Richard T. Wong

Network Design and Transportation Planning: Models and Algorithms

Numerous transportation applications as diverse as capital investment decision-making, vehicle fleet planning, and traffic light signal setting all involve some form of (discrete choice) network design. In this paper, we review some of the uses and limitations of integer programming-based approaches to network design, and describe several discrete and continuous choice models and algorithms. Our objectives are threefold---to provide a unifying view for synthesizing many network design models, to propose a unifying framework for deriving many network design algorithms, and to summarize computational experience in solving design problems. We also show that many of the most celebrated combinatorial problems that arise in transportation planning are specializations and variations of a generic design model. Consequently, the network design concepts described in this paper have great potential application in a wide range of problem settings.

Accelerating Benders Decomposition: Algorithmic Enhancement and Model Selection Criteria

This paper proposes methodology for improving the performance of Benders decomposition when applied to mixed integer programs. It introduces a new technique for accelerating the convergence of the algorithm and theory for distinguishing “good” model formulations of a problem that has distinct but equivalent mixed integer programming representations. The acceleration technique is based upon selecting judiciously from the alternate optima of the Benders subproblem to generate strong or pareto-optimal cuts. This methodology also applies to a much broader class of optimization algorithms that includes Dantzig-Wolfe decomposition for linear and nonlinear programs and related “cutting plane” type algorithms that arise in resource directive and price decomposition. When specialized to network location problems, this cut generation technique leads to very efficient algorithms that exploit the underlying structure of these models. In discussing the “proper” formulation of mixed integer programs, we suggest criteria for comparing various mixed integer formulations of a problem and for choosing formulations that can provide stronger cuts for Benders decomposition. From this discussion intimate connections between the previously disparate viewpoints of strong Benders cuts and tight linear programming relaxations of integer programs emerge.

A dual ascent approach for steiner tree problems on a directed graph

The Steiner tree problem on a directed graph (STDG) is to find a directed subtree that connects a root node to every node in a designated node setV. We give a dual ascent procedure for obtaining lower bounds to the optimal solution value. The ascent information is also used in a heuristic procedure for obtaining feasible solutions to the STDG. Computational results indicate that the two procedures are very effective in solving a class of STDGs containing up to 60 nodes and 240 directed/120 undirected arcs.The directed spanning tree and uncapacitated plant location problems are special cases of the STDG. Using these relationships, we show that our ascent procedure can be viewed as a generalization ofboth the Chu-Liu-Edmonds directed spanning tree algorithm and the Bilde-Krarup-Erlenkotter ascent algorithm for the plant location problem. The former comparison yields a dual ascent interpretation of the steps of the directed spanning tree algorithm.

A Dual-Ascent Procedure for Large-Scale Uncapacitated Network Design

The fixed-charge network design problem arises in a variety of problem contexts including transportation, communication, and production scheduling. We develop a family of dual-ascent algorithms for this problem. This approach generalizes known ascent procedures for solving shortest path, plant location, Steiner network and directed spanning tree problems. Our computational results for several classes of test problems with up to 500 integer and 1.98 million continuous variables and constraints show that the dual-ascent procedure and an associated drop-add heuristic generate solutions that, in almost all cases, are guaranteed to be within 1 to 4% of optimality. Moreover, the procedure requires no more than 150 seconds on an IBM 3083 computer. The test problems correspond to dense and sparse networks, including some models that arise in freight transport.

Aggregation and Disaggregation Techniques and Methodology in Optimization

A fundamental issue in the use of optimization models is the tradeoff between the level of detail and the ease of using and solving the model. Aggregation and disaggregation techniques have proven to be valuable tools for manipulating data and determining the appropriate policies to employ for this tradeoff. Furthermore, aggregation and disaggregation techniques offer promise for solving large-scale optimization models, supply a set of promising methodologies for studying the underlying structure of both univariate and multivariate data sets, and provide a set of tools for manipulating data for different levels of decision makers. In this paper, we develop a general framework for aggregation and disaggregation methodology, survey previous work regarding aggregation and disaggregation techniques for optimization problems, illuminate the appropriate role of aggregation and disaggregation methodology for optimization applications, and propose future research directions.

An Integrated Inventory Allocation and Vehicle Routing Problem

We address the problem of distributing a limited amount of inventory among customers using a fleet of vehicles so as to maximize profit. Both the inventory allocation and the vehicle routing problems are important logistical decisions. In many practical situations, these two decisions are closely interrelated, and therefore, require a systematic approach to take into account both activities jointly. We formulate the integrated problem as a mixed integer program and develop a Lagrangian-based procedure to generate both good upper bounds and heuristic solutions. Computational results show that the procedure is able to generate solutions with small gaps between the upper and lower bounds for a wide range of cost structures.

Models for planning capacity expansion in local access telecommunication networks

The rapid progress of communications technology has created new opportunities for modeling and optimizing the design of local telecommunication systems. The complexity, diversity, and continuous evolution of these networks pose several modeling challenges. In this paper, we present an overview of the local telephone network environment, and discuss possible modeling approaches. In particular, we (i) discuss the engineering characteristics of the network, and introduce terminology that is commonly used in the communications industry and literature; (ii) describe a general local access network planning model and framework, and motivate different possible modeling assumptions; (iii) summarize various existing planning models in the context of this framework; and (iv) describe some new modeling approaches. The discussion in this paper is directed both to researchers interested in modeling local telecommunications systems and to planners interested in using such models. Our goal is to present relevant aspects of the engineering environment for local access telecommunication networks, and to discuss the relationship between engineering issues and the formulation of economic decision models. We indicate how changes in the underlying switching and transmission technology affect the modeling of the local telephone network. We also review various planning issues and discuss possible optimization approaches for treating them.

A Decomposition Algorithm for Local Access Telecommunications Network Expansion Planning

Growing demand, increasing diversity of services, and advances in transmission and switching technologies are prompting telecommunication companies to rapidly expand and modernize their networks. This paper develops and tests a decomposition methodology to generate cost-effective expansion plans, with performance guarantees, for one major component of the network hierarchy-the local access network. The model captures economies of scale in facility costs and tradeoffs between installing concentrators and expanding cables to accommodate demand growth. Our solution method exploits the special tree and routing structure of the expansion planning problem to incorporate valid inequalities, obtained by studying the problems polyhedral structure, in a dynamic program which solves an uncapacitated version of the problem. Computational results for three realistic test networks demonstrate that our enhanced dynamic programming algorithm, when embedded in a Lagrangian relaxation scheme with problem preprocessing and local improvement, is very effective in generating good upper and lower bounds: Implemented on a personal computer, the method generates solutions within 1.2-7.0% of optimality. In addition to developing a successful solution methodology for a practical problem, this paper illustrates the possibility of effectively combining decomposition methods and polyhedral approaches.

Integrated Facility Location and Vehicle Routing Models: Recent Work and Future Prospects

SYNOPTIC ABSTRACTFacility location and vehicle routing are two widely used and studied management science resource planning models. According to the 1986 Statistical Abstract of the United States, freight transportation outlays for local trucking totaled 80.9 billion dollars in 1983. Thus, location/routing decisions have considerable economic importance in domains such as distribution systems planning. Since location and routing decisions are closely related, integrated models that consider the two aspects simultaneously offer the promise of more effective and economical decisions. However, such integrated models are complex and their design poses challenges in combining the short-term operational considerations of vehicle routing with the medium/long-term strategic issues of facility location. This paper discusses recent research related to various modeling approaches for location/routing problems, including comprehensive mathematical programming formulations, analytical approximations, and modified faci...

A network model for the rotating workforce scheduling problem

The rotating workforce scheduling problem involves the construction of an efficient sequence of work and rest periods spanning over a number of weeks. This schedule must satisfy the workforce requirements during the different shifts of each day and conform to all the other conditions imposed on the work/rest periods and their sequence. We consider the modeling of the rotating workforce scheduling problem as a network flow problem. All the constraints on the problem are incorporated in the network itself, except for the staff-covering constraints that are treated as side constraints. The optimal solution to the problem corresponds to a path in the network and is identified using a dual-based approach. The model deals with the issues of rest-period identification, work/rest period sequencing, and shift scheduling simultaneously and is designed to handle multiple shift cases with time-varying demands. The procedure, which is capable of solving large-scale problems, is applied to three well-known problems in rotating workforce scheduling. The computational results presented indicate that this procedure provides a useful method for solving large-scale complex problems in workforce scheduling.