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### Publications

N. Boland, M. Hewitt, L. Marshall, and M. Savelsbergh. “The Continuous Time Service Network Design Problem”, Operations Research, to appear.

G. Guastaroba, M.W.P. Savelsbergh, and M.G. Speranza. “Adaptive Kernel Search: A Heuristic for Solving Mixed Integer Linear Programs”, EJOR, to appear.

G. Ozbaygin, O. Karasan, M. Savelsbergh, and H. Yaman. “A branch-and-price algorithm for the vehicle routing problem with roaming deliveries”, Transportation Research Part B: Methodology 100, 115-137, 2017.

D. Reyes, M. Savelsbergh, and A. Toriello. “Vehicle Routing with Roaming Delivery Locations", Transportation Research Part C 80, 71-91, 2017.

K. Lindsey, A. Erera, and M.W.P. Savelsbergh. “Improved Integer Programming Based Neighborhood Search for LTL Load Plan Design”. Transportation Science 50, 1360-1379, 2016.

E. Angelelli, I. Arsik, V. Morandi, M. Savelsbergh, and M. Speranza. “Proactive route guidance to avoid congestion”, Transportation Research Part B: Methodology 94, 1-21, 2016.

T. Kalinowski, R. Kapoor, and M. Savelsbergh. “Scheduling Reclaimers Serving a Stock Pad at a Coal Terminal”, J. of Scheduling 20, 85-101, 2017.

M. Colombi, R. Mansini, and M. Savelsbergh. “The Generalized Independent Set Problem”, EJOR 260, 41-55, 2017.

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, 417-432, 2017.

M. Stiglic, N. Agatz, M. Gradisar, and M. Savelsbergh. “Making Dynamic Ride-sharing Work: The Impact of Driver and Rider Flexibility”, Transportation Research Part E 91, 190-207, 2016.

C. Archetti, M.G. Speranza, and M.W.P. Savelsbergh “The Vehicle Routing Problem with Occasional Drivers”, EJOR 254, 472-480, 2016.

Elin E. Halvorsen-Weare and Martin W.P. Savelsbergh. “Bi-objective Mixed Capacitated General Routing”, EJOR 251, 451-465, 2016.

N.L. Boland, H. Charkhgard, and M.W.P. Savelsbergh. “The L-Shape Search Method for Triobjective Integer Programming”, Mathematical Programming Computation 8, 217-251, 2015.

M.W.P. Savelsbergh and K. Smilowitz. “Appointment Scheduling for Chronic Disease Management Programs”. IIE Transactions on Healthcare Systems Engineering 6, 5-87, 2016.

M. Savelsbergh and T. van Woensel, “City Logistics: Challenges and Opportunities”, Transportation Science 50, 579-590, 2016.

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 260, 904-991, 2017.

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 260, 873-885, 2017.

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, 101-108, 2016.

H. Charkhgard and M.W.P. Savelsbergh. "Efficient Algorithms for Travelling Salesman Problems Arising in Warehouse Order Picking". ANZIAM Journal 57, 166-174, 2015.

M. Stiglic, N. Agatz, M. Gradisar, and M. Savelsbergh. "The Benefits of Meeting Points in Ride-sharing Systems", Transportation Research Part B 82, 36-53, 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, 597-618, 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, 735-754, 2015.

N. Boland, M. Fischetti, M. Monaci, and M. Savelsbergh. "Proximity Benders: A Decomposition Heuristic for Stochastic Programs", Journal of Heuristics 22, 181-198, 2016.

E. Angelelli, T. Kalinowski, R. Kapoor, and M. Savelsbergh. "A Reclaimer Scheduling Problem Arising in Coal Stockyard management", J. of Scheduling 19, 563-582, 2016.

A. Lee and M.W.P. Savelsbergh. "Dynamic Ridesharing: Is there a Role for Dedicated Drivers." Transportation Research Part B 81, 483-497, 2015.

T. Keshavarz, M.W.P. Savelsbergh, and N. Salmasi. "A Branch-and-Bound Algorithm for Single Machine Sequence-dependent Group Scheduling with Earliness and Tardiness Penalties." Applied Mathematical Modeling 39, 6410-6424, 2015.

A. Lee and M.W.P. Savelsbergh. "An Extended Demand Responsive Connector." European Journal of Transportation Science and Logistics 6, 25-50, 2017.

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, 34-58, 2015.

T. Kalinowski, D. Matsypura, and M.W.P. Savelsbergh. "The Incremental Network Design Problem with Maximum Flows", EJOR 242, 51-62, 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, 139-173, 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, 54-67, 2015.

N.L. Boland, M. Talebian, and M.W.P. Savelsbergh. "Assortment and Pricing with Demand Learning." EJOR 237, 555-565, 2014.

O. Smith and M.W.P. Savelsbergh. "A Note on Shortest Path Problems with Forbidden Paths". Networks 63, 239-242, 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, 65-79, 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, 255-279, 2014.

M. Baxter, T. Elgindy, A.T. Ernst, T. Kalinowski, and M.W.P. Savelsbergh. "Incremental Network Design with Shortest Paths". EJOR 238, 675-684, 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, 235-254, 2013.

M. Hewitt, G.L. Nemhauser, M.W.P. Savelsbergh, and J-H. Song. "Branch-and-Price Guided Search for a Maritime Inventory Routing Problem." Computers and Operations Research 40, 1410-1419, 2013.

A. Erera, M. Hewitt, M.W.P. Savelsbergh, and Y. Zhang. "Creating schedules and computing operating costs." Computers and Operations Research 40, 691-702, 2013.

M. Hewitt, G.L. Nemhauser and M.W.P. Savelsbergh. "'Branch-and-Price Guided Search for Integer Programs with an Application to the Multicommodity Fixed Charge Network Flow Problem." INFORMS Journal on Computing 25, 302-316, 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, 5-27, 2013.

M. Guzelsoy, G.L. Nemhauser, and M. Savelsbergh. "Restrict-and-Relax Search for 0-1 Mixed Integer Programs". European Journal of Computational Optimization 1, 201-218, 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, 412-427, 2013.

O. Ö. Özener, Ö. Ergun, and M.W.P. Savelsbergh. "Allocating Cost of Service to Customers in Inventory Routing." Operations Research 61, 112-125, 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, 128-138, 2013.

R.Mansini, M.W.P. Savelsbergh, and B. Tochella. "The Supplier Selection Problem with Quantity Discounts and Truckload Shipping." OMEGA 40, 445-455, 2012.

C. Archetti, A. Goel, and M.W.P. Savelsbergh. "Truck Driver Scheduling in Australia." Computers & Operations Research 39, 1122-1132, 2012.

F.G. Engineer, K. Furman, G.L. Nemhauser, M.W.P. Savelsbergh, J.-H. Song. "A Branch-Price-And-Cut Algorithm for a Maritime Inventory Routing Problem." Operations Research 60, 106-122, 2012.

D.J. Papageorgiou, A. Toriello, G.L. Nemhauser, M.W.P. Savelsbergh. "Fixed-Charge Transportation with Product Blending." Transportation Science 46, 281-295, 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, 578-596, 2012

Y. Li, G.L. Nemhauser, and M.W.P. Savelsbergh. "Pricing for Production and Delivery Flexibility in Single-Item Lot-Sizing" Computer and Operations Research 39, 3408-3419, 2012.

N.L. Boland, D. Gulczynski, and M.W.P. Savelsbergh. "A Stockyard Planning Problem." EURO Journal on Transportation and Logistics 1, 197-236, 2012.

N. Agatz, A. Erera, M.W.P. Savelsbergh, and X. Wang. "Optimization for Dynamic Ride-Sharing: A Review." European Journal of Operations Research 222, 295-303, 2012.

O. Ergun, O. Ozener, and M.W.P. Savelsbergh. "Lane Exchange Mechanisms for Truckload Carrier Collaboration." Transportation Science 45, 1-17, 2011.

N. Agatz, A.M. Campbell, M. Fleischmann, and M.W.P. Savelsbergh. "Time Slot Management in Attended Home Delivery." Transportation Science 45, 435-449, 2011.

F. Engineer, G. Nemhauser, and M. Savelsbergh. "Dynamic Programming – Based Column Generation on Time-Expanded Networks: Application to the Dial-a-Flight Problem." INFORMS Journal on Computing 23, 105-119, 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, 911-914, 2011.

N. Agatz, A. Erera, M.W.P. Savelsbergh, and X. Wang. "The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta." Transportation Research Part B: Methodological 45, 1450-1464, 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, 686-695, 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, 314-325, 2010.

G. Keysan, G.L. Nemhauser, and M.W.P. Savelsbergh. "Tactical and Operational Planning of Scheduled Maintenance for Per-Seat On-Demand Air Transportation." Transportation Science 44, 291-306, 2010.

S. Ahmed, O. Gozbasi, M.W.P. Savelsbergh, T. Fox, I. Crocker, and E Schreibmann. "A Fully-Automated Intensity-Modulated Radiation Therapy." INFORMS Journal on Computing 22, 568-583, 2010.

A. Erera, J. Morales, M.W.P. Savelsbergh. "The Vehicle Routing Problem with Stochastic Demand and Duration Constraints." Transportation Science 44, 474-492, 2010.

A. Toriello, G.L. Nemhauser, and M.W.P. Savelsbergh. "Decomposing inventory routing problems with approximate value functions." Naval Research Logistics 57, 718-727, 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, 988-1005, 2009.

Alan Erera, Martin Savelsbergh, and Emrah Uyar. "Fixed Routes with Backup Vehicles for Stochastic Vehicle Routing Problems with Time Constraints." Networks 54, 270-283, 2009.

C. Archetti and M.W.P. Savelsbergh. "The Trip Scheduling Problem." Transportation Science 43, 417-431, 2009.

F. Kilinc-Karzan, G.L. Nemhauser, and M.W.P. Savelsbergh (2009). Information-Based Branching Schemes for Binary Linear Mixed Integer Problems. Math. Prog. Comp. 1, 249-293.

Branching variable selection can greatly affect the effectiveness and efficiency of a branch-and-bound 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 branch-and-bound 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, 468-483, 2009.

This paper develops a robust optimization framework for dynamic empty repositioning problems modeled using time-expanded 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 system-wide 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 polynomially-solvable. 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 Ready-Mixed Concrete Delivery. Transportation Science 43, 70-85.

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 cost-effective 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 multi-commodity flow optimization component and a variable neighborhood search component in order to find high-quality 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, 707-725.

We introduce a problem faced by an Austrian blood bank: how to cost-effectively 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 Kilinc-Karzan, Alejandro Toriello, Shabbir Ahmed, George Nemhauser, and Martin Savelsbergh (2009). Approximating the Stability Region for Binary Mixed-Integer Programs. Operations Research Letters 37, 250-254.

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, 270-283.

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 sampling-based techniques to handle the presence of delivery time windows during the construction of primary and backup routes. A computational study based on real-life 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, 988-1005.

We consider the problem of determining the home locations, or domiciles, of truck drivers of a less-than-truckload 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 less-than-truckload 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 Less-than-Truckload Carriers. Computers and Operations Research 35, 2297-3411.

J.R. Hardin, G.L. Nemhauser, M.W.P. Savelsbergh (2008). Strong valid inequalities for the resource-constrained scheduling problem with uniform resource requirements. Discrete Optimization 5, 19-35.

D. Espinoza, R. Garcia, M. Goycoolea, G.L. Nemhauser, and M.W.P. Savelsbergh (2008). Per-Seat, On-Demand Air Transportation Part I: Problem Description and an Integer Multi-Commodity Flow Model. Transportation Science 42, 263-278.

The availability of relatively cheap small jet planes has lead to the creation of on-demand air transportation services in which travelers call a few days in advance to schedule a flight. A successful on-demand 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 multi-commodity network flow model with side constraints for such dial-a-flight 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 high-quality solutions efficiently for large-scale real-life instances.

D. Espinoza, R. Garcia, M. Goycoolea, G.L. Nemhauser, and M.W.P. Savelsbergh (2008). Per-Seat, On-Demand Air Transportation Part II: Parallel Local Search. Transportation Science 42, 279-291.

The availability of relatively cheap small jet aircrafts suggests a new air transportation business: dial-a-flight, an on-demand service in which travelers call a few days in advance to schedule transportation. A successful on-demand 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 multi-commodity network flow model with side constraints for the dial-a-flight problem and showed that small instances can be solved effectively. Here, we demonstrate that high-quality solutions for large-scale real-life 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, 114-123.

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 Optimization-Based Heuristic for the Split Delivery Vehicle Routiong Problem. Transportation Science 42, 22-31.

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, 2266-2282.

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 out-and-back trips. Unfortunately, this is not always the reality. We focus on the inventory routing problem with continuous moves, which incorporates two important real-life complexities: limited product availabilities at facilities and customers that cannot be served using out-and-back 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.), 49-72.

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.), 379-396.

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 high-quality solutions quickly. The solution approach combines mathematical programming algorithms with heuristic search techniques. To obtain high-quality solutions it relies on neighborhood search with neighborhoods that involve solving carefully chosen integer programs derived from the arc-based formulation of FCNF. To obtain lower bounds, the linear programming relaxation of the path-based 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 Fully-Automated Intensity-Modulated 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 value-at-risk constraints. Conditional value-at-risk 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 bi-criteria weighting of beam angle geometries and a selection mechanism to choose from among the set of non-dominated geometries. The technology is fully automated and generates several high-quality 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 relaxation-based 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 time-expanded networks are used to model a problem. The relaxation-based 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 e-tailers providing attended home delivery, especially e-grocers, 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 cost-effective delivery routes, but also needs to ensure an acceptable level of service to the customer. We present two fully-automated approaches that are capable of producing high-quality 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 trade-offs.

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 NP-complete. 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 cost-effective and less labor-intensive 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 Multi-Period Uncapacitated Routing Problems. Networks 49, 308-317.

We study a dynamic multi-period 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, 206-221.

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, 44-54. Ozlem Ergun, Gultekin Kuyzu, and M.W.P. Savelsbergh (2007). Shipper Collaboration. Computers & Operations Research 34, 1551-1560. J. Hardin, G. Nemhauser, and M.W.P. Savelsbergh (2007). Analysis of Bounds for a Capacitated Single-item Lot-Sizing Problem. Computers & Operations Research 34, 1721-1743. M.W.P. Savelsbergh and J.-H. Song (2007). Inventory Routing with Continuous Moves. Computers & Operations Research 34, 1744-1763. A. Campbell and M.W.P. Savelsbergh (2006). Incentive Schemes for Attended Home Delivery Services. Transportation Science 40, 327-341. C. Archetti, M.W.P. Savelsbergh, and M.G. Speranza (2006). Worst-Case Analysis of Split Delivery Routing Problems. Transportation Science 40, 226-234. F. Vanderbeck and M.W.P. Savelsbergh (2006). A Generic View of Dantzig-Wolfe Decomposition for Integer Programming. Operations Research Letters 34, 296-306. The Dantzig-Wolfe 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 resource-constrained scheduling problem when each job's resource requirements remain constant over its processing time. We study a time-indexed formulation of the problem, providing facet-defining 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 Multi-Period Routing Problems. Operations Research Letters. To appear. We analyze a simple and natural on-line algorithm (dispatch policy) for a dynamic multi-period 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 on-line algorithm for instances with customers on the non-negative 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 time-constrained 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 pre-scheduled. The technology developed in this paper combines greedy search with enumeration of time-feasible driver duties, and is capable of generating in a matter of minutes cost-effective driver schedules covering 15,000-20,000 loads satisfying a variety of real-life driver constraints. Computational results justify the algorithmic design choices made in the development of the scheme, and a comparison with real-world 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, 249-260. M.W.P. Savelsbergh, R.N. Uma, and J. Wein (2005). An Experimental Study of LP-Based Approximation Algorithms for Scheduling Problems. INFORMS J. on Computing 17, 123-136. 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, 551-566. 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 high-value 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 large-scale multi-commodity flow problem on a time-discretized 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 out-and-back trips. Unfortunately, this is not always the reality. We introduce the inventory routing problem with continuous moves to study two important real-life complexities: limited product availabilities at facilities and customers that cannot be served using out-and-back 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, 313-327.

Alper Atamturk and M.W.P. Savelsbergh (2005). Integer-Programming Software Systems. Annals of Operations Research 140, 67-124.

J.-F. Cordeau, G. Laporte, J.-Y. Potvin and M.W.P. Savelsbergh (2004). Transportation on Demand. CRT-2004-25.

A. Campbell and M.W.P. Savelsbergh (2004). A Decomposition Approach for the Inventory-Routing Problem. Transportation Science 38, 488-502.

A. Campbell and M.W.P. Savelsbergh (2004). Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems. Transportation Science 38, 369-378.

A. Campbell and M.W.P. Savelsbergh (2004). Delivery Volume Optimization. Transportation Science 38, 210-223.

A. Kleywegt, V. Nori, and M.W.P. Savelsbergh (2004). Dynamic Programming Approximations for a Stochastic Inventory Routing Problem. Transportation Science 38, 42-70.

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, 555-590.

A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh (2003). A Multi-Item Production Planning Model with Setup Times: Algorithms, Reformulations, and Polyhedral Characterizations for a Special Case. Mathematical Programming 95, 71-90.

A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh (2003). On the Polyhedral Structure of a Multi-Item Production Planning Model with Setup Times. Mathematical Programming 94, 375-405.

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, 477-494.

A. Kleywegt, V. Nori, M.W.P. Savelsbergh (2002). The Stochastic Inventory Routing Problem with Direct Deliveries. Transportation Science 36, 94-118.

B. Hunsaker and M.W.P. Savelsbergh (2002). Efficient Feasibility Testing for Dial-a-Ride Problems. Operations Research Letters 30, 169-173.

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, 335-358.

P. Bauer, J. Linderoth, M.W.P. Savelsbergh (2002). A Branch and Cut Approach to the Cardinality Constrained Circuit Problem. Mathematical Programming 91, 307-348.

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, 309-330.

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, 27-52.

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, 1-19.

A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2001). Valid Inequalities for Problems with Additive Variable Upper Bounds. Mathematical Programming 91, 145-162.

G. Depuy, M.W.P. Savelsbergh, J. Ammons, L. McGinnis (2001). An Integer Programming Heuristic for Printed Circuit Card Assembly. Journal of Heuristics 7, 351-369.

M.W.P. Savelsbergh. (2001). Branch-and-Price: 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 Multi-Item Capacitated Lot-sizing Problems with Setup Times by Branch-and-Cut. Report TLI-00-04, 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, 846-857.

A.J. Miller, G.L. Nemhauser, and M.W.P. Savelsbergh (2000). On the Capacitated Lot-Sizing and Continuous 0-1 Knapsack Polyhedra. European Journal of Operations Research 125, 298-315.

A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2000). The Mixed Vertex Packing Problem. Mathematical Programming 89, 35-53.

A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2000). Conflict Graphs in Integer Programming. European Journal of Operations Research 121, 40-55.

A. Campbell, J. Goentzel, M.W.P. Savelsbergh (2000). Experiences with using Supply-Chain Software in the Classroom. Production and Operations Management 9, 66-80.

E.L. Johnson, G.L. Nemhauser, M.W.P. Savelsbergh (2000). Progress in Linear Programming Based Branch-and-Bound 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, 109-129.

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 ACM-SIAM 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, 109-133.

Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999). Cover Inequalities for 0-1 Linear Programs: Complexity. INFORMS J. on Computing 11, 117-123.

J. Linderoth, M.W.P. Savelsbergh (1999). A Computational Study of Search Strategies for Mixed Integer Programming. INFORMS J. on Computing 11, 173-187.

Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999). Lifted Flow Covers for Mixed 0-1 Integer Programs. Mathematical Programming 85, 439-468.

J.M. van den Akker, C.P.M. van Hoesel, M.W.P. Savelsbergh (1999). A Polyhedral Approach to Single-Machine Scheduling Problems. Mathematical Programming 85, 541-572.

M.W.P. Savelsbergh, R.N. Uma, J. Wein (1998). An Experimental Study of LP-Based Approximation Algorithms for Scheduling Problems. Proceedings of the 9th Annual ACM-SIAM 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, 12-15.

J.M. van den Akker, C.A.J. Hurkens, M.W.P. Savelsbergh (1998). Time-Indexed 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, 474-490.

C. Barnhart, E.L. Johnson, G.L. Nemhauser, M.W.P. Savelsbergh, P.H. Vance (1998). Branch-and-Price: Column Generation for Huge Integer Programs. Operations Research 46, 316-329.

Gu, G.L., Nemhauser, and M.W.P. Savelsbergh (1998). Cover Inequalities for 0-1 Linear Programs: Computation. INFORMS Journal on Computing 10, 427-437.

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, 95-113.

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: Constraint-directed Scheduling.

M.W.P. Savelsbergh (1997). A Branch-and-Price Algorithm for the Generalized Assignment Problem. Operations Research 45, 831-841.

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, 337-360.

A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (1996). A Combined Lagrangian, Linear Programming, and Implication Heuristic for Large-scale Set Partitioning Problem. Journal of Heuristics 1, 247-259.

M. Wennink, M.W.P. Savelsbergh (1996). Towards a Planning Board Generator. Decision Support Systems 17, 199-226.

M.W.P. Savelsbergh, M. Sol (1995). The General Pickup and Delivery Problem. Transportation Science 29, 17-29.

M.W.P. Savelsbergh, M. Goetschalckx (1995). A Comparison of the Efficiency of Fixed Versus Variable Vehicle Routes. Journal of Business Logistics 16, 163-188.

G.L. Nemhauser, M.W.P. Savelsbergh, G.S. Sigismondi (1994). MINTO, a Mixed INTeger Optimizer. Oper. Res. Letters 15, 47-58.

M.W.P. Savelsbergh (1994). Preprocessing and Probing for Mixed Integer Programming Problems. ORSA J. on Computing 6, 445-454.

G.A.P. Kindervater, J.K. Lenstra, M.W.P. Savelsbergh (1993). Sequential and Parallel Local Search for the Time-constrained Traveling Salesman P. Discrete Applied Mathematics 42, 211-225.

M. Makowski, M.W.P. Savelsbergh (1993). MP-DIT Mathematical Programming Data Interchange Tool. COAL Bulletin 22, 7-18.

R. Perelaer, J.K. Lenstra, M.W.P. Savelsbergh, F. Soumis (1993). The Bus Driver Scheduling Problem of the Amsterdam Transit Company. COSOR-Memorandum 93-26, 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, 146-154.

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, Springer-Verlag, 63-84.

G.L. Nemhauser, M.W.P. Savelsbergh, G.S. Sigismondi (1992). Constraint Classification for Mixed Integer Programming Formulations. COAL Bulletin 20, 8-12.

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, 201-212.

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 COC-91-03, 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, 322-332.

M.W.P. Savelsbergh (1990). A parallel insertion heuristic for vehicle routing with side constraints. Statistica Neerlandica 44, 139-147.

M.W.P. Savelsbergh (1990). An efficient implementation of local search for constrained routing problems. European J. Oper. Res. 47, 75-85.

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, 153-164.

G.A.P. Kindervater, J.K. Lenstra, M.W.P. Savelsbergh (1989). Parallel local search for the time-constrained traveling salesman problem. J.K. Lenstra, H.C. Tijms, A. Volgenant (eds.). Twenty-five Years of Operations Research in the Netherlands: Papers Dedicated to Gijs de Leve, CWI Tract 70, Centre for Mathematics and Computer Science (CWI), Amsterdam, 61-75.

J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1989). Behind the screen: DSS from an OR point of view. Decision Support Systems, 413-419.

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, North-Holland, Amsterdam, 65-84.

J.M. Anthonisse, J.K. Lenstra, M.W.P. Savelsbergh (1987). Functional description of CAR, an interactive system for computer aided routing'. Report OS-R8716, 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 OS-N8701, Centre for Mathematics and Computer Science.

M.W.P. Savelsbergh (1986). Local search for routing problems with time windows. Ann. of Oper. Res. 4, 285-305.

M.W.P. Savelsbergh, A. Volgenant (1985). Edge exchanges in the degree-constrained minimum spanning tree problem. Comput. Oper. Res. 12, 341-348.

M.W.P. Savelsbergh (1985). Interactieve methoden voor de routering van voertuigen (in dutch). INFORMATIE 12, 1038-1042.

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, 808-815.

M.W.P. Savelsbergh, P. van Emde Boas (1984). BOUNDED TILING, an alternative to SATISFIABILITY? G. Wechsung (ed.). Frege Conference 1984, Akademie-Verlag, Berlin, 354-363.

M.W.P. Savelsbergh (1984). Vehicle routing and computer graphics. Note OS-N8402, Centre for Mathematics and Computer Science.

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