JournalTOCs

EURO Journal of Transportation and Logistics
Number of Followers: 15

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2192-4376 - ISSN (Online) 2192-4384
Published by Springer-Verlag

[2626 journals]

High-performance order processing in picking workstations
- Abstract: Abstract This paper investigates the order processing in picking workstations, which are among today’s most efficient order picking devices. A workstation is continuously supplied with storage bins containing stock keeping units (SKUs) from an interconnected storage system, so that a human worker only has to repack the items into customer bins. This way of unproductive work content, e.g., travel time, is eliminated and up to 1000 picks per hour is enabled. We aim at a concerted processing of picking orders and storage bins delivered from the storage system, such that an even more efficient picking process is enabled. We formalize the resulting order processing problem and present an efficient solution procedure. Our computational results show that the automated crane of the storage system feeding the picking workstation with bins can considerably be relieved by our procedure.
  PubDate: 2019-03-01
  DOI: 10.1007/s13676-017-0113-8
Strategic placement of telemetry units considering customer usage
correlation
- Abstract: Abstract Telemetry units relay customer inventory levels to the supplier. This information can be used to create good estimates of future delivery dates, which is useful for route planning. Incorporating customer usage rate correlation can help with better planning for customers without telemetry units, but who are correlated with ones that have telemetry. We study a variant of the Telemetry Location Problem that exploits the correlation in customer demand to choose which customers should have telemetry units to minimize routing costs while preserving a given customer service level for all customers. To the best of our knowledge, this problem has not been previously addressed in the literature. We present a model and new algorithm for this problem. We perform computational experiments and provide insights regarding when consideration of correlation is particularly useful.
  PubDate: 2019-03-01
  DOI: 10.1007/s13676-017-0104-9
Sterilization network design
- Abstract: Abstract With the increasing costs of health care, service providers are looking for innovative ways to cut cost without sacrificing high service quality. Resource pooling is a promising trend in different health care areas, and sterilization is no exception. Specialized medical sterilization centers (SCs) that offer reusable medical device (RMD) sterilization services for hospitals have the potential to cut cost and improve efficiency through better utilization of resources, risk-pooling and economies of scale. However, it is unclear whether the resulting cost savings can offset the additional transportation costs and the operational complications associated with a centralized system. We compare three schemes to organize RMD sterilization in a group of hospitals: fully distributed, centralized processing, and centralized processing and stock keeping. The sterilization network design problem is formulated as a mixed-integer concave minimization program (MISOCP) that considers economies of scale, service level requirements and variable demand, with the objective of minimizing capacity, transportation, and inventory holding costs. The mathematical model is reformulated as a mixed-integer second-order cone program with a piecewise-linear cost function so it can be solved efficiently. We also consider the case when the first two moments of the RMD demand distributions are not known with certainty but are rather estimated based on sample data. We show that, with proper selection of the uncertainty structure, the robust nonlinear optimization problem can be tractably reformulated as a MISOCP as well. Testing on a realistic case study under different scenarios reveals that significant cost savings can be achieved by consolidating sterilization services. Compared to the distributed scheme, we found that cost saving in the second scheme is attributed primarily to improved resource utilization and economies of scale. Risk-pooling in the last scheme results in an additional small cost saving that has to be weighed against other operational and legal considerations.
  PubDate: 2019-03-01
  DOI: 10.1007/s13676-018-0118-y
A Branch-and-Price algorithm for two multi-compartment vehicle routing
problems
- Abstract: Abstract Despite the vast body of literature on vehicle routing problems, little attention has been paid to multi-compartment vehicle routing problems that investigate transportation of different commodities on the same vehicle, but in different compartments. In this project, we present two strategically different versions of the MCVRP in general settings. In the first version, different commodities may be delivered to the customer by different vehicles, but the full amount of each product must be delivered by a single vehicle. In the second version, each customer may only be serviced by a single vehicle, which must deliver the full amount of all commodities demanded by that customer. We present a Branch-and-Price algorithm for solving the two versions of the problem to optimality and we analyze the effect of the strategic decision of whether or not to allow multiple visits to the same customer by comparing the optimal costs of the two versions. Computational results are presented for instances with up to 100 customers and the algorithm can solve instances with up to 50 customers and 4 commodities to optimality.
  PubDate: 2019-03-01
  DOI: 10.1007/s13676-016-0096-x
The opportunity cost of time window violations
- Authors: Matteo Salani; Maria Battarra
  Pages: 343 - 361
  Abstract: Abstract This paper studies a variant of the vehicle routing problem with soft time windows (VRPSTW), inspired by real-world distribution problems. In applications, violations of the prescribed delivery time are commonly accepted. Customers’ inconvenience due to early or late arrival is typically modelled as a penalty cost included in the VRPSTW objective function, added to the routing costs. However, weighting routing costs against customer inconvenience is not straightforward for practitioners. In our problem definition, practitioners evaluate solutions by comparison with the hard time windows solution. The desired routing cost saving is set by the practitioners as a percentage of the nominal solution’s routing costs. The objective function minimizes the time window violations, or the customer inconvenience, with respect to the nominal solution. This allows practitioners to quantify the opportunity cost (i.e. the customer inconvenience), when a target routing cost saving is imposed. To solve the problem, we apply two exact algorithms: the first is based on a standard branch-and-cut-and-price (BCP), the second is a BCP nested in a bisection algorithm. Computational results demonstrate that the second algorithm outperforms the standard implementation. Solutions obtained with the opportunity cost interpretation of soft time windows are then compared with solutions obtained using both hard time windows and the standard interpretation of soft time windows.
  PubDate: 2018-12-01
  DOI: 10.1007/s13676-018-0121-3
  Issue No: Vol. 7, No. 4 (2018)
The stochastic vehicle routing problem, a literature review, part I:
models
- Authors: Jorge Oyola; Halvard Arntzen; David L. Woodruff
  Pages: 193 - 221
  Abstract: Abstract Building on the work of Gendreau et al. (Eur J Oper Res 88(1):3–12; 1996), we review the past 20 years of scientific literature on stochastic vehicle routing problems. The numerous variants of the problem that have been studied in the literature are described and categorized.
  PubDate: 2018-09-01
  DOI: 10.1007/s13676-016-0100-5
  Issue No: Vol. 7, No. 3 (2018)
The vehicle routing problem with hard time windows and stochastic service
times
- Authors: F. Errico; G. Desaulniers; M. Gendreau; W. Rei; L.-M. Rousseau
  Pages: 223 - 251
  Abstract: Abstract In this paper we consider the vehicle routing problem with hard time windows and stochastic service times (VRPTW-ST); in this variant of the classic VRPTW the service times are random variables. In particular, given a set of vehicle routes, some of the actual service times might not lead to a feasible solution, given the customer time windows. We consider a chance-constrained program to model the VRPTW-ST and provide a new set partitioning formulation that includes a constraint on the minimum success probability of the set of vehicle routes. Under some mild conditions, we develop a method to exactly compute the success probability of the routes. We then solve the VRPTW-ST by a branch-price-and-cut algorithm, where the main challenges are in the solution of the subproblems of the column generation procedure. We adapt the dynamic programming algorithm to account for the probabilistic resource consumption by extending the label dimension and by providing new dominance rules. Extensive computational experiments prove the effectiveness of both the solution method and the stochastic model.
  PubDate: 2018-09-01
  DOI: 10.1007/s13676-016-0101-4
  Issue No: Vol. 7, No. 3 (2018)
A decomposition method for estimating recursive logit based route choice
models
- Authors: Tien Mai; Fabian Bastin; Emma Frejinger
  Pages: 253 - 275
  Abstract: Abstract Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks of more than 7000 and 40,000 links, and we show that the DeC method significantly reduces the estimation time. We also use the DeC method to estimate two mixed RL specifications, one using random coefficients and one incorporating error components associated with subnetworks (Frejinger and Bierlaire 2007). The models are estimated on a real network and a cross-validation study is performed. The results suggest that the mixed RL models can be estimated in a reasonable time with the DeC method. These models yield sensible parameter estimates and the in-sample and out-of sample fits are significantly better than the RL model.
  PubDate: 2018-09-01
  DOI: 10.1007/s13676-016-0102-3
  Issue No: Vol. 7, No. 3 (2018)
Scheduled service network design with resource acquisition and management
- Authors: Teodor Gabriel Crainic; Mike Hewitt; Michel Toulouse; Duc Minh Vu
  Pages: 277 - 309
  Abstract: Abstract We present a new planning model for freight consolidation carriers, one that links strategic, resource acquisition, and allocation decisions with tactical, service network design-related decisions. Specifically, such as service network design models that recognize resource constraints, the model selects services and routes both commodities and the resources needed to support the services that transport them. In addition, the model recognizes that resources can be grouped into types that differ from one another with respect to capabilities, e.g., speeds, capacities, scheduling rules, etc. Ultimately, along with recognizing resource constraints, the model also makes strategic decisions such as how many resources of each type should be acquired, to what terminal new resources should be assigned, and which existing terminal-based resources should be reassigned. As such, the model can be used from a strategic planning, resource acquisition, mixing, and allocation perspective as it provides an estimate of the impact of such decisions on transportation costs. We extend a matheuristic for a service network design problem with a fixed set (both in number and allocation) of resources of a single type to one that can also make these acquisition and allocation decisions for multiple types of resource. Then, with an extensive computational study, we demonstrate the efficacy of the matheuristic and benchmark its performance against both a leading commercial solver and a column generation-based heuristic. Finally, we perform an extensive computational study to understand how the resource-related and service network design-related components of the model interact, including how freight volumes and cost structures impact how many resources should beacquired.
  PubDate: 2018-09-01
  DOI: 10.1007/s13676-017-0103-x
  Issue No: Vol. 7, No. 3 (2018)
Advances in vehicle routing and logistics optimization: exact methods
- Authors: Michael Schneider; Timo Gschwind; Daniele Vigo
  PubDate: 2018-12-07
  DOI: 10.1007/s13676-018-0139-6
Special issue on: “Models and methods for the supply chain and
logistics management”
- Authors: Chefi Triki; Teodor Gabriel Crainic
  PubDate: 2018-11-20
  DOI: 10.1007/s13676-018-0137-8
Electric vehicle routing problem with time windows, recharging stations
and battery swapping stations
- Authors: Amit Verma
  Abstract: Abstract Due to new environmental measures and targets for reducing emissions, many logistics companies have started adopting electric vehicles in their fleet for cargo delivery. They are increasingly becoming popular for last mile deliveries. However, due to their limited range, they require frequent visits to recharging stations while delivering products to customers along their route. Long recharging times at available stations can have an adverse impact on route planning especially when short delivery time windows are considered. Battery swapping has the potential to reduce recharging times by replacing the vehicles’ batteries with fully charged batteries. However, each battery swap is expensive than traditional recharging methods. In this paper, we present a variant of the Electric Vehicle Routing Problem with Time Windows and Recharging Stations by allowing the available stations to serve both as Recharging Stations (RSs) and Battery Swapping Stations (BSSs). To the best of our knowledge, this problem has not been previously addressed in the literature. A model and algorithm for this problem are presented, computational experiments are performed and insights regarding when consideration of BSSs are particularly useful are provided.
  PubDate: 2018-11-15
  DOI: 10.1007/s13676-018-0136-9
Stochastic user equilibrium in the presence of state dependence
- Authors: Claudia Castaldi; Paolo Delle Site; Francesco Filippi
  Abstract: Abstract We consider the following two state-dependent effects at the level of route choice: inertia to change and, as a consequence of experience, lower perception variance for the currently used route. A heteroscedastic extreme value model embodying heterogeneity across alternatives in the mean of the random terms is used. Estimations based on stated preference data confirm the presence of both state-dependent effects. We introduce a new class of stochastic user equilibrium (SUE) models that take state-dependent effects into account. The class includes conventional SUE as special case. The equilibrium conditions are formulated as fixed-point states of deterministic day-to-day assignment processes. At the equilibrium (1) no user can improve her/his utility by unilaterally changing route, and (2) if each user shifts from her/his current route to her/his newly chosen route the observed route flows do not change. The existence of the equilibrium is guaranteed under usually satisfied conditions. A modified method of successive averages is proposed for solution. Examples related to a two arc network and to the Nguyen-Dupuis network illustrate the model.
  PubDate: 2018-10-05
  DOI: 10.1007/s13676-018-0135-x
Cost allocation under competition: a new rail access charging policy
- Authors: Martin Savelsbergh; Masoud Talebian
  Abstract: Abstract We consider a setting in which a rail infrastructure provider divides the track costs proportionally between the above-rail operators based on their usage. We study a proposed access charge regime aimed at incentivizing the operators to use longer train configurations. The regime sets a target length and gives a discount on an operator’s charge if it deploys a configuration of at least the target. That is, the operators may be able to reduce their access charges by deploying a longer train configuration. We analyze the policy, and conditions under which both operators have an incentive to deploy long train configurations.
  PubDate: 2018-09-22
  DOI: 10.1007/s13676-018-0133-z
A matheuristic for a multimodal long haul routing problem
- Authors: David Wolfinger; Fabien Tricoire; Karl F. Doerner
  Abstract: Abstract We address a planning problem faced by logistics service providers who transport freight over long distances. Given a set of transportation requests, where the origin and the destination of each request are located far apart from each other, a logistics service provider must find feasible vehicle routes to fulfil those requests at minimum cost. When transporting freight over long distances, multimodal transportation provides a viable alternative to traditional unimodal road transportation. We introduce this new problem, which we call the multimodal long haul routing problem (MMLHRP), and present a mathematical formulation for it. Furthermore, we propose a matheuristic, using iterated local search within a column generation framework, for solving the MMLHRP. Results show that large cost savings can be achieved through multimodal transportation compared to unimodal road transportation.
  PubDate: 2018-08-22
  DOI: 10.1007/s13676-018-0131-1
Unsupervised prototype reduction for data exploration and an application
to air traffic management initiatives
- Authors: Alexander Estes; David J. Lovell; Michael O. Ball
  Abstract: Abstract We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of “representative” elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the unsupervised prototype reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the unsupervised prototype reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.
  PubDate: 2018-08-13
  DOI: 10.1007/s13676-018-0132-0
A hybrid recourse policy for the vehicle routing problem with stochastic
demands
- Authors: Majid Salavati-Khoshghalb; Michel Gendreau; Ola Jabali; Walter Rei
  Abstract: Abstract In this paper, we propose a new recourse policy for the vehicle routing problem with stochastic demands (VRPSD). In this routing problem, customer demands are characterized by known probability distributions. The actual demand values are only revealed upon arriving at a customer location. The objective of the problem is to plan routes minimizing the travel cost and the expect recourse cost. The latter cost is a result of a predetermined recourse policy designed to handle route failures. Given the planned routes, such failures may occur in the event where a vehicle has insufficient capacity to serve the current customer or the next customer. In the relevant literature, there are three types of recourse policies: (i) classical, where failures at customers are handled by return trips to the depot, (ii) optimal restocking, where preventive restocking trips to the depot are performed based on optimized customer-specific thresholds, and failures are handled by return trips to the depot, and (iii) rule-based policies, where preventive restocking trips are performed based on thresholds established by preset rules, and failures are handled by performing return trips to the depot. While the first type is rather myopic, the last two types of recourse policies use simplistic comparisons of the vehicle’s residual capacity to trigger recourse actions. In this paper, we propose a more advanced rule-based recourse policy, which does not solely depend on the vehicle’s residual capacity. To do so, we first propose a taxonomy that groups rule-based policies into three classes, we then propose the first hybrid recourse policy, which simultaneously combines two of these classes, namely risk and distance. We develop an exact solution algorithm for the VRPSD with this hybrid recourse policy and conduct a broad range of computational experiments. The algorithm is able to solve instances with up to 60 customers, and for certain experimental configurations, the exact algorithm solves to optimality up to 79% of the instances. Finally, we show that when the optimal routes of the hybrid policy are operated under the optimal restocking policy they yield a marginal average cost difference.
  PubDate: 2018-07-09
  DOI: 10.1007/s13676-018-0126-y
Energy-efficient planning for supplying assembly lines with vehicles
- Authors: C. Briand; Y. He; S. U. Ngueveu
  Abstract: Abstract This paper focuses on minimizing the energy consumed by vehicles for parts supplying to workstations in mixed-model assembly lines. The parts are assumed to be packed in bins (or pallets). The bins are shipped from a so-called “supermarket” and delivered periodically to the workstations using tow trains with wagons. For each time period, depending on the production plan, a workstation has a specific demand of parts expressed as a portion of a bin. The problem is to define the sequence of workstations to be visited by the tow train at each time period, as well as the number of bins to be delivered at each stop, so as to avoid any material shortage. This paper provides an analysis of the computational complexity related to this problem and proposes two original mixed integer linear programming formulations. We also provide computational analysis and experiments that put the efficiency of one particular formulation into evidence and show the conditions under which energy-efficient supplying is not in contradiction with economic efficiency.
  PubDate: 2018-06-15
  DOI: 10.1007/s13676-018-0129-8
Reliable capacitated facility location problem with service levels
- Authors: José A. Santiváñez; Héctor J. Carlo
  Abstract: Abstract This study is concerned with a generalized version of the reliable capacitated facility location problem that guarantees a minimum level of service level in terms of satisfied demands under correlated facility disruptions with uncertain joint distribution. A linear mixed integer formulation for the generalized problem is presented and shown to correspond with the worst-case (distributionally robust) problem when the service-level function is submodular on the disruption scenarios. Managerial insights, including the effect of service-level parameters on optimal solutions, are presented based on experimental results. It is concluded that the proposed model may be used to design supply chain networks with the same expected total cost than the traditional reliable capacitated facility location problem, but with an improved total expected service level.
  PubDate: 2018-05-02
  DOI: 10.1007/s13676-018-0125-z
A variable neighborhood search heuristic for supply chain coordination
using dynamic price discounts
- Authors: Viktoryia Buhayenko; Sin C. Ho; Anders Thorstenson
  Abstract: Abstract This research studies the problem of supply chain coordination using temporary price discounts. The supplier decides how much discount should be introduced in each period to each of the customers, aiming to maximize its profit, while giving the customers the incentive to order in the desired periods. To solve this problem, a variable neighborhood search is introduced. The results of computational experiments indicate that the variable neighborhood search outperforms the mixed integer-based heuristic introduced earlier in the literature for this problem. Our metaheuristic procedure found the optimal solution for small instances in more than 80% of the cases. Moreover, an advantage of this metaheuristic is the significantly shorter computing time that allows applying it to larger instances.
  PubDate: 2018-04-26
  DOI: 10.1007/s13676-018-0122-2