A Survey On Multi Trip Vehicle Routing Problem

The vehicle routing problem (VRP) and its variants are well known and greatly explored in the transportation literature. The vehicle routing problem can be considered as the scheduling of vehicles (trucks) to a set of customers under various side constraints. In most studies, a fundamental assumption is that a vehicle dispatched for service finishes its duty in that scheduling period after it returns back to the depot. Clearly, in many cases this assumption may not hold. Thus, in the last decade some studies appeared in the literature where this basic assumption is relaxed, and it is allowed for a vehicle to make multiple trips per period. We consider this new variant of the VRP an important one with direct practical impact. In this survey, we define the vehicle routing problem with multiple trips, define the current state-of-the-art, and report existing results from the current literature

Pemecahan Masalah Rute Kendaraan Dengan Trip Majemuk, Jendela Waktu Dan Pengantaran-penjemputan Simultan Menggunakan Algortima Genetika

Vehicle routing problem (VRP) is one of decision problems having an important role in transportation and distribution activity in the logistic management. The VRP deals with determining vehicle routes that minimizes total distance by satisfying the following constraints: (1) each route starts and ends at the depot, (2) each vehicle serves only one route, (3) each costumer is served by one route, (4) all customers must be served, and (5) total load for each route does not exceed the vehicle capacity. In literature, this definition is the definition for the basic or classical VRP. This paper discusses an extension of the basic VRP including the following characteristics: (1)multiple trips (MT), (2) time windows (TW), and (3) simultaneous pickup-delivery (SPD). A solution method based on genetic algorithm (GA) is proposed to solve the VRP discussed in this papaer. The proposed GA is examined using some hypothetical instances

GIS Oriented Service Optimization Tool For Fecal Sludge Collection

In developing countries most of the urban dwellers don’t have access to sewer system. People are mostly using “onsite” systems such as septic tanks or pit latrines that need to be emptied periodically, as the densely built urban environment won’t allow new pits to be dug every time they fill up. In the conventional fecal sludge collection systems, authorities are collecting the sludge from house to house and dump on the plant. Fecal sludge collection system is different from traditional vehicle routing and even from solid waste collection system in terms of dynamic collection points, urgency of getting the service and diversity of demand. Due to those vibrant factors authorities are facing proper networking and management problems. This research describes algorithms that can accommodate constraints and prioritized customers who need immediate service. The GPS log data of the fecal sludge collection truck that maintained by Nonthaburi Municipality, Thailand has been considered as the base data during the development of this application. Spatial analysis has been done using Geographic Information Systems (GIS).Tabu Search has been implemented in order to optimize. Basically two algorithms were produced for assisting fecal sludge collection systems. First algorithm was able to produce multiple trip for each vehicle if required considering all the customers having equal priority, time window. The second one was able to perform optimization that can accommodate priority along with the first one. Input for the algorithms were very simple; distance matrix having distance between each customers and plant, customer order with latitude, longitude, order unit, time window, priority and vehicles with capacity. Algorithms were able to produce better result than the actual operation or even from shortest path algorithm in term of distance. After optimization, efficiency of the algorithms were tested with the actual travelling distance. Travelling distance were reduced to half compare to actual cost and it ensured maximum utilization of vehicle capacity by allocating maximum number of customers in each route

A review of the Tabu Search Literature on Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is one of the most widely studied problems inrncombinatorial optimization. It has long been known to be NP-hard and hence research onrndeveloping algorithms for the TSP has focused on approximate methods in addition to exactrnmethods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. Inrnthis paper, we review the tabu search literature on the TSP, point out trends in it, and bringrnout some interesting research gaps in this literature.

PERANCANGAN RUTE ARMADA DI PT XYZ MENGGUNAKAN ALGORITMA TABU SEARCH PADA VEHICLE ROUTING PROBLEM HETEROGENEOUS FLEET WITH TIME WINDOW UNTUK MEMINIMASI BIAYA TRANSPORTASI

PT. XYZ adalah sebuah perusahaan yang mengkhususkan diri dalam pembuatan suku cadang dan ko mponen motor. Perusahaan terletak di daerah Cimahi, Jawa Barat dan mulai beroperasi pada tahun 1969. Perusahaan ini memiliki gudang sebagai tempat penyimpanan bahan baku dan finish good. Finish good tersebut akan didistribusikan ke luar kota Bandung. PT. XYZ sering mengalami keterlambatan pengiriman sehingga menyebabkan biaya yang harus dikeluarkan oleh perusahaan lebih besar. Penelitian ini bertujuan untuk menghasilkan rute yang dapat meminimasi total biaya transportasi yang harus dikeluarkan oleh perusahaan. Permasalahan yang terjadi pada PT. XYZ merupakan permasalahan umum pada bidang transportasi yang pada umumnya diselesaikan dengan pendekatan Vehicle Routing Problem. VRP yang terjadi pada PT. XYZ termasuk kedalam karakteristik VRP with Heterogeneous Fleet, VRP with Time Windows dan VRP with Split Delivery. Permasalahan ini diselesaikan menggunakan Algoritma Tabu Search dan Algoritma Nearest Neighbour sebagai solusi awal yang digunakan untuk masukkan dalam algoritma Tabu Search. Pendekatan VRP menggunakan algoritma Tabu Search mampu menghasilkan rute yang dapat meminimasi total biaya transportasi secara keseluruhan sebesar 33% dari kondisi eksisting

Research of Oil Product Secondary Distribution Optimization Based on Collaborative Distribution

AbstractDuring peak seasons, the petrol company's oil supply capacity is insufficient, therefore, with limited trucks, adjusting the distribution quantity of petrol station and formulating an effective distribution route can minimize the total cost and maximize the vehicle utilization. In this paper we observe the extension of the multi-depot half open vehicle routing problem with time windows (MDHOVRPTW) in oil product secondary distribution. Based on the characteristics of secondary distribution and MDHOVRPTW problem, this paper formulates oil distribution model intra-area with distribution quantity and distribution routing as decision variables. A proposed algorithm is applied to solve this model and result compared with the traditional non-cooperative method to verify the effectiveness of collaborative distribution

The Waste Collection Vehicle Routing Problem with Time Windows in a City Logistics Context

AbstractCollection of waste is an important logistic activity within any city. In this paper we study how to collect waste in an efficient way. We study the Waste Collection Vehicle Routing Problem with Time Window which is concerned with finding cost optimal routes for garbage trucks such that all garbage bins are emptied and the waste is driven to disposal sites while respecting customer time windows and ensuring that drivers are given the breaks that the law requires. We propose an adaptive large neighborhood search algorithm for solving the problem and illustrate the usefulness of the algorithm by showing that the algorithm can improve the objective of a set of instances from the literature as well as for instances provided by a Danish garbage collection company

Minimizing Total Tardiness in the m-Machine Flow-Shop Problem by Heuristic Algorithms

In this work the m-machine permutation flow-shop problem has been considered. The permutation flow-shop scheduling problem where a set of jobs have to be scheduled on a set of machines in the same order. We propose  heuristic algorithms for the flow-shop problem to minimizing the total tardiness. A new genetic and Tabu search algorithm which initialized by the solution of EDD, NEH and EN algorithm. Computational experiments are performed on benchmark instances and the results show the good performances of these methods. Finally, some future research directions are given