Chung-Cheng Lu

A simulated annealing heuristic for the truck and trailer routing problem with time windows

European firms have been using a combination of trucks and trailers in the delivery/collection of food products for years. Thus, some previous studies had been devoted to improving the efficiency of the resulting truck and trailer routing problem (TTRP). Since time window constraints are present in many real-world routing applications, in this study, we introduce the truck and trailer routing problem with time windows (TTRPTW) to bring the TTRP model closer to the reality. A simulated annealing (SA) heuristic is proposed for solving the TTRPTW. Two computational experiments are conducted to test the performance of the proposed SA heuristic. The results indicate that the proposed SA heuristic is capable of consistently producing quality solutions to the TTRPTW within a reasonable time.

Robust scheduling on a single machine to minimize total flow time

In a real-world manufacturing environment featuring a variety of uncertainties, production schedules for manufacturing systems often cannot be executed exactly as they are developed. In these environments, schedule robustness that guarantees the best worst-case performance is a more appropriate criterion in developing schedules, although most existing studies have developed optimal schedules with respect to a deterministic or stochastic scheduling model. This study concerns robust single machine scheduling with uncertain job processing times and sequence-dependent family setup times explicitly represented by interval data. The objective is to obtain robust sequences of job families and jobs within each family that minimize the absolute deviation of total flow time from the optimal solution under the worst-case scenario. We prove that the robust single machine scheduling problem of interest is NP-hard. This problem is reformulated as a robust constrained shortest path problem and solved by a simulated annealing-based algorithmic framework that embeds a generalized label correcting method. The results of numerical experiments demonstrate that the proposed heuristic is effective and efficient for determining robust schedules. In addition, we explore the impact of degree of uncertainty on the performance measures and examine the tradeoff between robustness and optimality.

Toll pricing and heterogeneous users: Approximation algorithms for finding bicriterion time-dependent efficient paths in large-scale traffic networks

This paper presents both exact and approximation algorithms for finding extreme efficient time-dependent shortest paths for use with dynamic traffic assignment applications to networks with variable toll pricing and heterogeneous users (with different value of time preferences). A parametric least-generalized cost path algorithm is presented to determine a complete set of extreme efficient time-dependent paths that simultaneously consider travel time and cost criteria. However, exact procedures may not be practical for large networks. For this reason, approximation schemes are devised and tested. Based on the concept of ϵ-efficiency in multiobjective shortest path problems, a binary search framework is developed to find a set of extreme efficient paths that minimize expected approximation error, with the use of the underlying value of time distribution. Both exact and approximation schemes (along with variants) are tested on three actual traffic networks. The experimental results indicate that the computa...

Minimization of maximum lateness on parallel machines with sequence-dependent setup times and job release dates

In this paper, we consider an identical parallel machine scheduling problem with sequence-dependent setup times and job release dates. An improved iterated greedy heuristic with a sinking temperature is presented to minimize the maximum lateness. To verify the developed heuristic, computational experiments are conducted on a well-known benchmark problem data set. The experimental results show that the proposed heuristic outperforms the basic iterated greedy heuristic and the state-of-art algorithms on the same benchmark problem data set. It is believed that this improved approach will also be helpful for other applications.

Dynamic Network Simulation-Assignment Platform for Multiproduct Intermodal Freight Transportation Analysis

This paper develops a dynamic freight network simulation–assignment platform for the analysis of multiproduct intermodal freight transportation systems. At the core of the platform is a model framework for the mode–path assignment problem in multimodal freight transportation networks. The framework consists of three main components: a multimodal freight network simulation component, a multimodal freight assignment component, and a multiple product intermodal shortest path procedure. The freight network simulation component incorporates a bulk queuing model to evaluate transfer delay experienced by shipments at intermodal transfer terminals, classification yards, and ports. The multimodal freight assignment component determines the network flow pattern from a mode–path alternative set calculated by the multiple product intermodal shortest path procedure, based on the link travel costs and node transfer delays from the multimodal freight network simulation component. This model can represent individual shipment mode–path choice behavior, consolidation policy, conveyance link moving, and individual shipment terminal transfer in an iterative solution framework.

Cell formation using a simulated annealing algorithm with variable neighbourhood

The broad applications of cellular manufacturing make the cell formation problem (CFP) a core subject in the field of manufacturing. Due to the combinatorial nature of the CFP, a simulated annealing-based meta-heuristic with variable neighbourhood was developed to form part-machine cells. To validate and verify the proposed approach, computational experiments were conducted on a set of CFPs from the literature. Using the grouping efficacy as a performance criterion, the proposed approach is shown to outperform existing state-of-the-art algorithms by exceeding or matching the best known solutions in the majority of the test problems. The evaluation results clearly show that this study successfully develops an effective approach for CFPs. [Submitted 25 July 2009; Revised 22 October 2009, 11 November 2009; Accepted 12 November 2009]

How Reliable Is This Route? Predictive Travel Time and Reliability for Anticipatory Traveler Information Systems

Schemes are addressed for providing predictive travel time information in consistent anticipatory route guidance systems. A user-equilibrium time-dependent traffic assignment algorithm with the ability for multiple-user class network loading is used to generate consistent anticipatory route guidance information when a fraction of the population is equipped to receive and act on that information. Both a theoretical analysis and a simulation-based approach are presented to evaluate the provision of anticipatory route guidance information in terms of experienced path travel times and their variations. Results indicate that the proposed anticipatory route guidance strategy is accurate and reliable and can improve real-time traffic network performance.

Robust weighted vertex p-center model considering uncertain data: An application to emergency management

This paper presents a generalized weighted vertex p-center (WVPC) model that represents uncertain nodal weights and edge lengths using prescribed intervals or ranges. The objective of the robust WVPC (RWVPC) model is to locate p facilities on a given set of candidate sites so as to minimize worst-case deviation in maximum weighted distance from the optimal solution. The RWVPC model is well-suited for locating urgent relief distribution centers (URDCs) in an emergency logistics system responding to quick-onset natural disasters in which precise estimates of relief demands from affected areas and travel times between URDCs and affected areas are not available. To reduce the computational complexity of solving the model, this work proposes a theorem that facilitates identification of the worst-case scenario for a given set of facility locations. Since the problem is NP-hard, a heuristic framework is developed to efficiently obtain robust solutions. Then, a specific implementation of the framework, based on simulated annealing, is developed to conduct numerical experiments. Experimental results show that the proposed heuristic is effective and efficient in obtaining robust solutions. We also examine the impact of the degree of data uncertainty on the selected performance measures and the tradeoff between solution quality and robustness. Additionally, this work applies the proposed RWVPC model to a real-world instance based on a massive earthquake that hit central Taiwan on September 21, 1999.

Efficient Implementation of Method of Successive Averages in Simulation-Based Dynamic Traffic Assignment Models for Large-Scale Network Applications

The method of successive averages remains by far the most widely used solution heuristic in simulation-based dynamic traffic assignment. Its simplicity and the nonrequirement of derivative information for the flow-cost mapping function are the main reasons for its widespread use, especially in the realm of dynamic traffic assignment (DTA). However, its convergence properties in real-life networks have been inconclusive, especially because (a) simulation-based models typically are not well behaved mathematically, and therefore their solution properties are not guaranteed, and (b) predetermined step sizes do not exploit local information in searching for a solution and therefore tend to have sluggish performance properties. An effort was made to improve on the performance of the method of successive averages heuristic for user-equilibrium and system-optimal DTA problems on large congested networks through novel implementations that derive their efficiency from exploiting local information made available in the results of vehicle-based simulation models used to provide the mapping between a feasible path flow assignment and the experienced travel cost in a DTA solution framework. The results of extensive numerical tests on actual networks are reported, confirming the performance improvements attainable with the new approach.

Robust vertex p-center model for locating urgent relief distribution centers

This work locates urgent relief distribution centers (URDCs) on a given set of candidate sites using a robust vertex p-center (RVPC) model. This model addresses uncertain travel times, represented using fixed intervals or ranges instead of probability distributions, between URDCs and affected areas. The objective of locating a predetermined number (p) of URDCs is to minimize worst-case deviation in maximum travel time from the optimal solution. To reduce the complexity of solving the RVPC problem, this work proposes a property that facilitates identification of the worst-case scenario for a given set of URDC locations. Since the problem is NP-hard, a heuristic framework is developed to efficiently obtain robust solutions. Then, a specific implementation of the framework, based on simulated annealing, is developed to conduct computational experiments. Experimental results show that the proposed heuristic is effective and efficient in obtaining robust solutions of interest. This work examines the impact of the degree of data uncertainty on the selected performance measures and the tradeoff between solution quality and robustness. Additionally, this work demonstrates the applicability of the proposed model to natural disasters based on a real-world instance. The result is compared with that obtained by a scenario-based, two-stage stochastic model. This work contributes significantly to the growing body of literature applying robust optimization approaches to emergency logistics.