A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

Single-Row Transformation of Complete Graphs

A complete graph is a fully-connected graph where every node is adjacent to all other nodes in the graph. Very often, many applications in science and engineering are reducible to this type of graph. Hence, a simplified form of a complete graph contributes in providing the solutions to these problems. In this paper, we present a technique for transforming a complete graph into a single-row routing problem. Single-row routing is a classical technique in the VLSI design that is known to be NP-complete. We solved this problem earlier using a method called ESSR, and, the same technique is applied to the present work to transform a complete graph into its single-row routing representation. A parallel computing model is proposed which contributes in making the problem modular and scalable. We also discuss the application of this work on the channel assignment problem in the wireless cellular telephone networks.     

Routing management in very large-scale networks

Very large-scale networks and internetworks are already a reality, and routing constitutes an essential element for the operation of such networks. Unfortunately, the overhead of an adaptive routing algorithm becomes prohibitive in a network or an internetwork with several nodes (in the order of thousands or more) and a flat routing architecture in which each node must know how to reach every other node in the system. In this paper, we discuss the problems with existing network architectures and adaptive routing algorithms when they are applied to very large networks. Then we present a new hierarchical network architecture designed to solve these problems. The two key features of the new architecture are a new loop-free routing algorithm and a new hierarchical addressing mechanism. We compare this architecture with others previously proposed from the standpoint of the savings in routing overhead and the optimality of the paths obtained.     

Optimizing multiship routing and scheduling with constraints on inventory levels in a Brazilian oil company

This study addresses a real‐life multiship routing and scheduling application with inventory constraints that arises in pickup and delivery operations of different types of crude oil from various offshore oil rigs (platforms) to coastal terminals. Oil transportation largely results from the need to maintain inventories at each supply point (platform) between minimum and maximum levels, considering production rates in these operational points, and to meet demands of different oils in the terminals within the planning time horizon. Routing and scheduling of the available fleet aims to obtain solutions of minimum total costs, subject to various constraints such as the maximum volume of cargo carried on each ship, simultaneous cargo unloading in some terminals, conditions that rule ship docking in offshore platforms and terminal berths, among others. In this research, we modify and extend inventory constrained maritime routing and scheduling models to appropriately represent the problem of a case study at a Brazilian company and to solve small‐to‐moderate instances based on real data. We also present a matheuristic to deal with larger problem instances. Solution evaluation by company experts indicates that the model and this hybrid heuristic properly represent the problem and highlights the potential of their application in practice.     

A decomposition approach for commodity pickup and delivery with time-windows under uncertainty

We consider a special class of large-scale, network-based, resource allocation problems under uncertainty, namely that of multi-commodity flows with time-windows under uncertainty. In this class, we focus on problems involving commodity pickup and delivery with time-windows. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By a priori modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. We propose a novel modeling and solution framework involving a decomposition scheme that separates problems into a routing master problem and Scheduling Sub-Problems; and iterates to find the optimal solution. Uncertainty is captured in part by the master problem and in part by the Scheduling Sub-Problem. We present proof-of-concept for our approach using real data involving routing and scheduling for a large shipment carrier’s ground network, and demonstrate the improved robustness of solutions from our approach.     

A traveling salesman problem with pickups and deliveries,time windows and draft limits: Case study from chemical shipping

This paper introduces and studies a real in-port ship routing and scheduling problem faced by chemical shipping companies. We show that this problem can be modeled as a Traveling Salesman Problem with Pickups and Deliveries, Time Windows and Draft Limits (TSPPD-TWDL). We propose a mathematical formulation for the TSPPD-TWDL and suggest a solution method based on forward dynamic programming (DP) to solve the problem. A set of label extension rules are also proposed to accelerate and enhance the performance of the algorithm. Computational studies show that the label extension rules are essential to the DP-algorithm, and the proposed solution method is able to solve real-sized in-port routing and scheduling problems in chemical shipping efficiently.     

A dynamic vehicle routing problem with time-dependent travel times

In this paper we present a formulation for the dynamic vehicle routing problem with time-dependent travel times. We also present a genetic algorithm to solve the problem. The problem is a pick-up or delivery vehicle routing problem with soft time windows in which we consider multiple vehicles with different capacities, real-time service requests, and real-time variations in travel times between demand nodes.The performance of the genetic algorithm is evaluated by comparing its results with exact solutions and lower bounds for randomly generated test problems. For small size problems with up to 10 demands, the genetic algorithm provides almost the same results as the exact solutions, while its computation time is less than 10% of the time required to produce the exact solutions. For the problems with 30 demand nodes, the genetic algorithm results have less than 8% gap with lower bounds.This research also shows that as the uncertainty in the travel time information increases, a dynamic routing strategy that takes the real-time traffic information into account becomes increasingly superior to a static one. This is clear when we compare the static and dynamic routing strategies in problem scenarios that have different levels of uncertainty in travel time information. In additional tests on a simulated network, the proposed algorithm works well in dealing with situations in which accidents cause significant congestion in some part of the transportation network.     

A new and efficient ant-based heuristic method for solving the traveling salesman problem

Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.     

Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery

The vehicle routing problem with simultaneous pick-up and delivery is the problem of optimally integrating goods distribution and waste collection, when no precedence constraints are imposed on the order in which the operations must be performed. The purpose of this paper is to present heuristic algorithms to solve this problem approximately in a small amount of computing time. We present and compare constructive algorithms, local search algorithms and tabu search algorithms, reporting on our computational experience with all of them. In particular we address the issue of applying the tabu search paradigm to algorithms based on complex and variable neighborhoods. For this purpose we combine arc-exchange-based and node-exchange-based neighborhoods, employing different and interacting tabu lists. All the algorithms presented in this paper are applicable to problems in which each customer may have either a pick-up demand or a delivery demand as well as to problems in which each customer may require both kinds of operation.     

A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet

Nowadays genetic algorithms stand as a trend to solve NP-complete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses parallel genetic algorithms and scatter search coupled with a decomposition-into-petals procedure for solving a class of vehicle routing and scheduling problems. The parallel genetic algorithm presented is based on the island model and its performance is evaluated for a heterogeneous fleet problem, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.     

Solving the ship inventory routing and scheduling problem with undedicated compartments

This paper presents a ship inventory routing and scheduling problem with undedicated compartments (sIRPSP-UC). The objective of the problem is to find a minimum cost solution while satisfying a number of technical and physical constraints within a given planning horizon. In this problem, we identify four sub-problems that need to be decided simultaneously: route selections, ship selection, loading, and unloading activity procedures. To solve this problem, first, we developed a mixed integer linear programming model. We then developed a one-step greedy heuristic, and then based on this heuristic, we propose a set of heuristics. Each heuristic has a combination of rules for each sub-problem. A number of problem instances are used to compare the solutions of the two approaches. We applied selected good combinations of rules to solve each problem using the heuristic approach. The results show that 8 out 12 of the considered problem instances have no gap with MILP solution solved using LINGO. We also find that the average gap is 1.96%. In contrast when we consistently use the same combination for all iterations, there are no dominant combinations of heuristics that can find good solutions for all the problem instances.     

Approximate solutions for the capacitated arc routing problem

The capacitated arc routing problem (CARP), is a capacitated variation of the arc routing problems in which there is a capacity constraint associated with each vehicle. Due to the computational complexity of the problem, recent research has focussed on developing and testing heuristic algorithms which solve the CARP approximately. In this paper, we review some of the existing solution procedures, analyze their complexity, and present two modifications of the existing methods to obtain near-optimal solutions for the CARP. Extensive computational results are presented and analyzed.     

Using the primal-dual interior point algorithm within the branch-price-and-cut method

Branch-price-and-cut has proven to be a powerful method for solving integer programming problems. It combines decomposition techniques with the generation of both columns and valid inequalities and relies on strong bounds to guide the search in the branch-and-bound tree. In this paper, we present how to improve the performance of a branch-price-and-cut method by using the primal-dual interior point algorithm. We discuss in detail how to deal with the challenges of using the interior point algorithm with the core components of the branch-price-and-cut method. The effort to overcome the difficulties pays off in a number of advantageous features offered by the new approach. We present the computational results of solving well-known instances of the vehicle routing problem with time windows, a challenging integer programming problem. The results indicate that the proposed approach delivers the best overall performance when compared with a similar branch-price-and-cut method which is based on the simplex algorithm.     

Heuristics for the multi-period orienteering problem with multiple time windows

We present the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering problem (OP) and team orienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application. The problem itself comes from a real industrial case. Specific route duration constraints result in a route feasibility subproblem. We propose an exact algorithm for this subproblem, and we embed it in a variable neighborhood search method to solve the whole routing problem. We then provide experimental results for this method. We compare them to a commercial solver. We also adapt our method to standard benchmark OP and TOP instances, and provide comparative tables with state-of-the-art algorithms.     

Very large-scale vehicle routing: new test problems,algorithms, and results

The standard vehicle routing problem was introduced in the OR/MS literature about 45 years ago. Since then, the vehicle routing problem has attracted an enormous amount of research attention. In the late 1990s, large vehicle routing problem instances with nearly 500 customers were generated and solved using metaheuristics. In this paper, we focus on very large vehicle routing problems. Our contributions are threefold. First, we present problem instances with as many as 1200 customers along with estimated solutions. Second, we introduce the variable-length neighbor list as a tool to reduce the number of unproductive computations. Third, we apply record-to-record travel with a variable-length neighbor list to 32 problem instances and obtain high-quality solutions, very quickly.     

Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)

Bridging the gap between low-level features and semantics is a problem commonly acknowledged in the Multimedia community. Event modeling can fill this gap by representing knowledge about the data at different level of abstraction. In this paper we present the Simple Event Model (SEM) and its application in a Maritime Safety and Security use case about Situational Awareness, where the data also come as low-level features (of ship trajectories). We show how we abstract over these low-level features, recognize simple behavior events using a Piecewise Linear Segmentation algorithm, and model the resulting events as instances of SEM. We aggregate web data from different sources, apply deduction rules, spatial proximity reasoning, and semantic web reasoning in SWI-Prolog to derive abstract events from the recognized simple events. The use case described in this paper comes from the Dutch Poseidon project.     

利用卫星SAR监测海上航行船舶

星载合成孔径雷达能够得到高分辨率的遥感图像。船舶目标以及船舶航迹在一些卫星ＳＡＲ海洋图像中清晰可见。文中就利用卫星ＳＡＲ对船舶目标和船舶航迹的监测问题进行了论述。首先简要介绍了船舶目标及其航迹的ＳＡＲ成像原理,然后对不同成像条件得到的航迹图像进行了分类,最后分析了船舶目标的检测、船舶航迹特征的检测以及船舶的相应参数的估计问题。通过对上述问题的讨论,认为利用卫星ＳＡＲ监测海上航行的船舶是可行的,而且是一项很有意义的工作。     

An iterative approach to robust and integrated aircraft routing and crew scheduling

In airline scheduling a variety of planning and operational decision problems have to be solved. We consider the problems aircraft routing and crew pairing: aircraft and crew must be allocated to flights in a schedule in a minimal cost way. Although these problems are not independent, they are usually formulated as independent mathematical optimisation models and solved sequentially. This approach might lead to a suboptimal allocation of aircraft and crew, since a solution of one of the problems may restrict the set of feasible solutions of the problem solved later. Also, when minimal cost solutions are used in operations, a short delay of one flight can cause very severe disruptions of the schedule later in the day. We generate solutions that incur small costs and are also robust to typical stochastic variability in airline operations. We solve the two original problems iteratively. Starting from a minimal cost solution, we produce a series of solutions which are increasingly robust. Using data from domestic airline schedules we evaluate the benefits of the approach as well as the trade-off between cost and robustness. We extend our approach considering the aircraft routing problem together with two crew pairing problems, one for technical crew and one for flight attendants.     

Stochastic routing in ad-hoc networks

We investigate a network routing problem where a probabilistic local broadcast transmission model is used to determine routing. We discuss this model's key features, and note that the local broadcast transmission model can be viewed as soft handoff for an ad-hoc network. We present results showing that an index policy is optimal for the routing problem. We extend the network model to allow for control of transmission type, and prove that the index nature of the optimal routing policy remains unchanged. We present three distributed algorithms which compute an optimal routing policy, discuss their convergence properties, and demonstrate their performance through simulation.     

A case study of mutual routing-scheduling reformulation

Classical models of combinatorial problems, such as scheduling, play a key role in optimization research as they allow theoretical and empirical work to focus on core issues of problem solving performance. By their very nature, however, classical models are a simplification of real-world problems. We argue that the challenge now is to address those real-world features that were simplified away in the classical models, and that in order to do this we should investigate how problem features affect solution technologies. In this paper, we perform an empirical study of vehicle routing problems (VRPs) and job shop scheduling problems (JSPs) using commercially available constraint-based optimization software, ILOG Dispatcher, and ILOG Scheduler. We start with instances of the classical VRP and JSP and systematically make the two problems more realistic by removing the simplifying assumptions of the classical models. While doing so, we empirically investigate the key problem characteristics that make the problems more amenable to one solution technique or the other. We[-5pc] argue that our observations are symptomatic of the underlying technologies used in the employed software. This work was supported by EPSRC research grant GR/M90641, by Science Foundation Ireland under Grant 00/PI.1/C075, and by ILOG, SA.