DESIGNING DISTRIBUTION ROUTES OF FMCG PRODUCT IN PT ABC WITH MULTI-TRIP VEHICLE ROUTING PROBLEM AND TIME WINDOW USING BRANCH AND BOUND METHOD TO MINIMIZE TRAVEL DISTANCE

PT.ABC is a food industry that produces chocolate and cocoa products that have been established for 175 years and located in Bandung. In a distribution process PT ABC have 29 local customer around West Java, with delivery confirmation done one day before loading date to customer. Delay in delivery to customer is the main problem of PT ABC that need to solve, they reach 21% delay in distribution customer order for 6 month. It will affect increase in transportation cost because PT ABC should pay penalty cost. There are 4 factors impact delay of distribution process, but delay in departure of fleet has the biggest rate , about 61%. It caused by there is no schedule in fleet departure and there is no right designing route for each customer. In this research, this case will be done by create scheduling and vehicle routing problem with characteristic contained Multi-Trip Vehicle Routing Problem and Vehicle Routing Problem with Time Window using Branch and Bound Algorithm to minimize travel distance. In the end, the travel distance can be minimized 17,1% and the departure time schedule for each fleet is explain that each kind of fleet should be depart form depot/distribution center at 07:30. In this research does not considering penalty cost and transportation cost, so researcher suggest for further research can considering transportation cost component and can also used other algorithm. The traffict jam and use horizon planning more than one day can also considered. Keywords : Multiple-Trip Vehicle Routing Problem, Vehicle Routing Problem with Time Window, Branch and Bound Algorithm

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

Cargo Consolidation and Distribution Through a Terminals-Network: A Branch-And-Price Approach

Less-than-truckload is a transport modality that includes many practical variations to convey a number of transportation-requests from the origin locations to their destinations by using the possibility of goods-transshipments on the carrier?s terminals-network. In this way logistics companies are required to consolidate shipments from different suppliers in the outbound vehicles at a terminal of the network. We present a methodology for finding near-optimal solutions to a less-than-truckload shipping modality used for cargo consolidation and distribution through a terminals-network. The methodology uses column generation combined with an incomplete branch-and-price procedure.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Combining Column Generation and Lagrangian Relaxation

Although the possibility to combine column generation and Lagrangian relaxation has been known for quite some time, it has only recently been exploited in algorithms. In this paper, we discuss ways of combining these techniques. We focus on solving the LP relaxation of the Dantzig-Wolfe master problem. In a first approach we apply Lagrangian relaxation directly to this extended formulation, i.e. no simplex method is used. In a second one, we use Lagrangian relaxation to generate new columns, that is Lagrangian relaxation is applied to the compact for-mulation. We will illustrate the ideas behind these algorithms with an application in Lot-sizing. To show the wide applicability of these techniques, we also discuss applications in integrated vehicle and crew scheduling, plant location and cutting stock problems.column generation;Lagrangean relaxation;cutting stock problem;lotsizing;vehicle and crew scheduling

A solution approach for dynamic vehicle and crew scheduling

In this paper, we discuss the dynamic vehicle and crew schedulingproblem and we propose a solution approach consisting of solving asequence of optimization problems. Furthermore, we explain why itis useful to consider such a dynamic approach and compare it witha static one. Moreover, we perform a sensitivity analysis on ourmain assumption that the travel times of the trips are knownexactly a certain amount of time before actual operation.We provide extensive computational results on some real-world datainstances of a large public transport company in the Netherlands.Due to the complexity of the vehicle and crew scheduling problem,we solve only small and medium-sized instances with such a dynamicapproach. We show that the results are good in the case of asingle depot. However, in the multiple-depot case, the dynamicapproach does not perform so well. We investigate why this is thecase and conclude that the fact that the instance has to be splitin several smaller ones, has a negative effect on the performance.transportation;vehicle and crew scheduling;large-scale optimization;dynamic planning

Vehicle and crew scheduling: solving large real-world instances with an integrated approach

In this paper we discuss several methods to solve large real-worldinstances of the vehicle and crew scheduling problem. Although,there has been an increased attention to integrated approaches forsolving such problems in the literature, currently only small ormedium-sized instances can be solved by such approaches.Therefore, large instances should be split into several smallerones, which can be solved by an integrated approach, or thesequential approach, i.e. first vehicle scheduling and afterwardscrew scheduling, is applied.In this paper we compare both approaches, where we considerdifferent ways of splitting an instance varying from very simplerules to more sophisticated ones. Those ways are extensivelytested by computational experiments on real-world data provided bythe largest Dutch bus company.

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools