Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times

This paper presents a variant of the asymmetric traveling salesman problem (ATSP) in which the traveling time between each pair of cities is represented by an interval of values (wherein the actual travel time is expected to lie) instead of a fixed (deterministic) value as in the classical ATSP. Here the ATSP (with interval objective) is formulated using the usual interval arithmetic. To solve the interval ATSP (I-ATSP), a genetic algorithm with interval valued fitness function is proposed. For this purpose, the existing revised definition of order relations between interval numbers for the case of pessimistic decision making is used. The proposed algorithm is based on a previously published work and includes some new features of the basic genetic operators. To analyze the performance and effectiveness of the proposed algorithm and different genetic operators, computational studies of the proposed algorithm on some randomly generated test problems are reported.     

A Branch & Cut Algorithm for the Asymmetric Traveling Salesman Problem with Precedence Constraints

In this article we consider a variant of the classical asymmetric traveling salesman problem (ATSP), namely the ATSP in which precedence constraints require that certain nodes must precede certain other nodes in any feasible directed tour. This problem occurs as a basic model in scheduling and routing and has a wide range of applications varying from helicopter routing (Timlin, Master's Thesis, Department of Combinatorics and Optimization, University of Waterloo, 1989), sequencing in flexible manufacturing (Ascheuer et al., Integer Programming and Combinatorial Optimization, University of Waterloo, Waterloo, 1990, pp. 19–28; Idem., SIAM Journal on Optimization, vol. 3, pp. 25–42, 1993), to stacker crane routing in an automatic storage system (Ascheuer, Ph.D. Thesis, Tech. Univ. Berlin, 1995). We give an integer programming model and summarize known classes of valid inequalities. We describe in detail the implementation of a branch&cut-algorithm and give computational results on real-world instances and benchmark problems from TSPLIB. The results we achieve indicate that our implementation outperforms other implementations found in the literature. Real world instances with more than 200 nodes can be solved to optimality within a few minutes of CPU-time. As a side product we obtain a branch&cut-algorithm for the ATSP. All instances in TSPLIB can be solved to optimality in a reasonable amount of computation time.     

Exponential irreducible neighborhoods for combinatorial optimization problems

This paper deals with irreducible augmentation vectors associated with three combinatorial optimization problems: the TSP, the ATSP, and the SOP. We study families of irreducible vectors of exponential size, derived from alternating cycles, where optimizing a linear function over each of these families can be done in polynomial time. A family of irreducible vectors induces an irreducible neighborhood; an algorithm for optimizing over this family is known as a local search heuristic. Irreducible neighborhoods do not only serve as a tool to improve feasible solutions, but do play an important role in an exact primal algorithm; such families are the primal counterpart of a families of facet inducing inequalities.     

A Queueing Framework for Routing Problems with Time-dependent Travel Times

Assigning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.      

Constant factor approximation for ATSP with two edge weights

We give a constant factor approximation algorithm for the Asymmetric Traveling Salesman Problem on shortest path metrics of directed graphs with two different edge weights. For the case of unit edge weights, the first constant factor approximation was given recently by Svensson. This was accomplished by introducing an easier problem called Local-Connectivity ATSP and showing that a good solution to this problem can be used to obtain a constant factor approximation for ATSP. In this paper, we solve Local-Connectivity ATSP for two different edge weights. The solution is based on a flow decomposition theorem for solutions of the Held–Karp relaxation, which may be of independent interest.     

Vehicle routing with stochastic time-dependent travel times

Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the traffic congestion is captured in an analytical way using queueing theory. The congestion is then applied to the VRP problem. In this paper, we introduce the variability in traffic flows into the model. This allows for an evaluation of the routes based on the uncertainty involved. Different experiments show that the risk taking behavior of the planner can be taken into account during optimization. As more weight is given to the variability component, the resulting optimal route will take a slightly longer travel time, but will be more reliable. We propose a powerful objective function that is easily implemented and that captures the trade-off between the average travel time and its variance. The evaluation of the solution is done in terms of the 95th-percentile of the travel time distribution (assumed to be lognormal), which reflects well the quality of the solution in this stochastic time-dependent environment.     

A class of lifted path and flow-based formulations for the asymmetric traveling salesman problem with and without precedence constraints

In this paper, we present a new class of polynomial length formulations for the asymmetric traveling salesman problem (ATSP) by lifting an ordered path-based model using logical restrictions in concert with the Reformulation–Linearization Technique (RLT). We show that a relaxed version of this formulation is equivalent to a flow-based ATSP model, which in turn is tighter than the formulation based on the exponential number of Dantzig–Fulkerson–Johnson (DFJ) subtour elimination constraints. The proposed lifting idea is applied to derive a variety of new formulations for the ATSP, and we explore several dominance relationships among these. We also extend these formulations to include precedence constraints in order to enforce a partial order on the sequence of cities to be visited in a tour. Computational results are presented to exhibit the relative tightness of our formulations and the efficacy of the proposed lifting process.     

Understanding chicken walks on n × n grid: Hamiltonian paths,discrete dynamics,and rectifiable paths

Understanding animal movements and modeling the routes they travel can be essential in studies of pathogen transmission dynamics. Pathogen biology is also of crucial importance, defining the manner in which infectious agents are transmitted. In this article, we investigate animal movement with relevance to pathogen transmission by physical rather than airborne contact, using the domestic chicken and its protozoan parasite Eimeria as an example. We have obtained a configuration for the maximum possible distance that a chicken can walk through straight and nonoverlapping paths (defined in this paper) on square grid graphs. We have obtained preliminary results for such walks which can be practically adopted and tested as a foundation to improve understanding of nonairborne pathogen transmission. Linking individual nonoverlapping walks within a grid‐delineated area can be used to support modeling of the frequently repetitive, overlapping walks characteristic of the domestic chicken, providing a framework to model fecal deposition and subsequent parasite dissemination by fecal/host contact. We also pose an open problem on multiple walks on finite grid graphs. These results grew from biological insights and have potential applications. © 2014 The Authors. Mathematical Methods in the Applied Sciences published by John Wiley & Sons Ltd.     

Travel time models for automated warehouses with aisle transferring storage and retrieval machine

This paper presents analytical travel time models for the computation of travel time for automated warehouses with the aisle transferring S/R machine (in continuation multi-aisle AS/RS). These models consider the operating characteristics of the storage and retrieval machine such as acceleration and deceleration and the maximum velocity. Assuming uniform distributed storage rack locations and pick aisles and using the probability theory, the expressions of the cumulative distribution functions with which the mean travel time is calculated, have been determined. The computational models enable the calculation of the mean travel time for the single and dual command cycles, from which the performance of multi-aisle AS/RS can be evaluated. A simulation model of multi-aisle AS/RS has been developed to compare the performances of the proposed analytical travel time models. The analyses show that regarding all examined types of multi-aisle AS/RS, the results of proposed analytical travel time models correlate with the results of simulation models of multi-aisle AS/RS.     

A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem

This paper introduces a new memetic algorithm specialized for the asymmetric instances of the traveling salesman problem (ATSP). The method incorporates a new local search engine and many other features that contribute to its effectiveness, such as: (i) the topological organization of the population as a complete ternary tree with thirteen nodes; (ii) the hierarchical organization of the population in overlapping clusters leading to the special selection scheme; (iii) efficient data structures. Computational experiments are conducted on all ATSP instances available in the TSPLIB, and on a set of larger asymmetric instances with known optimal solutions. The comparisons show that the results obtained by our method compare favorably with those obtained by several other algorithms recently proposed for the ATSP.     

Cluster based branching for the asymmetric traveling salesman problem

This paper presents a new branching scheme for the asymmetric traveling salesman problem (ATSP) based on clusters. A cluster is defined as a node set with the characteristic that there exists an optimal solution in which the nodes in the node set are visited consecutively. The paper considers identification of clusters, implementation of a cluster based branching scheme, and cluster based dominance tests. The new approach is implemented in a branch and bound algorithm using a well-known additive bounding procedure. Considerable savings in computing time are obtained compared to previously published assignment based branch and bound algorithms for the ATSP.     

Ambulance location and relocation problems with time-dependent travel times

EMERGENCY SERVICE PROVIDERS ARE FACING THE FOLLOWING PROBLEM: how and where to locate vehicles in order to cover potential future demand effectively. Ambulances are supposed to be located at designated locations such that in case of an emergency the patients can be reached in a time-efficient manner. A patient is said to be covered by a vehicle if (s)he can be reached by an ambulance within a predefined time limit. Due to variations in speed and the resulting travel times it is not sufficient to solve the static ambulance location problem once using fixed average travel times, as the coverage areas themselves change throughout the day. Hence we developed a multi-period version, taking into account time-varying coverage areas, where we allow vehicles to be repositioned in order to maintain a certain coverage standard throughout the planning horizon. We have formulated a mixed integer program for the problem at hand, which tries to optimize coverage at various points in time simultaneously. The problem is solved metaheuristically using variable neighborhood search. We show that it is essential to consider time-dependent variations in travel times and coverage respectively. When ignoring them the resulting objective will be overestimated by more than 24%. By taking into account these variations explicitly the solution on average can be improved by more than 10%.     

User equilibrium traffic network assignment with stochastic travel times and late arrival penalty

The classical Wardrop User Equilibrium (UE) assignment model assumes traveller choices are based on fixed, known travel times, yet these times are known to be rather variable between trips, both within and between days; typically, then, only mean travel times are represented. Classical Stochastic User Equilibrium (SUE) methods allow the mean travel times to be differentially perceived across the population, yet in a conventional application neither the UE or SUE approach recognises the travel times to be inherently variable. That is to say, there is no recognition that drivers risk arriving late at their destinations, and that this risk may vary across different paths of the network and according to the arrival time flexibility of the traveller. Recent work on incorporating risky elements into the choice process is seen either to neglect the link to the arrival constraints of the traveller, or to apply only to restricted problems with parallel alternatives and inflexible travel time distributions. In the paper, an alternative approach is described based on the ‘schedule delay’ paradigm, penalising late arrival under fixed departure times. The approach allows flexible travel time densities, which can be fitted to actual surveillance data, to be incorporated. A generalised formulation of UE is proposed, termed a Late Arrival Penalised UE (LAPUE). Conditions for the existence and uniqueness of LAPUE solutions are considered, as well as methods for their computation. Two specific travel time models are then considered, one based on multivariate Normal arc travel times, and an extended model to represent arc incidents, based on mixture distributions of multivariate Normals. Several illustrative examples are used to examine the sensitivity of LAPUE solutions to various input parameters, and in particular its comparison with UE predictions. Finally, paths for further research are discussed, including the extension of the model to include elements such as distributed arrival time constraints and penalties.     

A study of travel time prediction using universal kriging

This study describes an approach for predicting travel time by kriging. The kriging method, which is a means of spatial prediction, is used as prediction measure for car travel time in an imaginary four-dimensional space. Each point in the space represents a single journey: the coordinates of a point are the coordinates of the origin and destination on a plane. The travel time can then be viewed as a function over this four-dimensional space. The prediction relies on the feature that nearby points (in the four-dimensional space) will have almost the same travel time. In this approach, it is not necessary to break down travel times from origin to destination into link travel times. The approach will also allow us to use information from “probe vehicles” for travel time prediction in the near future. The feasibility of this approach is demonstrated through a case study in London and its environs. The case study uses 200 observations for verification. The multiple correlation coefficient of estimated travel time and the verification data is 0.9045. The results indicate that 95% prediction limits are between ±10 minutes and ±30 minutes for travel between two arbitrary points. This prediction method is effective for urban districts with links having changeable travel time owing to congestion.     

The asymmetric travelling salesman problem and a reformulation of the Miller–Tucker–Zemlin constraints

In this paper we compare the linear programming (LP) relaxations of several old and new formulations for the asymmetric travelling salesman problem (ATSP). The main result of this paper is the derivation of a compact formulation whose LP relaxation is characterized by a set of circuit inequalities given by Grotschel and Padberg (In: Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A., Shmoys, D.B. (Eds.), The Travelling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, New York, 1985). The new compact model is an improved and disaggregated version of a well-known model for the ATSP based on the subtour elimination constraints (Miller et al., Journal of ACM 7 (1960) 326–329). The circuit inequalities are weaker than the subtour elimination constraints given by Dantzig et al. However, each one of these circuit inequalities can be lifted into several different facet defining inequalities which are not dominated by the subtour elimination inequalities. We show that some of the inequalities involved in the previously mentioned compact formulation can be lifted in such a way that, by projection, we obtain a small subset of the so-called D_k^← and D_k^→ inequalities. This shows that the LP relaxation of our strongest model is not dominated by the LP relaxation of the model presented by Dantzig et al. (Operations Research 2 (1954) 393–410). The new models motivate a new classification of formulations for the ATSP.     

Self‐organized trail systems in groups of humans

We have developed an experimental platform for studying the trail systems that spontaneously emerge when people are motivated to take advantage of the trails left by others. In this virtual environment, the participants' task is to reach randomly selected destinations while minimizing travel costs. The travel cost of every patch in the environment is inversely related to the number of times the patch was visited by others. The resulting trail systems are a compromise between people going to their destinations and going where many people have previously traveled. We compare the results from our group experiments to the Active Walker model of pedestrian motion from biophysics. The Active Walker model accounted for deviations of trails from the beeline paths, the gradual merging of trails over time, and the influences of scale and configuration of destinations on trail systems, as well as correctly predicting the approximate spatial distribution of people's steps. Two deviations of the model from empirically obtained results were corrected by (1) incorporating a distance metric sensitive to canonical horizontal and vertical axes, and (2) increasing the influence of a trail's travel cost on an agent's route as the agent approaches its destination. © 2006 Wiley Periodicals, Inc. Complexity 11: 43–50, 2006     

A travel time estimation model for a high-level picker-to-part system with class-based storage policies

Most previous related studies on warehouse configurations and operations only investigated single-level storage rack systems where the height of storage racks and the vertical movement of the picking operations are both not considered. However, in order to utilize the space efficiently, high-level storage systems are often used in warehouses in practice. This paper presents a travel time estimation model for a high-level picker-to-part system with the considerations of class-based storage policy and various routing policies. The results indicate that the proposed model appears to be sufficiently accurate for practical purposes. Furthermore, the effects of storage and routing policies on the travel time and the optimal warehouse layout are discussed in the paper.     

Algorithms for a stochastic selective travelling salesperson problem

In this paper, the selective travelling salesperson problem with stochastic service times, travel times, and travel costs (SSTSP) is addressed. In the SSTSP, service times, travel times and travel costs are known a priori only probabilistically. A non-negative value of reward for providing service is associated with each customer and there is a pre-specified limit on the duration of the solution tour. It is assumed that not all potential customers can be visited within this tour duration limit, even under the best circumstances. And, thus, a subset of customers must be selected. The objective of the SSTSP is to design an a priori tour that visits each chosen customer once such that the total profit (total reward collected by servicing customers minus travel costs) is maximized and the probability that the total actual tour duration exceeds a given threshold is no larger than a chosen probability value. We formulate the SSTSP as a chance-constrained stochastic program and propose both exact and heuristic approaches for solving it. Computational experiments indicate that the exact algorithm is able to solve small- and moderate-size problems to optimality and the heuristic can provide near-optimal solutions in significantly reduced computing time.     

A survey of literature on automated storage and retrieval systems

Automated Storage and Retrieval Systems (AS/RSs) are warehousing systems that are used for the storage and retrieval of products in both distribution and production environments. This paper provides an overview of literature from the past 30 years. A comprehensive explanation of the current state of the art in AS/RS design is provided for a range of issues such as system configuration, travel time estimation, storage assignment, dwell-point location, and request sequencing. The majority of the reviewed models and solution methods are applicable to static scheduling and design problems only. Requirements for AS/RSs are, however, increasingly of a more dynamic nature for which new models will need to be developed to overcome large computation times and finite planning horizons, and to improve system performance. Several other avenues for future research in the design and control of AS/RSs are also specified.     

Performance of miniload systems with two-class storage

To increase throughput, the higher turnover items are often stored near the input/output point of a miniload system. We analyze the performance of such a miniload with a square-in-time rack containing two storage zones: high turnover and low turnover. First, we derive the distribution of the dual command travel time. System performance measures such as the throughput depend upon the distribution of the dual command travel time and the distribution of pick times. We work out the details and obtain closed-form expressions for throughput for two important families of pick time distributions: deterministic and exponential. Finally, we investigate how the size of the high turnover region affects throughput.