Publications
N. Boland, M. Hewitt, L. Marshall, and M. Savelsbergh. “The Continuous Time Service Network Design Problem”, Operations Research, to appear.
G. Ozbaygin, O. Karasan, M. Savelsbergh, and H. Yaman. “A branchandprice algorithm for the vehicle routing problem with roaming deliveries”, Transportation Research Part B, to appear.
K. Lindsey, A. Erera, and M.W.P. Savelsbergh. “Improved Integer Programming Based Neighborhood Search for LTL Load Plan Design”. Transportation Science 50, 13601379, 2016.
E. Angelelli, I. Arsik, V. Morandi, M. Savelsbergh, and M. Speranza. “Proactive route guidance to avoid congestion”, Transportation Research Part B, 2016. Available online. http://dx.doi.org/10.1016/j.trb.2016.08.015
T. Kalinowski, R. Kapoor, and M. Savelsbergh. “Scheduling Reclaimers Serving a Stock Pad at a Coal Terminal”, J. of Scheduling, 2016. Available online. doi:10.1007/s1095101604958
M. Colombi, R. Mansini, and M. Savelsbergh. “The Generalized Independent Set Problem”, EJOR, 2016. Available online. http://dx.doi.org/10.1016/j.ejor.2016.11.050
C. Engel, T. Kalinowski, and M.W.P. Savelsbergh. “The Incremental Network Design Problem with Minimum Spanning Trees”, Journal of Graph Algorithms and Applications 21, 417432, 2017.
M. Stiglic, N. Agatz, M. Gradisar, and M. Savelsbergh. “Making Dynamic Ridesharing Work: The Impact of Driver and Rider Flexibility”, Transportation Research Part E 91, 190207, 2016.
C. Archetti, M.G. Speranza, and M.W.P. Savelsbergh “The Vehicle Routing Problem with Occasional Drivers”, EJOR, 2016. Available online. doi:10.1016/j.ejor.2016.03.049
Elin E. HalvorsenWeare and Martin W.P. Savelsbergh. “Biobjective Mixed Capacitated General Routing”, EJOR 251, 451465, 2016.
N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. “The LShape Search Method for Triobjective Integer Programming”, Mathematical Programming Computation, 2015. Available online. doi:10.1007/s1253201500933
M.W.P. Savelsbergh and K. Smilowitz. “Appointment Scheduling for Chronic Disease Management Programs”. IIE Transactions on Healthcare Systems Engineering, 2016. Available online. doi:10.1080/19488300.2016.1156200
M. Savelsbergh and T. van Woensel, “City Logistics: Challenges and Opportunities”, Transportation Science, 2016. Available online. http://dx.doi.org/10.1287/trsc.2016.0675
N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. “A New Method for Optimizing a Linear Function over the Efficient Set of a Multiobjective Integer Program”, EJOR, 2016. Available online. doi:10.1016/j.ejor.2016.02.037
N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. “The Quadrant Shrinking Method: A Simple and Efficient Algorithm for Solving Triobjective Integer Programs”, EJOR, 2016. Available online. doi:10.1016/j.ejor.2016.03.035
M. Talebian, C. Moffiet, and M.W.P. Savelsbergh. "A New Rail Access Charging Policy: Hunter Valley Coal Chain Case Study", Journal of Transportation Policy 46, 101108, 2016.
H. Charkhgard and M.W.P. Savelsbergh. "Efficient Algorithms for Travelling Salesman Problems Arising in Warehouse Order Picking". ANZIAM Journal 57, 166174, 2015.
M. Stiglic, N. Agatz, M. Gradisar, and M. Savelsbergh. "The Benefits of Meeting Points in Ridesharing Systems", Transportation Research Part B 82, 3653, 2015.
N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. "A Criterion Space Search Algorithm for Biobjective Mixed Integer Programming: The Triangle Search Method". INFORMS J. on Computing 27, 597618, 2015.
N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. "Criterion Space Search Algorithms for Biobjective Integer Programming: The Balanced Box Method". INFORMS J. on Computing 27, 735754, 2015.
N. Boland, M. Fischetti, M. Monaci, and M. Savelsbergh. "Proximity Benders: A Decomposition Heuristic for Stochastic Programs", Journal of Heuristics 22, 181198, 2016.
E. Angelelli, T. Kalinowski, R. Kapoor, and M. Savelsbergh. "A Reclaimer Scheduling Problem Arising in Coal Stockyard management", J. of Scheduling, 2015. Available online doi: 10.1007/s109510150436y
A. Lee and M.W.P. Savelsbergh. "Dynamic Ridesharing: Is there a Role for Dedicated Drivers." Transportation
Research Part B 81, 483497, 2015.
T. Keshavarz, M.W.P. Savelsbergh, and N. Salmasi. "A BranchandBound Algorithm for Single Machine
Sequencedependent Group Scheduling with Earliness and Tardiness Penalties." Applied Mathematical Modeling 39, 64106424, 2015.
A. Lee and M.W.P. Savelsbergh. "An Extended Demand Responsive Connector." European Journal of
Transportation Science and Logistics, 2014. Available online doi:10.1007/s1367601400606
G. Kuyzu, C.K. Akyol, O. Ergun, and M.Savelsbergh. "Bid Price Optimization for Truckload Carriers in Simultaneous Transportation Procurement Auctions", Transportation Research Part B 73, 3458, 2015.
T. Kalinowski, D. Matsypura, and M.W.P. Savelsbergh. "The Incremental Network Design Problem with Maximum Flows", EJOR 242, 5162, 2015.
M. Dall, C. Moffiet, M. Savelsbergh, and H. Waterer. "Possession Assessment and Capacity Evaluation of the Central Queensland Coal Network", European Journal of Transportation Science and Logistics 4, 139173, 2015.
N.L. Boland, M.W.P. Savelsbergh, and H. Waterer. "Shipping Data Generation for the Hunter Valley Coal Chain." Computers and Operations Research 53, 5467, 2015.
N.L. Boland, M. Talebian, and M.W.P. Savelsbergh. "Assortment and Pricing with Demand Learning." EJOR 237, 555565, 2014.
O. Smith and M.W.P. Savelsbergh. "A Note on Shortest Path Problems with Forbidden Paths". Networks 63, 239242, 2014.
A. Bennett, P. Griffin, M. Hewitt, and M.W.P. Savelsbergh. "The Value of Remote Monitoring Systems for Treatment of Chronic Disease." IIE Transactions on Healthcare Systems Engineering 4, 6579, 2014.
N.L. Boland, A.C. Eberhard, F.G. Engineer, M. Fischetti, M.W.P. Savelsbergh, A. Tsoukalas. "Boosting the Feasibility Pump." Math. Prog. Comp. 6, 255279, 2014.
M. Baxter, T. Elgindy, A.T. Ernst, T. Kalinowski, and M.W.P. Savelsbergh. "Incremental Network Design with Shortest Paths". EJOR 238, 675684, 2014.
M.W.P. Savelsbergh and O. Smith. "Cargo Assembly Planning". European Journal of Transportation Science and Logistics. Available online April 29, 2014.
A. Carbajal, A. Erera, and M.W.P. Savelsbergh. "Balancing Fleet Size and Repositioning Costs in LTL Trucking." Annals of Operations Research 203, 235254, 2013.
M. Hewitt, G.L. Nemhauser, M.W.P. Savelsbergh, and JH. Song. "BranchandPrice Guided Search for a Maritime Inventory Routing Problem." Computers and Operations Research 40, 14101419, 2013.
A. Erera, M. Hewitt, M.W.P. Savelsbergh, and Y. Zhang. "Creating schedules and computing operating costs." Computers and Operations Research 40, 691702, 2013.
M. Hewitt, G.L. Nemhauser and M.W.P. Savelsbergh. "'BranchandPrice Guided Search for Integer Programs with an Application to the Multicommodity Fixed Charge Network Flow Problem." INFORMS Journal on Computing 25, 302316, 2013.
A. Erera, K. Lindsey, and M. Savelsbergh. "A Pickup and Delivery Problem using Crossdocks and Truckload Lane Rates". European Journal of Transportation Science and Logistics 2, 527, 2013.
M. Guzelsoy, G.L. Nemhauser, and M. Savelsbergh. "RestrictandRelax Search for 01 Mixed Integer Programs". European Journal of Computational Optimization 1, 201218, 2013.
A. Erera, M. Hewitt, M.W.P. Savelsbergh, and Y. Zhang. "Improved Load Plan Design Through Integer Programming Based Local Search." Transportation Science 47, 412427, 2013.
O. Ö. Özener, Ö. Ergun, and M.W.P. Savelsbergh. "Allocating Cost of Service to Customers in Inventory Routing." Operations Research 61, 112125, 2013.
N. Agatz, A.M. Campbell, M. Fleischmann, J. van Nunen, and M.W.P. Savelsbergh. "Revenue Management Opportunities for Retailers." Journal of Revenue & Pricing Management 12, 128138, 2013.
R.Mansini, M.W.P. Savelsbergh, and B. Tochella. "The Supplier Selection Problem with Quantity Discounts and Truckload Shipping." OMEGA 40, 445455, 2012.
C. Archetti, A. Goel, and M.W.P. Savelsbergh. "Truck Driver Scheduling in Australia." Computers & Operations Research 39, 11221132, 2012.
F.G. Engineer, K. Furman, G.L. Nemhauser, M.W.P. Savelsbergh, J.H. Song. "A BranchPriceAndCut Algorithm for a Maritime Inventory Routing Problem." Operations Research 60, 106122, 2012.
D.J. Papageorgiou, A. Toriello, G.L. Nemhauser, M.W.P. Savelsbergh. "FixedCharge Transportation with Product Blending." Transportation Science 46, 281295, 2012.
F. Engineer, G.L. Nemhauser, M.W.P. Savelsbergh, and J.H. Song. "The Fixed Charge Shortest Path Problem." INFORMS Journal on Computing 24, 578596, 2012
Y. Li, G.L. Nemhauser, and M.W.P. Savelsbergh. "Pricing for Production and Delivery Flexibility in SingleItem LotSizing" Computer and Operations Research 39, 34083419, 2012.
N.L. Boland, D. Gulczynski, and M.W.P. Savelsbergh. "A Stockyard Planning Problem." EURO Journal on Transportation and Logistics 1, 197236, 2012.
N. Agatz, A. Erera, M.W.P. Savelsbergh, and X. Wang. "Optimization for Dynamic RideSharing: A Review." European Journal of Operations Research 222, 295303, 2012.
O. Ergun, O. Ozener, and M.W.P. Savelsbergh. "Lane Exchange Mechanisms for Truckload Carrier Collaboration." Transportation Science 45, 117, 2011.
N. Agatz, A.M. Campbell, M. Fleischmann, and M.W.P. Savelsbergh. "Time Slot Management in Attended Home Delivery." Transportation Science 45, 435449, 2011.
F. Engineer, G. Nemhauser, and M. Savelsbergh. "Dynamic Programming – Based Column Generation on TimeExpanded Networks: Application to the DialaFlight Problem." INFORMS Journal on Computing 23, 105119, 2011.
P. Vallotton, C. Sun, D. Lovell, M. Savelsbergh, M. Payne, and G. Muench. "Identifying weak linear features with the "Coalescing Shortest Path Image Transform"". Microscopy and Microanalysis 17, 911914, 2011.
N. Agatz, A. Erera, M.W.P. Savelsbergh, and X. Wang. "The Value of Optimization in Dynamic RideSharing: a Simulation Study in Metro Atlanta." Transportation Research Part B: Methodological 45, 14501464, 2011
V. Hemmelmayr, K.F Doerner, R.F Hartl, and M.W.P. Savelsbergh "Vendor Managed Inventory for Environments with Stochastic Product Usage." European Journal of Operations Research 202, 686695, 2010.
H. Hewitt, G.L. Nemhauser, and M.W.P. Savelsbergh. "Combining Exact and Heuristic Approaches for the Capacitated Fixed Charge Network Flow Problem." INFORMS Journal of Computing 22, 314325, 2010.
G. Keysan, G.L. Nemhauser, and M.W.P. Savelsbergh. "Tactical and Operational Planning of Scheduled Maintenance for PerSeat OnDemand Air Transportation." Transportation Science 44, 291306, 2010.
S. Ahmed, O. Gozbasi, M.W.P. Savelsbergh, T. Fox, I. Crocker, and E Schreibmann. "A FullyAutomated IntensityModulated Radiation Therapy." INFORMS Journal on Computing 22, 568583, 2010.
A. Erera, J. Morales, M.W.P. Savelsbergh. "The Vehicle Routing Problem with Stochastic Demand and Duration Constraints." Transportation Science 44, 474492, 2010.
A. Toriello, G.L. Nemhauser, and M.W.P. Savelsbergh. "Decomposing inventory routing problems with approximate value functions." Naval Research Logistics 57, 718727, 2010.
A. Erera, M. Hewitt, B. Karacik, and M.W.P. Savelsbergh. "Locating Drivers in a Trucking Terminal Network." Transportation Research Part E: Logistics and Transportation Review 45, 9881005, 2009.
Alan Erera, Martin Savelsbergh, and Emrah Uyar. "Fixed Routes with Backup Vehicles for Stochastic Vehicle Routing Problems with Time Constraints." Networks 54, 270283, 2009.
C. Archetti and M.W.P. Savelsbergh. "The Trip Scheduling Problem." Transportation Science 43, 417431, 2009.
F. KilincKarzan, G.L. Nemhauser, and M.W.P. Savelsbergh (2009).
InformationBased Branching Schemes for Binary Linear Mixed Integer Problems. Math. Prog. Comp. 1, 249293.
Branching variable selection can greatly affect the effectiveness and efficiency
of a branchandbound algorithm. Traditional approaches to branching variable selection
rely on estimating the effect of the candidate variables on the objective function.
We propose an approach which is empowered by exploiting the information contained in
a family of fathomed subproblems, collected beforehand from an incomplete branchandbound
tree. In particular, we use this information to define new branching rules that reduce
the risk of incurring inappropriate branchings. We provide computational results that
demonstrate the effectiveness of the new branching rules on various benchmark instances.
A. Erera, J. Morales, and M.W.P. Savelsbergh.
Robust Optimization
for Empty Repositioning Problems. Operations
Research 57, 468483, 2009.
This paper develops a robust optimization framework for dynamic
empty repositioning problems modeled using timeexpanded networks.
In such problems, uncertainty arises primarily from forecasts of
future supplies and demands for assets at different time epochs.
The approach developed in this paper models such uncertainty using
intervals about nominal forecast values and a single control
parameter $k$ limiting the systemwide absolute deviation from the
nominal forecast values (and thus determining the conservatism of
a reposition plan). A robust repositioning plan is defined as one
in which the typical flow balance constraints and flow bounds are
satisfied for the nominal forecast values and the plan is
recoverable under a limited set of recovery actions,
where a plan is recoverable when feasibility can be reestablished
for any uncertain outcome. We develop necessary and sufficient
conditions for flows to be robust under this definition for three
types of allowable recovery actions. When recovery actions allow
only flow changes on inventory arcs, we show that the resulting
problem is polynomiallysolvable. When recovery actions allow
limited reactive repositioning flows, we develop feasibility
conditions that are independent of the size of the uncertain
outcome space.
V. Schmid, K.F. Doerner, R.F. Hartl, M.W.P. Savelsbergh, and W. Stoecher (2009).
A Hybrid Solution Approach for ReadyMixed Concrete Delivery.
Transportation Science 43, 7085.
Companies in the concrete industry are facing the following
scheduling problem on a daily basis: concrete produced at several
plants has to be delivered at customers' construction sites using
a heterogeneous fleet of vehicles in a timely, but costeffective
manner. As the ordered quantity of concrete typically exceeds the
capacity of a single vehicle several deliveries need to be
scheduled in order to fulfill an order. The deliveries cannot
overlap and the time between consecutive deliveries has to be
small. Our solution approach effectively integrates optimization and
heuristic techniques. Information is passed back and forth between
an integer multicommodity flow optimization component and a
variable neighborhood search component in order to find
highquality solutions in a reasonable amount of time. Even
though both components are capable of producing feasible
solutions, the integrated approach is far more effective.
Computational experiments show that our hybrid method outperforms
a commercially available solution approach based on simulated
annealing by more than 20 percent on average.
V. Hemmelmayr, K.F. Doerner, R.F. Hartl, and Martin W. P. Savelsbergh (2009).
Delivery Strategies for Blood Products Supplies.
OR Spektrum 31, 707725.
We introduce a problem faced by an Austrian blood bank:
how to costeffectively organize the delivery of blood products to
Austrian hospitals. We investigate the potential value of switching
from the current vendee managed inventory set up to a vendor
managed inventory system. We present solution approaches based
on integer programming and variable neighborhood search and
evaluate their performance.
Fatma KilincKarzan, Alejandro Toriello, Shabbir Ahmed, George Nemhauser, and
Martin Savelsbergh (2009). Approximating the Stability
Region for Binary MixedInteger Programs. Operations Research Letters 37, 250254.
The stability region of a solution is the set of objective coefficients for which
the solution is optimal. We consider an optimization problem with some binary variables,
where the objective function is linear in these variables. We are interested in the
stability region in the space of coefficients of the binary variables. We develop polyhedral
inner and outer approximations of this stability region using only a number of
inequalities proportional to the number of binary variables. Furthermore, when a
new objective function is not in the stability region, we produce a list of good
solutions that can be used as a quick heuristic or as a warm start for future solves.
Alan Erera, Martin Savelsbergh, and Emrah Uyar (2009).
Fixed Routes with Backup Vehicles for Stochastic Vehicle Routing Problems with Time Constraints.
Networks 54, 270283.
We propose a practical and flexible fixed routing system that preserves
many of the benefits of traditional fixed routes but can be deployed in
settings with medium to high variability and delivery time window
constraints. The key ideas are the introduction of a new recourse
strategy, in which customers are assigned to \textit{two} planned
routes, a primary and a backup, and recourse decisions can move
customers to backup routes to regain feasibility or improve costs, and
the use of samplingbased techniques to handle the presence of delivery
time windows during the construction of primary and backup routes. A
computational study based on reallife data demonstrates the efficacy
of the proposed fixed routing system and the route construction
techniques.
A. Erera, M. Hewitt, B. Karacik, and M.W.P. Savelsbergh (2009).
Locating Drivers in a Trucking
Terminal Network. Transportation Research E: Logistics and
Transportation Review 45, 9881005.
We consider the problem of determining the home locations, or
domiciles, of truck drivers of a lessthantruckload carrier.
Domiciling decisions are complex, not in the least because of U.S.
DOT regulations and union rules restricting driver schedules, but
have a noticeable impact on the operating costs of
lessthantruckload carriers. We present an iterative scheme,
using driver dispatch technology in each iteration, to allocate
drivers to terminals and to determine drivers' bids so as to
satisfy union requirements. Computational experiments demonstrate
the value of the iterative scheme and quantify the impact of union
rules on the number of drivers required (and thus on carrier
operating costs).
A. Erera, B. Karacik, and M.W.P. Savelsbergh (2008).
A Dynamic Driver Management
Scheme for LessthanTruckload Carriers.
Computers and Operations Research 35, 22973411.
J.R. Hardin, G.L. Nemhauser, M.W.P. Savelsbergh (2008).
Strong valid inequalities for the
resourceconstrained scheduling problem with uniform resource
requirements. Discrete Optimization 5, 1935.
D. Espinoza, R. Garcia, M. Goycoolea, G.L. Nemhauser, and M.W.P. Savelsbergh (2008).
PerSeat, OnDemand Air Transportation
Part I: Problem Description and an Integer MultiCommodity Flow Model.
Transportation Science 42, 263278.
The availability of relatively cheap small jet planes has lead to
the creation of ondemand air transportation services in which
travelers call a few days in advance to schedule a flight. A
successful ondemand air transportation service requires an
effective scheduling system to construct minimum cost pilot and
jet itineraries for a set of accepted transportation requests. We
present an integer multicommodity network flow model with side
constraints for such dialaflight problems. We develop a variety
of techniques to control the size of the network and to strengthen
the quality of the linear programming relaxation, which allows the
solution of small to medium size instances. In Part II, we
describe how this core optimization technology is embedded in a
parallel, large neighborhood, local search scheme to produce
highquality solutions efficiently for largescale reallife
instances.
D. Espinoza, R. Garcia, M. Goycoolea, G.L. Nemhauser, and M.W.P. Savelsbergh (2008).
PerSeat, OnDemand Air Transportation
Part II: Parallel Local Search.
Transportation Science 42, 279291.
The availability of relatively cheap small jet aircrafts suggests
a new air transportation business: dialaflight, an ondemand
service in which travelers call a few days in advance to schedule
transportation. A successful ondemand air transportation service
requires an effective scheduling system to construct minimum cost
pilot and jet itineraries for a set of accepted transportation
requests. In Part I, we introduced an integer multicommodity
network flow model with side constraints for the dialaflight
problem and showed that small instances can be solved effectively.
Here, we demonstrate that highquality solutions for largescale
reallife instances can be produced efficiently by embedding the
core optimization technology in a local search scheme. To achieve
the desired level of performance, metrics were devised to select
neighborhoods intelligently, a variety of search diversification
techniques were included, and an asynchronous parallel
implementation was developed.
C. Archetti, M.W.P. Savelsbergh, and M.G. Speranza (2008).
To Split or Not To Split:
That is the Question. Transportation Research
E 44, 114123.
In distribution problems, a fleet of vehicles is available to
serve the demand of a set of customers. When the demand of each
customer is less than the vehicle capacity, each customer is
typically served by a single vehicle. However, more cost effective
distribution plans may exist if some of the customers are served
by more than one vehicle, that is, if some deliveries are split.
In this paper, we characterize distribution environments in which
allowing split deliveries is likely to be beneficial. The insights
resulting from this research will help companies understand the
potential benefits of allowing split deliveries based on the
characteristics of their customers, e.g., the customers' locations
and the customers' demand patterns.
C. Archetti, M.W.P. Savelsbergh, and M.G. Speranza (2008).
An OptimizationBased Heuristic for the
Split Delivery Vehicle Routiong Problem. Transportation Science 42, 2231.
The split delivery vehicle routing problem is
concerned with serving the demand of a set of customers with a
fleet of capacitated vehicles at minimum cost. A customer,
contrary to what is assumed in the classical vehicle routing
problem, can be served by more than one vehicle, if convenient. We
present a solution approach that integrates heuristic search with
optimization by using an integer program to explore promising
parts of the search space identified by a tabu search heuristic.
Computational results show that the method is able to improve the
solution of the tabu search in all but one instance of a large
test set.
M.W.P. Savelsbergh and J.H. Song (2008).
An Optimization Algorithm for the
Inventory Routing with Continuous Moves.
Computers and Operations Research 35, 22662282.
The typical inventory routing problem deals with the repeated
distribution of a single product from a single facility with an
unlimited supply to a set of customers that can all be reached
with outandback trips. Unfortunately, this is not always the
reality. We focus on the inventory routing problem with continuous
moves, which incorporates two important reallife complexities:
limited product availabilities at facilities and customers that
cannot be served using outandback tours. We need to design
delivery tours spanning several days, covering huge geographic
areas, and involving product pickups at different facilities. We
develop an integer programming based optimization algorithm
capable of solving small to medium size instances. This
optimization algorithm is embedded in local search procedure to
improve solutions produced by a randomized greedy heuristic. We
demonstrate the effectiveness of this approach in an extensive
computational study.
L. Bertazzi, M.W.P. Savelsbergh, and M.G. Speranza (2008).
Inventory Routing. In "The Vehicle Routing
Problem: Latest Advances and New Challenges", B.L. Golden, E. Wasil,
R. Raghavan (eds.), 4972.
N. Agatz, A.M. Campbell, M.W.P. Savelsbergh, and M. Fleischman (2008).
Challenges and Opportunities in Attended Home Delivery.
In "The Vehicle Routing
Problem: Latest Advances and New Challenges", B.L. Golden, E. Wasil,
R. Raghavan (eds.), 379396.
Ozlem Ergun, Orsan Ozener, Martin Savelsbergh (2008).
Collaboration for Truckload Carriers. To appear.
Because of historically high fuel prices, the trucking industry's
operating expenses are higher than ever, and thus profit margins lower
than ever. To cut costs, the trucking industry is searching for and
exploring new ideas. We investigate the potential of collaborative
opportunities in truckload transportation. When carriers serve
transportation requests from many shippers, they may be able to reduce
their repositioning costs by exchanging one or more of them. We develop
optimization models to determine the maximum benefit that can be
derived from collaborating, and we develop various exchange mechanisms,
differing in terms of information sharing requirements and side payment
options, which allow carriers to realize some or all of the costs
savings opportunities.
Mike Hewitt, George L. Nemhauser, Martin W.P. Savelsbergh (2008).
Combining Exact and Heuristic Approaches for the
Capacitated Fixed Charge Network Flow Problem. INFORMS J. on Computing. To appear.
We develop a solution approach for the fixed charge network flow
problem (FCNF) that produces provably highquality solutions
quickly. The solution approach combines mathematical
programming algorithms with heuristic search techniques. To obtain
highquality solutions it relies on neighborhood search with
neighborhoods that involve solving carefully chosen integer programs
derived from the arcbased formulation of FCNF. To obtain lower bounds,
the linear programming
relaxation of the pathbased formulation is used and strengthened with
cuts discovered during the neighborhood search. The solution approach
incorporates randomization to diversify the search and learning to
intensify the search. Computational experiments demonstrate the
efficacy of the proposed approach.
Shabbir Ahmed, Ozan Gozbasi, Martin Savelsbergh, Tim Fox, Ian Crocker,
and Eduard Schreibmann (2008). A FullyAutomated
IntensityModulated Radiation Therapy. To appear.
We designed and implemented a linear programming based IMRT treatment
plan generation technology that effectively and efficiently optimizes
beam geometry as well as beam intensities. The core of the technology
is a fluence map optimization model that approximates partial dose
volume constraints using conditional valueatrisk constraints.
Conditional valueatrisk constraints require careful tuning of their
parameters to achieve desirable plan quality and therefore we developed
an automated search strategy for parameter tuning. Another novel
feature of our fluence map optimization model is the use of virtual
critical structures to control coverage and conformity. Finally, beam
angle selection has been integrated with fluence map optimization. The
beam angle selection scheme employs a bicriteria weighting of beam
angle geometries and a selection mechanism to choose from among the set
of nondominated geometries. The technology is fully automated and
generates several highquality treatment plans satisfying dose
prescription requirements in a single invocation and without human
guidance. The technology has been tested on various real patient cases
with uniform success. Solution times are an order of magnitude faster
than what is possible with currently available commercial planning
systems and the quality of the generated treatment plans is at least as
good as the quality of the plans produced with existing technology.
Faramroze Engineer, George Nemhauser, and Martin Savelsbergh
(2008). Shortest Path Based Column
Generation on Large Networks with Many
Resource Constraints.
To appear.
We present a relaxationbased dynamic programming algorithm for
solving resource constrained shortest path problems arising in
column generation pricing problems for formulations involving
extremely large networks and a huge number of local resource
constraints. Such formulations typically occur when timeexpanded
networks are used to model a problem. The relaxationbased dynamic
programming algorithm alternates between a forward and a backward
search. Each search employs bounds derived in the previous search to
prune the search space. Between consecutive searches, the relaxation
is tightened using a set of critical resources and a set of critical
arcs over which these resources are consumed. As a result, a
relatively small state space is maintained and many paths can be
pruned while guaranteeing that an optimal path is ultimately found.
Niels Agatz, Ann Campbell, Moritz Fleischmann, Martin Savelsbergh
(2008). Time Slot Management in Attended Home Delivery.
Submitted.
Many etailers providing attended home delivery, especially
egrocers, offer narrow delivery time slots to ensure satisfactory
customer service. The choice of delivery time slots has to balance
marketing and operational considerations, which results in a complex
planning problem. We study the problem of selecting the set of time
slots to offer in each of the zip codes in a service region. The
selection needs to facilitate costeffective delivery routes, but also
needs to ensure an acceptable level of service to the customer. We
present two fullyautomated approaches that are capable of producing
highquality delivery time slot offerings in a reasonable amount of
time. Computational experiments reveal the value of these approaches
and the impact of the environment on the underlying tradeoffs.
C. Archetti and M.W.P. Savelsbergh (2007).
The Trip Scheduling Problem.
Transportation Science. To appear.
The hours of service regulations of the department of
transportation severely restrict the set of feasible driver
schedules. So much so that establishing whether a sequence of full
truckload transportation requests, each with a dispatch windows at
the origin, can feasibly be executed by a driver is no longer a
matter of simple forward simulation. In fact, it has been
conjectured that answering this question is NPcomplete. We
disprove this conjecture by providing a polynomial algorithm for
establishing whether a sequence of full truckload transportation
requests, each with a dispatch windows at the origin, can feasibly
be executed by a driver.
Ozlem Ergun, Gultekin Kuyzu, and M.W.P. Savelsbergh (2007).
Bid Price Optimization for Simultaneous
Truckload Transportation Procurement Auctions. Submitted.
We study simultaneous transportation procurement auctions from a
truckload carrier's perspective. We formulate a stochastic bid
price optimization problem with the objective of maximizing a
carrier's expected profit. The formulation takes into account the
synergies among the lanes and the competing carriers' bidding
strategies. For solving this stochastic optimization problem, we
develop an iterative coordinate search algorithm that finds good
solutions efficiently. The effectiveness of the developed
algorithm and the overall strategy is demonstrated through
computational experiments.
A. Bennett, P.M. Griffin, M. Gutierrez, M. Hewitt, M.W.P. Savelsbergh (2007).
The Promise and Value of Remote Monitoring Devices. Submitted.
Caring for patients with chronic illnesses is costly  over a
trillion dollars in 2006. These costs are likely to rise
significantly in the future as the number of people with chronic
illnesses is expected to increase dramatically. New technologies,
most prominently remote monitoring devices, are likely to change the
care environment for patients with chronic illnesses and will
alleviate some of the anticipated resource shortages and potentially
reduce the number of occurrences of serious, costly complications.
These technological advances may provide a more costeffective and
less laborintensive way to manage the care of patients with chronic
illnesses. We study the potential societal value of such remote
monitoring devices by developing a model that estimates the value of
providing remote monitoring devices to target populations. The model
is flexible and can easily accommodate different levels of detail,
in terms of disease categories and use options and benefit
characteristics of the devices.
E. Angelelli, M.W.P. Savelsbergh, and M.G. Speranza (2007).
Competitive Analysis for Dynamic
MultiPeriod Uncapacitated Routing Problems.
Networks 49, 308317.
We study a dynamic multiperiod routing problem where, at the
beginning of each time period, a set of orders arrive that have to
be fulfilled either that time period or the next. Thus, in each
time period there are customers which have to be served and
customers whose service may be postponed. Once it has been decided
which customers to serve, an optimal route is constructed and
executed. The objective of the problem is to minimize the average
distance traveled per time period. Deciding which customers to
serve in a time period is done on the basis of incomplete
information. No knowledge is available about customers requiring
service in future time periods. We introduce simple algorithms,
ones which naturally arise in practice, and analyze these
algorithms by studying their competitive ratio.
Ozlem Ergun, Gultekin Kuyzu, and M.W.P. Savelsbergh (2007).
Reducing Truckload Transportation Costs Through
Collaboration. Transportation Science 41, 206221.
Truckload transportation represents a significant portion of the
logistics market in the U.S. The key to a profitable truckload
transportation operation is high asset utilization. High asset
utilization is achieved by minimizing repositioning of trucks.
Reducing asset repositioning is difficult because asset
repositioning depends on interactions between the shippers served
by a carrier. We discuss optimization technology that can be used
to assist in the identification of repeatable, dedicated truckload
continuous move tours with little asset repositioning. Timing
considerations are critical to practical viability and are a key
focus of our efforts. We demonstrate the effectiveness of the
algorithms developed on various randomly generated instances as
well as on instances derived from data obtained from a strategic
sourcing consortium for a \$14 billion dollar sized US industry.
M.W.P. Savelsbergh and J.H. Song (2007).
Performance Measurement for Inventory Routing.
Transportation Science 41, 4454.
Ozlem Ergun, Gultekin Kuyzu, and M.W.P. Savelsbergh (2007).
Shipper Collaboration.
Computers & Operations Research 34, 15511560.
J. Hardin, G. Nemhauser, and M.W.P. Savelsbergh
(2007). Analysis of Bounds for
a Capacitated Singleitem LotSizing Problem.
Computers & Operations Research 34, 17211743.
M.W.P. Savelsbergh and J.H. Song (2007).
Inventory Routing with Continuous Moves.
Computers & Operations Research 34, 17441763.
A. Campbell and M.W.P. Savelsbergh (2006).
Incentive Schemes for Attended
Home Delivery Services. Transportation Science 40, 327341.
C. Archetti, M.W.P. Savelsbergh, and M.G. Speranza (2006).
WorstCase Analysis of Split
Delivery Routing Problems. Transportation Science 40, 226234.
F. Vanderbeck and M.W.P. Savelsbergh (2006).
A Generic View of DantzigWolfe Decomposition for Integer Programming.
Operations Research Letters 34, 296306.
The DantzigWolfe reformulation principle is presented based on
the concept of generating sets. The use of generating sets allows
for an easy extension to mixed integer programming. Moreover, it
provides a unifying framework for viewing various column
generation practices, such as relaxing or tightening the column
generation subproblem and introducing stabilization techniques.
We consider the resourceconstrained scheduling problem when each
job's resource requirements remain constant over its processing
time. We study a timeindexed formulation of the problem,
providing facetdefining inequalities for a projection of the
resulting polyhedron that exploit the resource limitations
inherent in the problem. Lifting procedures are then provided for
obtaining strong valid inequalities for the original polyhedron.
Computational results are presented to demonstrate the strength of
these inequalities.
E. Angelelli, M.W.P. Savelsbergh, and M.G. Speranza (2006).
Competitive Analysis of a Dispatch Policy
for a Dynamic MultiPeriod Routing Problems.
Operations Research Letters. To appear.
We analyze a simple and natural online algorithm (dispatch
policy) for a dynamic multiperiod uncapacitated routing problem,
in which at the beginning of each time period a set of orders
arrive that have to be served either in that time period or in the
next. The objective of the problem is to minimize the average
routing cost per time period. We show that the competitive ratio
of this online algorithm for instances with customers on the
nonnegative real line is 3/2.
This paper describes a scheme for the dynamic management of
linehaul drivers developed for the one of the largest U.S. LTL
carriers. Virtually all scheduling problems faced
by transportation service providers are complicated by
timeconstrained vehicle operators that can be renewed only after
resting. LTL driver scheduling is further complicated by the fact
that trucking moves, unlike passenger airline flights or train
dispatches, are not prescheduled. The technology developed in
this paper combines greedy search with enumeration of
timefeasible driver duties, and is capable of generating in a
matter of minutes costeffective driver schedules covering
15,00020,000 loads satisfying a variety of reallife driver
constraints. Computational results justify the algorithmic design
choices made in the development of the scheme, and a comparison
with realworld dispatch data indicates that
the technology produces driver schedules of very high quality.
I. Vis, R. de Koster, and M.W.P. Savelsbergh (2005).
Minimum Vehicle Fleet
Size Under Time Window Constraints at a
Container Terminal. Transportation Science 39, 249260.
M.W.P. Savelsbergh, R.N. Uma, and J. Wein (2005).
An Experimental Study of LPBased
Approximation Algorithms for Scheduling Problems.
INFORMS J. on Computing 17, 123136.
A. Erera, J. Morales, and M.W.P. Savelsbergh (2005).
Global Intermodel Tank Container Management
for the Chemical Industry. Transportation Research Part E:
The Logistics and Transportation Review 41, 551566.
The scale of the global chemical industry is enormous: in 2003,
the total value of global production exceeded US\$1.7 trillion.
International logistics is especially crucial to the highvalue
chemicals industry, since raw materials sources, production
facilities, and consumer markets are distributed globally.
Fluctuating demand, imbalanced trade flows, and expensive
transportation equipment necessitate dynamic asset management.
This paper focuses on asset management problems faced by tank
container operators, and formulates an operational tank container
management problem as a largescale multicommodity flow problem
on a timediscretized network. By integrating container routing
and repositioning decisions in a single model, total operating
costs and fleet sizes can be reduced. A computational study
verifies this hypothesis.
The typical inventory routing problem deals with the repeated
distribution of a single product from a single facility with an
unlimited supply to a set of customers that can all be reached
with outandback trips. Unfortunately, this is not always the
reality. We introduce the inventory routing problem with
continuous moves to study two important reallife complexities:
limited product availabilities at facilities and customers that
cannot be served using outandback tours. We need to design
delivery tours spanning several days, covering huge geographic
areas, and involving product pickups at different facilities. We
develop an innovative randomized greedy algorithm, which includes
linear programming based postprocessing technology, and we
demonstrate its effectiveness in an extensive computational study.
A. Campbell and M.W.P. Savelsbergh (2005).
Decision Support for
Consumer Direct Grocery Initiatives. Transportation Science 39, 313327.
Alper Atamturk and M.W.P. Savelsbergh (2005).
IntegerProgramming Software Systems.
Annals of Operations Research 140, 67124.
J.F. Cordeau, G. Laporte, J.Y. Potvin and M.W.P. Savelsbergh
(2004). Transportation on Demand. CRT200425.
A. Campbell and M.W.P. Savelsbergh (2004).
A Decomposition Approach for the
InventoryRouting Problem. Transportation Science 38, 488502.
A. Campbell and M.W.P. Savelsbergh (2004).
Efficient Insertion Heuristics for
Vehicle Routing and Scheduling Problems.
Transportation Science 38, 369378.
A. Campbell and M.W.P. Savelsbergh (2004).
Delivery Volume Optimization.
Transportation Science 38, 210223.
A. Kleywegt, V. Nori, and M.W.P. Savelsbergh (2004).
Dynamic Programming Approximations
for a Stochastic Inventory Routing Problem.
Transportation Science 38, 4270.
B. Hunsaker, A.J. Kleywegt, M.W.P. Savelsbergh, and C.A.
Tovey (2003). Optimal Online
Algorithms for Minimax Resource Scheduling.
SIAM J. on Discrete Mathematics 16, 555590.
A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh (2003).
A MultiItem Production
Planning Model with Setup Times: Algorithms, Reformulations, and
Polyhedral Characterizations for a Special Case.
Mathematical Programming 95, 7190.
A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh
(2003). On the Polyhedral Structure of
a MultiItem Production Planning Model with Setup Times.
Mathematical Programming 94, 375405.
H. Waterer, E.L. Johnson, P. Nobili, and M.W.P. Savelsbergh (2002).
The Relation of Time Indexed Formulations
of Single Machine Scheduling Problems to the Node Packing Problem.
Mathematical Programming 93, 477494.
A. Kleywegt, V. Nori, M.W.P. Savelsbergh (2002).
The Stochastic Inventory Routing
Problem with Direct Deliveries. Transportation
Science 36, 94118.
B. Hunsaker and M.W.P. Savelsbergh (2002).
Efficient Feasibility
Testing for DialaRide Problems.
Operations Research Letters 30, 169173.
S.P.M. van Hoesel, A.M.C.A. Koster, R.L.M.J. van de Leensel,
and M.W.P. Savelsbergh (2002). Polyhedral
Results for the Edge Capacity Polytope.
Mathematical Programming 92, 335358.
P. Bauer, J. Linderoth, M.W.P. Savelsbergh (2002).
A Branch and Cut Approach to the Cardinality Constrained
Circuit Problem. Mathematical Programming 91, 307348.
A. Campbell, L. Clarke, M.W.P. Savelsbergh (2002).
Inventory Routing in Practice.
The Vehicle Routing Problem (P. Toth, D. Viego, Eds.),
SIAM monographs on discrete mathematics and applications,
309330.
C.C.B. Cavalcante, C. Carvalho de Sousa, M.W.P. Savelsbergh,
Y. Wang, and L.A. Wolsey (2001).
Scheduling Projects with Labour Constraints. Discrete
Applied Mathematics 112, 2752.
E. Lee, J. Linderoth and M.W.P. Savelsbergh (2001).
A Parallel, Linear Programming Based Heuristic for
Large Scale Set Partitioning Problems. INFORMS J. on
Computing 13, 119.
A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2001).
Valid Inequalities for Problems
with Additive Variable Upper Bounds. Mathematical
Programming 91, 145162.
G. Depuy, M.W.P. Savelsbergh, J. Ammons, L. McGinnis (2001).
An Integer Programming Heuristic for
Printed Circuit Card Assembly. Journal of Heuristics 7,
351369.
M.W.P. Savelsbergh. (2001).
BranchandPrice: Integer Programming with
Column Generation. Encyclopedia of Optimization (C. Floudas,
P. Pardalos, Eds.).
A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh
(2000). Solving MultiItem Capacitated
Lotsizing Problems with Setup Times by BranchandCut.
Report TLI0004, Georgia Institute of Technology.
A. Atamturk, E.L. Johnson, J. T. Linderoth, M.W.P. Savelsbergh (2000).
ARMOS: A Relational Modeling System. Operations
Research 48, 846857.
A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh (2000).
On the Capacitated LotSizing and Continuous
01 Knapsack Polyhedra. European Journal of Operations
Research 125, 298315.
A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2000).
The Mixed Vertex Packing Problem.
Mathematical Programming 89, 3553.
A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2000).
Conflict Graphs in Integer Programming.
European Journal of Operations Research 121, 4055.
A. Campbell, J. Goentzel, M.W.P. Savelsbergh (2000).
Experiences with using SupplyChain
Software in the Classroom. Production and Operations
Management 9, 6680.
E.L. Johnson, G.L. Nemhauser, M.W.P. Savelsbergh (2000).
Progress in Linear Programming
Based BranchandBound Algorithms: An Exposition.
INFORMS Journal on Computing 12.
Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (2000).
Sequence Independent Lifting.
Journal of Combinatorial Optimization 4, 109129.
I.F.A. Vis, R. de Koster, and M.W.P. Savelsbergh (2000).
Estimation of the Number of Transport Vehicles at a
Container Terminal. In Progress in Material Handling
Research: 2000. Material Handling Institute, Charlotte,
North Carolina.
A. Kleywegt, V. Nori, M.W.P. Savelsbergh, C. Tovey (1999).
Online Resource Minimization.
Proceedings of the 10th Annual ACMSIAM Symposium on Discrete
Algorithms, 1999.
M. Desrochers, C.V. Jones, J.K. Lenstra, M.W.P. Savelsbergh,
and L. Stougie (1999). Towards a
Model and Algorithm Management System for Vehicle Routing
and Scheduling Problems. Decision Support Systems 25,
109133.
Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999).
Cover Inequalities for 01 Linear
Programs: Complexity. INFORMS J. on Computing 11,
117123.
J. Linderoth, M.W.P. Savelsbergh (1999).
A Computational Study of Search Strategies for Mixed
Integer Programming.
INFORMS J. on Computing 11, 173187.
Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999).
Lifted Flow Covers for Mixed 01 Integer
Programs. Mathematical Programming 85, 439468.
J.M. van den Akker, C.P.M. van Hoesel, M.W.P. Savelsbergh
(1999). A Polyhedral Approach to
SingleMachine Scheduling Problems. Mathematical
Programming 85, 541572.
M.W.P. Savelsbergh, R.N. Uma, J. Wein (1998).
An Experimental Study of LPBased Approximation Algorithms
for Scheduling Problems. Proceedings of the 9th
Annual ACMSIAM Symposium on Discrete Algorithm.
R.E. Bixby, S. Ceria, C.M. McZeal, M.W.P. Savelsbergh (1998).
An Updated Mixed Integer Programming Library:
MIPLIB 3.0. Optima 54, 1215.
J.M. van den Akker, C.A.J. Hurkens, M.W.P. Savelsbergh (1998).
TimeIndexed Formulations for Machine
Scheduling Problems: Column Generation.
INFORMS J. on Computing 12.
M.W.P. Savelsbergh and M. Sol (1998). DRIVE:
Dynamic Routing of Independent VEhicles. Operations
Research 46, 474490.
C. Barnhart, E.L. Johnson, G.L. Nemhauser, M.W.P. Savelsbergh,
P.H. Vance (1998). BranchandPrice:
Column Generation for Huge Integer Programs. Operations
Research 46, 316329.
Gu, G.L., Nemhauser, and M.W.P. Savelsbergh (1998).
Cover Inequalities for 01 Linear
Programs: Computation. INFORMS Journal on Computing
10, 427437.
A. Campbell, L. Clarke, A. Kleywegt, M.W.P. Savelsbergh (1997).
The Inventory Routing Problem. T. Crainic
and G. Laporte (eds.).
Fleet Management and Logistics. Kluwer Academic Publishers,
95113.
D.P. Clements, J.M. Crawford, D.E. Joslin, G.L. Nemhauser,
M.E. Puttlitz, M.W.P. Savelsbergh (1997).
Heuristic Optimization: A Hybrid AI/OR approach.
Proceedings of CP97: Constraintdirected Scheduling.
M.W.P. Savelsbergh (1997). A BranchandPrice
Algorithm for the Generalized Assignment Problem.
Operations Research 45, 831841.
G.A.P. Kindervater, M.W.P. Savelsbergh (1997). Vehicle
Routing: Handling Edge Exchanges. E.H.L. Aarts,
J.K. Lenstra (eds.). Local Search in Combinatorial Optimization,
Wiley, Chichester, 337360.
A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (1996).
A Combined Lagrangian, Linear Programming,
and Implication Heuristic for Largescale Set Partitioning
Problem. Journal of Heuristics 1, 247259.
M. Wennink, M.W.P. Savelsbergh (1996). Towards
a Planning Board Generator. Decision Support Systems
17, 199226.
M.W.P. Savelsbergh, M. Sol (1995). The
General Pickup and Delivery Problem. Transportation
Science 29, 1729.
M.W.P. Savelsbergh, M. Goetschalckx (1995).
A Comparison of the Efficiency of Fixed Versus Variable
Vehicle Routes. Journal of Business Logistics 16,
163188.
G.L. Nemhauser, M.W.P. Savelsbergh, G.S. Sigismondi
(1994). MINTO, a Mixed INTeger
Optimizer. Oper. Res. Letters 15, 4758.
M.W.P. Savelsbergh (1994). Preprocessing
and Probing for Mixed Integer Programming Problems.
ORSA J. on Computing 6, 445454.
G.A.P. Kindervater, J.K. Lenstra, M.W.P. Savelsbergh
(1993). Sequential and Parallel Local Search for the
Timeconstrained Traveling Salesman P. Discrete
Applied Mathematics 42, 211225.
M. Makowski, M.W.P. Savelsbergh (1993). MPDIT Mathematical
Programming Data Interchange Tool. COAL Bulletin 22,
718.
R. Perelaer, J.K. Lenstra, M.W.P. Savelsbergh, F. Soumis (1993).
The Bus Driver Scheduling Problem of the Amsterdam Transit
Company. COSORMemorandum 9326, Eindhoven University
of Technology.
M.W.P. Savelsbergh (1992). Computer Aided Routing. CWI
Tract 75, Centre for Mathematics and Computer Science
(CWI), Amsterdam.
M.W.P. Savelsbergh (1992). The Vehicle Routing Problem
with Time Windows: Minimizing Route Duration. ORSA Journal
on Computing 4, 146154.
G.L. Nemhauser, M.W.P. Savelsbergh (1992). A
cutting plane algorithm for the single machine scheduling
problem with release times. M. Akgul, H. Hamacher,
S. Tufecki (eds.). Combinatorial Optimization: New Frontiers
in the Theory and Practice, NATO ASI Series F: Computer
and Systems Sciences 82, SpringerVerlag, 6384.
G.L. Nemhauser, M.W.P. Savelsbergh, G.S. Sigismondi
(1992). Constraint Classification
for Mixed Integer Programming Formulations. COAL
Bulletin 20, 812.
C.P.M. van Hoesel, C.A.J. Hurkens, J.K. Lenstra, M.W.P.
Savelsbergh (1992). Produktieplanning in de praktijk
en theorie (in dutch). L. Fortuin, M. Zijlstra, H. Besselaar,
K. de Groot (eds.). De Kroonjuwelen, CQM Monograph 4,
Centre for Quantitative Methods, 201212.
M.W.P. Savelsbergh (1992). An
OSL based version of MINTO. EKKNEWS, International
Business Machines Corp.
M.W.P. Savelsbergh, G.S. Sigismondi, G.L. Nemhauser
(1991). Functional description of
MINTO, a Mixed INTeger Optimizer. Report COC9103,
Georgia Institute of Technology.
M. Desrochers, J.K. Lenstra, M.W.P. Savelsbergh (1990).
A classification scheme for vehicle routing and scheduling
problems. European J. Oper. Res. 46, 322332.
M.W.P. Savelsbergh (1990). A parallel insertion heuristic
for vehicle routing with side constraints. Statistica
Neerlandica 44, 139147.
M.W.P. Savelsbergh (1990). An efficient implementation
of local search for constrained routing problems. European
J. Oper. Res. 47, 7585.
J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1989).
De bochtige weg van idee naar systeem (in dutch). L.
Fortuin, P. van Beek, J. Wessels (eds.). Op de snede
tussen theorie en praktijk, Technische Universiteit
Eindhoven, 153164.
G.A.P. Kindervater, J.K. Lenstra, M.W.P. Savelsbergh
(1989). Parallel local search for the timeconstrained
traveling salesman problem. J.K. Lenstra, H.C. Tijms,
A. Volgenant (eds.). Twentyfive Years of Operations
Research in the Netherlands: Papers Dedicated to Gijs
de Leve, CWI Tract 70, Centre for Mathematics and Computer
Science (CWI), Amsterdam, 6175.
J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1989).
Behind the screen: DSS from an OR point of view. Decision
Support Systems, 413419.
M. Desrochers, J.K. Lenstra, M.W.P. Savelsbergh, F.
Soumis (1988). Vehicle routing with time windows: optimization
and approximation. B.L. Golden, A.A. Assad (eds.). Vehicle
Routing: Methods and Studies, NorthHolland, Amsterdam,
6584.
J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1987).
Functional description of CAR, an interactive system
for `computer aided routing'. Report OSR8716, Centre
for Mathematics and Computer Science.
J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1987).
Functionele beschrijving van CAR, een interactief
systeem voor `Computer Aided Routing'. Note OSN8701,
Centre for Mathematics and Computer Science.
M.W.P. Savelsbergh (1986). Local search for routing
problems with time windows. Ann. of Oper. Res. 4, 285305.
M.W.P. Savelsbergh, A. Volgenant (1985). Edge exchanges
in the degreeconstrained minimum spanning tree problem.
Comput. Oper. Res. 12, 341348.
M.W.P. Savelsbergh (1985). Interactieve methoden voor
de routering van voertuigen (in dutch). INFORMATIE 12,
10381042.
M.W.P. Savelsbergh (1985). Interactieve methoden voor
de routering van voertuigen (in dutch). Ontwikkelingen
rond CAD/CAM, Proceedings of CAPE '85, Samsom, Alphen
aan de Rijn, 808815.
M.W.P. Savelsbergh, P. van Emde Boas (1984). BOUNDED
TILING, an alternative to SATISFIABILITY? G. Wechsung
(ed.). Frege Conference 1984, AkademieVerlag, Berlin,
354363.
M.W.P. Savelsbergh (1984). Vehicle routing and computer
graphics. Note OSN8402, Centre for Mathematics and
Computer Science.
