Type II sensitivity analysis of cost coefficients in the degenerate transportation problem

This paper focuses on sensitivity analysis of the degenerate transportation problem (DTP) when perturbation occurs on one cost coefficient. The conventional Type I sensitivity analysis of the transportation problem (TP) determines the perturbation ranges for the invariant optimal basis. Due to different degenerate optimal basic solutions yielding different Type I ranges, the Type I range is misleading for the DTP. Type II sensitivity analysis, which determines the perturbation ranges for the invariant shipping pattern, is more practical for the DTP. However, it is too tedious to obtain Type II ranges by enumerating all optimal basic solutions and all primal optimal basic solutions while getting the union of each corresponding Type I ranges. Here, we propose two labeling algorithms to determine the Type II ranges of the cost coefficient. Besides, three lemmas are provided for obtaining the upper bound or lower bound of the Type II ranges of the cost coefficient directly under specific conditions of the DTP. A numerical example is given to demonstrate the procedure of the proposed labeling algorithms and computational results have been provided.     

The dial-a-ride problem: models and algorithms

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pickup and delivery requests between origins and destinations. The aim is to plan a set of m minimum cost vehicle routes capable of accommodating as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and a summary of the most important models and algorithms is provided. This is an updated version of a paper that appeared in 4OR 1:89–101, 2003.     

The Dial-a-Ride Problem (DARP): Variants,modeling issues and algorithms

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pick-up and drop-off requests between origins and destinations. The aim is to plan a set of m minimum cost vehicle routes capable of accommodating as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and classified and some modeling issues are discussed. A summary of the most important algorithms is provided.AMS classification: 90B06, 90C27, 90C59     

On lexicographic optimal solutions in transportation problems

Lexicographic versions of the cost minimizing transportation problem (CMTP) and the time minimizing transportation problem (TMTP) are presented in this paper. In addition to minimizing the quantity sent on the costliest routes in a cost minimizing transportation problem. an attempt is made to minimize the quantity transported on the second-costliest routes. if the shipment on the costliest routes is as small as possible and the quantity shipped on the third-costliest routes, if the shipments on the costliest and the second- costliest routes are as small as possible. and so on. In a lexicographic time minimizing transportation problem one is not only interested in minimizing the transportation cost on the routes of the longest duration but also on the routes of second longest, third-longest duration and so on. For finding lexicographic optimal solutions (LOS) of lexicographic cost minimizing and time minimizing transportation problems a standard cost minimizing transportation problem is formulated whose optimal solution is shown to provide the answer. Some extensions are also discussed     

Modelling and solving an <Emphasis Type="Italic">m</Emphasis>-location, <Emphasis Type="Italic">n</Emphasis>-courier,priority-based planning problem on a network

In this paper, we study an m-location, n-courier, priority-based planning problem on a network, which we refer to as the Courier Planning Problem (CPP). The CPP arises on a daily basis in the context of planning the transportation of materials and personnel in peacetime for the Turkish Armed Forces. The main issue addressed in CPP is to transport as many of deliverables as possible from their origins to their destinations via a fleet of transportation assets (couriers) that operate at fixed routes and schedules. Priorities must be taken into account and constraints on the routes, operating schedules, and capacities of the transportation assets must be obeyed. Time windows may be specified for some or all transportation requests and must be satisfied. We study the CPP as well as its two extensions, and present integer programming formulations based on the multi-commodity flow structure. The formulations are tested on real world-based data and display satisfactory computational performance. Our main contributions are to develop an effective formulation scheme for a complicated large-scale real world problem and to demonstrate that such problems are solvable via commercial general purpose solvers through meticulous modelling.     

A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars

The purpose of this article is to propose a tabu search heuristic for the split delivery Vehicle Routing Problem with Production and Demand Calendars (VRPPDC). This new problem consists of determining which customers will be served by a common carrier, as well as the delivery routes for those served by the private fleet, in order to minimize the overall transportation and inventory costs. We first model this problem and then propose a simple decomposition procedure that can be used to provide a starting solution. Next, we introduce a new tabu search heuristic and we describe two new neighbor reduction strategies. Finally, we present the results of our extensive computational tests. According to these tests, our reduction strategies are efficient not only at reducing computing time but also at improving the overall solution quality.     

A multi-space sampling heuristic for the vehicle routing problem with stochastic demands

The vehicle routing problem with stochastic demands consists in designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distributions. This paper proposes a simple yet effective heuristic approach that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set partitioning formulation to sample the solution space and find high-quality solutions for the problem. Computational experiments on benchmark instances from the literature show that the proposed approach is competitive with the state-of-the-art algorithm for the problem in terms of both accuracy and efficiency. In experiments conducted on a set of 40 instances, the proposed approach unveiled four new best-known solutions (BKSs) and matched another 24. For the remaining 12 instances, the heuristic reported average gaps with respect to the BKS ranging from 0.69 to 0.15 % depending on its configuration.     

An exact algorithm for team orienteering problems

Optimising routing of vehicles constitutes a major logistic stake in many industrial contexts. We are interested here in the optimal resolution of special cases of vehicle routing problems, known as team orienteering problems. In these problems, vehicles are guided by a reward that can be collected from customers, while the length of routes is limited. The main difference with classical vehicle routing problems is that not all customers have to be visited. The solution method we propose here is based on a Branch & Price algorithm. It is, as far as we know, the first exact method proposed for such problems, except for a preliminary work from Gueguen (Methodes de résolution exacte pour problémes de tournées de véhicules. Thése de doctorat, école Centrale Paris 1999) and a work from Butt and Ryan (Comput Oper Res 26(4):427–441 1999). It permits to solve instances with up to 100 customers.      

Equitable Demand Adjustment for Infeasible Transportation Problems

We present a modelling/solution procedure for adjusting demands to obtain an ‘equitably infeasible’ solution for an infeasible transportation problem. The infeasibility may be due to total supply not being equal to total demand, or inadmissible routes (arcs). We show that the problem can be modelled and solved as a pre-emptive, multicriteria, capacitated transportation problem, whose objective is to minimize the maximum deviation between the fractional undersupply to the demand nodes or, equivalently, to minimize the fractional undersupply of the demands. Further, we show that an optimal solution can be obtained by solving either a single-criterion transportation problem (by choosing sufficiently large weights to aggregate the criteria) or a sequence of single-criterion transportation problems.     

Internal transport in automated semiconductor manufacturing systems

This paper summarizes the main results presented by the author in his PhD thesis (Montoya-Torres 2005), supervised by Stéphane Dauzèere-Pérés, Jean-Pierre Campagne and Hélène Marian, and defended on 29 November 2005 at the école des Mines de Saint-étienne and Université Jean-Monnet. The thesis is written in French and is available upon request from the author. This work deals with a real-life transportation problem in the semiconductor industry. It proposes a new approach by integrating tactical and operational decisions for the control of the automated transport system. At the tactical level, the problem is modeled using integer linear programming models inspired from Location Theory. At the operational level, the solution obtained from the tactical optimization is coupled with a discrete-event computer simulation program and some policies for transportation operations are implemented and compared.     

A two-stage model for a day-ahead paratransit planning problem

We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which requests to subcontract to taxis? We call this problem the day-ahead paratransit planning problem. The developed model is a non-standard two-stage integer recourse model. Both stages consist of two consecutive optimization problems: the clustering of requests into routes, and the assignment of these routes to vehicles. To solve this model, a genetic algorithm approach is used. Computational results are presented for randomly generated data sets.     

Optimizing the periodic pick-up of raw materials for a manufacturer of auto parts

We describe a solution procedure for a special case of the periodic vehicle routing problem (PVRP). Operation managers at an auto parts manufacturer in the north of Spain described the optimization problem to the authors. The manufacturer must pick up parts (raw material) from geographically dispersed locations. The parts are picked up periodically at scheduled times. The problem consists of assigning a pickup schedule to each of its supplier’s locations and also establishing daily routes in order to minimize total transportation costs. The time horizon under consideration may be as long as 90 days. The resulting PVRP is such that the critical decision is the assignment of locations to schedules, because once this is done, the daily routing of vehicles is relatively straightforward. Through extensive computational experiments, we show that the metaheuristic procedure described in this paper is capable of finding high-quality solutions within a reasonable amount of computer time. Our main contribution is the development of a procedure that is more effective at handling PVRP instances with long planning horizons when compared to those proposed in the literature.     

Vehicle routing problems with alternative paths: An application to on-demand transportation

The class of vehicle routing problems involves the optimization of freight or passenger transportation activities. These problems are generally treated via the representation of the road network as a weighted complete graph. Each arc of the graph represents the shortest route for a possible origin–destination connection. Several attributes can be defined for one arc (travel time, travel cost, etc.), but the shortest route modeled by this arc is computed according to a single criterion, generally travel time. Consequently, some alternative routes proposing a different compromise between the attributes of the arcs are discarded from the solution space. We propose to consider these alternative routes and to evaluate their impact on solution algorithms and solution values through a multigraph representation of the road network. We point out the difficulties brought by this representation for general vehicle routing problems, which drives us to introduce the so-called fixed sequence arc selection problem (FSASP). We propose a dynamic programming solution method for this problem. In the context of an on-demand transportation (ODT) problem, we then propose a simple insertion algorithm based on iterative FSASP solving and a branch-and-price exact method. Computational experiments on modified instances from the literature and on realistic data issued from an ODT system in the French Doubs Central area underline the cost savings brought by the proposed methods using the multigraph model.     

An intermodal multicommodity routing problem with scheduled services

We study a multicommodity routing problem faced by an intermodal service operator that uses ground and maritime transportation. Given a planning horizon, a?set of commodities to be picked up at their release times and to be delivered not later than their duedates, the problem is to decide on routes for these commodities using trucks and scheduled and capacitated maritime services at minimum cost of transportation and stocking at the seaports. Two mixed integer programming formulations and valid inequalities are proposed for this problem. The results of a computational study to evaluate the strength of the linear programming relaxations and the solution times are reported.     

A probabilistic proof of the fundamental theorem of algebra

We use Lévy's theorem on invariance of planar Brownian motion under conformal maps and the support theorem for Brownian motion to show that the range of a non-constant polynomial of a complex variable consists of the whole complex plane. In particular, we obtain a probabilistic proof of the fundamental theorem of algebra.

     

Variational inequalities and time-dependent traffic equilibria

We consider existence, characterization, and calculation of equilibria in transportation networks, when the route capacities and demand requirements depend on time. The problem is expressed in terms of a variational inequality and is situated in a Banach space setting. © Académie des Sciences/Elsevier, Paris     

Solving a vehicle routing problem by balancing the vehicles time utilization

Various vehicle routing problems (VRP) appear in the literature due to their important applications in the area of transportation and distribution.A VRP is characterized by the constraints that the involved factors must satisfy and by an optimality goal.In this paper, we develop a heuristic algorithm that

• (i)partitions suitably a distribution network into subnetworks. A single depot complies with every subnetwork, where a fleet of identical vehicles will start their itinerary. The nodes of the corresponding subnetwork are demand nodes that require a onetime visit by one only vehicle.
• (ii)Determine the routes of k vehicles, k=2,3,…, for every subnetwork so to minimize the visiting time of the corresponding demand nodes. Consequently the method computes the necessary vehicle number for each subnetwork so as to complete promptly the visiting requirement of all the demand nodes of the whole network. The main strategy of the algorithm for designing the vehicle routes consists of balancing the time utilization of the used vehicles. The paper is integrated by an application of the presented algorithm to the center of the city of Thessaloniki.

     

Bayesian sequential testing for Lévy processes with diffusion and jump components

We study the Bayesian problem of sequential testing of two simple hypotheses about the Lévy-Khintchine triplet of a Lévy process, having diffusion component, represented by a Brownian motion with drift, and jump component of finite variation. The method of proof consists of reducing the original optimal stopping problem to a free-boundary problem. We show it is characterized by a second order integro-differential equation, that the unknown value function solves on the continuation region, and by the smooth fit principle, which holds at the unknown boundary points. Several examples are presented.     

Maritime crude oil transportation - A split pickup and split delivery problem

The maritime oil tanker routing and scheduling problem is known to the literature since before 1950. In the presented problem, oil tankers transport crude oil from supply points to demand locations around the globe. The objective is to find ship routes, load sizes, as well as port arrival and departure times, in a way that minimizes transportation costs. We introduce a path flow model where paths are ship routes. Continuous variables distribute the cargo between the different routes. Multiple products are transported by a heterogeneous fleet of tankers. Pickup and delivery requirements are not paired to cargos beforehand and arbitrary split of amounts is allowed. Small realistic test instances can be solved with route pre-generation for this model. The results indicate possible simplifications and stimulate further research.     

Less-Than-Truckload carrier collaboration problem: modeling framework and solution approach

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city line-hauls served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads, there typically exist opportunities for carriers to collaborate over such routes. We introduce a two stage framework for LTL carrier collaboration. Our first stage involves collaboration between multiple carriers at the entrance to the city and can be formulated as a vehicle routing problem with time windows (VRPTW). We employ guided local search for solving this VRPTW. The second stage involves collaboration between carriers at transshipment facilities while executing their routes identified in phase one. For solving the second stage problem, we develop novel local search heuristics, one of which leverages integer programming to efficiently explore the union of neighborhoods defined by new problem-specific move operators. Our computational results indicate that integrating integer programming with local search results in at least an order of magnitude speed up in the second stage problem. We also perform sensitivity analysis to assess the benefits from collaboration. Our results indicate that distance savings of 7–15 % can be achieved by collaborating at the entrance to the city. Carriers involved in intra-city collaboration can further save 3–15 % in total distance traveled, and also reduce their overall route times.