two important factors in route optimization with model results greatly influenced by changes in both parameters.info:eu-repo/semantics/publishedVersio

The Vehicle Rescheduling Problem

The capacitated vehicle routing problem is to find a routing schedule describing the order in which geographically dispersed customers are visited to satisfy demand by supplying goods stored at the depot, such that the traveling costs are minimized. In many practical applications, a long term routing schedule has to be made for operational purposes, often based on average demand estimates. When demand substantially differs, constructing a new schedule is beneficial. The vehicle rescheduling problem is to find a new schedule that not only minimizes the total traveling costs but also minimizes the costs of deviating from the original schedule. In this paper two mathematical programming formulations of the rescheduling problem are presented as well as two heuristic methods, a two-phase heuristic and a modified savings heuristic. Computational and analytical results show that for sufficiently high deviation costs, the two-phase heuristic generates a schedule that is on average close to optimal or even guaranteed optimal, for all considered problem instances. The modified savings heuristic generates schedules of constant quality, however the two-phase heuristic produces schedules that are on average closer to the optimum.vehicle routing;operational planning;vehicle rescheduling problem

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization

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

Mixed truck delivery systems with both hub-and-spoke and direct shipment

Department of Shipping and Transport Logistics2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

An ant colony algorithm for the mixed vehicle routing problem with backhauls

The Vehicle Routing Problem with Pickup and Delivery (VRPPD) is a variant of the Vehicle Routing Problem where the vehicles are not only required to deliver goods but also to pick up some goods from the customers. The Mixed Vehicle Routing Problem with Backhauls (MVRPB) is a special case of VRPPD where each customer has either a delivery or a pickup demand to be satisfied and the customers can be visited in any order along the route. Given a fleet of vehicles and a set of customers with known pickup or delivery demands MVRPB determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The objective is to minimize the total distance traversed with the least number of vehicles. For this problem, we propose an Ant Colony Optimization algorithm with a new visibility function which attempts to capture the “delivery” and “pickup” nature of the problem. Our numerical tests to compare the performance of the proposed approach with those of the well-known benchmark problems reveal that the proposed approach provides encouraging results

A Statistical Approach to the TSP

This paper is an example of the growing interface between statistics and mathematical optimization. A very efficient heuristic algorithm for the combinatorially intractable TSP is presented, from which statistical estimates of the optimal tour length can be derived. Assumptions, along with computational experience and conclusions are discussed.Supported in part by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)

Optimal Wind Farm Cabling

Wind farm cable length has a direct impact on the project cost, reliability and electrical losses. The optimum cable layout results in a lower unit cost of generating electricity offshore. This paper explores three cabling structures: the string structure, ring structures and multi-loop structure on a 3D seabed. The newly proposed multi-loop structure increases reliability and proves to be most economic when the failure rate and mean time to repair (MTTR) of cables are relatively high. Particle swarm optimization (PSO) is used to find the optimal substation location that minimizes the overall cable distance