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This paper presents a constraint logic programming model for the traveling salesman problem with time windows which yields an exact branch-and-bound optimization algorithm without any restrictive assumption on the time windows. Unlike dynamic programming approaches whose performance relies heavily on the degree of discretization applied to the data, our algorithm does not suffer from such space-complexity issues. The data-driven mechanism at its core more fully exploits pruning rules developed in operations research by using them not only a priori but also dynamically during the search. Computational results are reported and comparisons are made with both exact and heuristic algorithms. On Solomon's well-known test bed, our algorithm is instrumental in achieving new best solutions for some of the problems in set RC2 and strengthens the presumption of optimality for the best known solutions to the problems in set C2.

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This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads.

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This paper describes a tabu search heuristic for the vehicle routing problem with time windows. The tabu search is based on specialized local search heuristics that maintain the feasibility of the solution at all time. Computational results on a standard set of test problems are reported, as well as comparisons with other heuristics.

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This article describes a tabu search heuristic for the vehicle routing problem with backhauls and time windows. In this problem, the set of customers is partitioned into two subsets: linehaul customers where a given quantity of goods is delivered from a central depot, and backhaul customers where a given quantity of goods is collected and transported to the depot. Mixed routes with both linehaul and backhaul customers are constructed. A variant of this problem is examined where linehauls must precede bachhauls on each route. Furthermore, the time of beginning of service at each customer location must occur within a particular time interval. Computational results are reported on a standard set of test problems, and comparisons are made with other heuristic approaches and with optimal solutions.