Trends in transportation and logistics

Problems in transportation and logistics had to be tackled long before computers and Operational Research (OR) became available to support decision making. After the first optimization models were developed, OR has substantially contributed in making transportation systems efficient and companies with complex transportation and logistics problems competitive. Over the years, technology has evolved and the same has done OR. In this paper, the history of problems and OR contributions in transportation and logistics will be shortly reviewed together with the evolution of technology. Then, the future trends in this area will be discussed together with potential OR contributions.     

Hub location–allocation in intermodal logistic networks

Within the context of intermodal logistics, the design of transportation networks becomes more complex than it is for single mode logistics. In an intermodal network, the respective modes are characterized by the transportation cost structure, modal connectivity, availability of transfer points and service time performance. These characteristics suggest the level of complexity involved in designing intermodal logistics networks. This research develops a mathematical model using the multiple-allocation p-hub median approach. The model encompasses the dynamics of individual modes of transportation through transportation costs, modal connectivity costs, and fixed location costs under service time requirements. A tabu search meta-heuristic is used to solve large size (100 node) problems. The solutions obtained using this meta-heuristic are compared with tight lower bounds developed using a Lagrangian relaxation approach. An experimental study evaluates the performance of the intermodal logistics networks and explores the effects and interactions of several factors on the design of intermodal hub networks subject to service time requirements.     

OR Utilization in Nigeria: A Sample Survey

The study presents the results of a mail survey on the utilization of Operational Research (OR) techniques in tackling complex operational problems in the private sector in Nigeria. The survey was mailed to OR practitioners in nearly 1000 companies and was aimed at determining the familiarity with, and usage of, OR techniques, background of OR personnel, degree of computer usage, application areas, implementation and contributions of OR, problems attendant in using OR techniques, and future prospects for OR usage.     

多式联运分运人选择的模型和算法

在对多式联运分运人选择问题分析的基础上,运用图论技术构建了基于联运分运人选择的多式联运网络。综合考虑了运输的规模经济性、时效性、风险性和联运联盟的稳定性,建立了基于综合运输成本最小、运输风险最小和合作强度最大的多目标优化选择模型。通过主要目标法,将模型转化为单目标模型。借鉴生物免疫原理,设计了基于克隆增扩的人工免疫算法对问题进行求解。最后通过算例对方法进行了验证。     

基于CVaR的危险品公铁联运路径选择研究

公铁联运在危险品的多式联运中扮演着重要角色,为了降低危险品公铁联运风险,提高危险品公铁联运的安全性,危险品公铁联运的路径选择至关重要。本文运用条件风险价值(CVaR)理论,在对危险品公铁联运网络进行变形的基础上构建了考虑决策者风险规避程度的危险品公铁联运路径选择模型,设计了求解该模型的算法,并进行了算例分析。研究结果表明:通过该模型及其算法,可根据决策者对所需运输的危险品的运输风险规避程度,在危险品公铁联运网络中快速地选出使危险品公铁联运风险最小的运输路径和运输方式;决策者的风险规避程度会对危险品公铁联运过程中的运输路径和运输方式的选择产生重要影响。     

Minimizing greenhouse gas emissions in intermodal freight transport: an application to rail service design

Freight transport has undesirable effects on the environment. The most prominent of these is greenhouse gas emissions. Intermodal freight transport, where freight is shipped from origin to destination by a sequence of at least two transportation modes, offers the possibility of shifting freight (either partially or in full) from one mode to another in the hope of reducing the greenhouse emissions by appropriately scheduling the services and routing the freight. Traditional planning methods for scheduling services in an intermodal transportation network usually focus on minimizing travel or time-related costs of transport. This article breaks away from such an approach by addressing the issue of incorporating environment-related costs (greenhouse gases, to be specific) into freight transportation planning and proposes an integer program in the form of a linear cost, multicommodity, capacitated network design formulation that minimizes the amount of greenhouse gas emissions of transportation activities. Computational results based on an application of the proposed approach on a real-life rail freight transportation network are presented.     

Integrated supply chain planning based on a combined application of operations research and optimal control

In operations research (OR), remarkable advancements have been achieved in the domain of supply chain (SC) planning for the last two decades. In recent years, the works on SCs have been broadened to integrate production and transportation planning along with covering the SC dynamics and execution control. Based on a combination of fundamental results of the modern optimal program control (OPC) theory with the optimization methods of OR, an original integrated model of production and transportation planning in the SC is developed. It is shown explicitly how to distribute static and dynamics variables and constraints among the OR and OPC models and interconnect static elements in optimization linear programming model with corresponding dynamic elements in OPC model. This makes it possible to integrate SC production and transportation planning and execution taking into account process non-stationarity caused by structure dynamics. In doing so, the developed framework contributes to the advancing decision-making support for supply chain management (SCM). Conventionally isolated SCM problems may be considered from a different viewpoint and new integrated problems may be revealed, stated and solved due to the mutual enriching of OR and OPC techniques.     

Operations research practice on logistics management in Taiwan: An academic view

The opinions of logistics educators in Taiwan on OR practices in the domestic logistics industry are explored in this research. In this study, questionnaires were given to 42 pre-screened educators at 10 logistics departments and graduate institutes. According to the 37 valid returned questionnaires, 70% of the responding educators believe the major source of OR techniques in Taiwan’s logistics industry originates from individual employee training, while 92% believe OR is not widely used primarily because companies are unfamiliar with OR techniques. OR techniques were considered helpful in solving problems by 73%. Generally speaking, familiarity with OR techniques is not associated with implementation of OR techniques by educators. Additionally, logistics educators express concern about insufficient training for logistics students in Taiwan. However, they are optimistic about the logistics industry’s willingness to more actively adopt OR techniques in Taiwan in the next two years.     

Intermodal shipments as recourse in logistics disruptions

This research deals with the flexibility of transportation systems when faced with disruptions. A network optimization model is used to investigate the feasibility of using intermodal shipments as recourse to disruptions in a transportation network. In a study of US interstate highways and intermodal rail networks, performance of over-the-road and intermodal shipments is compared under different disruption scenarios. The results show that the topology of US transportation system and locations of existing intermodal terminals provide required path redundancies and a strategic benefit for intermodal shipments to bypass disrupted regions with lower costs and competitive shipment times compared to rerouted road shipments.     

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.     

The practice of operational research in Taiwan

This paper presents the results of a questionnaire survey of operational research/management science (OR/MS) practices in Taiwanese companies. The survey was addressed to the current status and future perspectives of OR/MS in Taiwan. During the spring of 2001, 2000 questionnaires were mailed to the top 1000 domestic manufacturing firms and top 1000 domestic service organizations. These asked questions concerning specific OR/MS techniques being used, application areas of OR/MS, reasons for using OR/MS, reasons for not using OR/MS, difficulties encountered practically, and future perspectives. Results from 262 valid respondents provided several informative findings. Compared with the 1995 survey of Kao et al, it found that the percentage of companies that have used OR/MS techniques increased from 62.7% in 1995 to 76.7% in 2001. Also, the percentage of companies that have never heard of OR/MS decreased from 16.1% in 1995 to 11.1% in 2001. Because of the Taiwan government recently developing educational and training programs in OR/MS, it found that the importance of OR/MS has been recognized by Taiwan companies as an efficient management tool for economic development, and the usage of OR/MS will continuously increase.     

A lead-time based approach for planning rail–truck intermodal transportation of dangerous goods

The remarkable growth in intermodal transportation over the past two decades has not been matched by a comparable level of academic activity, especially in the context of transporting hazardous materials (hazmats). In this paper, we present a first attempt for the development of an analytical framework for planning rail–truck intermodal transportation of hazmats. A bi-objective optimization model to plan and manage intermodal shipments is developed. To represent the current practice, the routing decisions in the model are driven by the delivery-times specified by the customers. An iterative decomposition based solution methodology which takes advantage of the problem structure is provided. A realistic problem instance based on the intermodal service network in eastern US is solved. This framework is used for developing a number of managerial insights, and for generating elements of the risk-cost frontier.     

Data mining-based dispatching system for solving the local pickup and delivery problem

The Local Pickup and Delivery Problem (LPDP) has drawn much attention, and optimization models and algorithms have been developed to address this problem. However, for real world applications, the large-scale and dynamic nature of the problem causes difficulties in getting good solutions within acceptable time through standard optimization approaches. Meanwhile, actual dispatching solutions made by field experts in transportation companies contain embedded dispatching rules. This paper introduces a Data Mining-based Dispatching System (DMDS) to first learn dispatching rules from historical data and then generate dispatch solutions, which are shown to be as good as those generated by expert dispatchers in the intermodal freight industry. Three additional benefits of DMDS are: (1) it provides a simulation platform for strategic decision making and analysis; (2) the learned dispatching rules are valuable to combine with an optimization algorithm to improve the solution quality for LPDPs; (3) by adding optimized solutions to the training data, DMDS is capable to generate better-than-actuals solutions very quickly.     

Operations research applications in Taiwan: A linguistic approach

This paper discusses the utilization of specified Operations Research (OR) techniques and the application areas of OR in Taiwan's companies. Since the data solicited from the business organizations are represented by fuzzy linguistic terms, a linguistic approach is used to transform the data to total utility values for ranking. The results show that decision making with computers and statistical forecasting are the two most frequently used OR techniques, while Markovian decision processes and queuing models are the two least used ones. Regarding the application areas, manufacturing firms consider production whereas service companies consider finance as the major area for application. Both agree that human resource is the area receiving the least attention. With the linguistic approach of this study, more valuable results are derived.     

A truck scheduling problem arising in intermodal container transportation

We address a truck scheduling problem that arises in intermodal container transportation, where containers need to be transported between customers (shippers or receivers) and container terminals (rail or maritime) and vice versa. The transportation requests are handled by a trucking company which operates several depots and a fleet of homogeneous trucks that must be routed and scheduled to minimize the total truck operating time under hard time window constraints imposed by the customers and terminals. Empty containers are considered as transportation resources and are provided by the trucking company for freight transportation. The truck scheduling problem at hand is formulated as Full-Truckload Pickup and Delivery Problem with Time Windows (FTPDPTW) and is solved by a 2-stage heuristic solution approach. This solution method was specially designed for the truck scheduling problem but can be applied to other problems as well. We assess the quality of our solution approach on several computational experiments.     

A Survey of the Application of Artificial Intelligence Techniques within the OR Society

This paper presents the findings of a survey of the application of artificial intelligence techniques amongst OR Society members. The survey involved sending a questionnaire to one-third of the UK-based members of the OR Society and generated a 26% response rate. The survey investigated general characteristics of the respondents to enable comparison with previous surveys; determined the current and growing use of AI techniques, and in which areas they are applied; and characterized the benefits and constraints of using such techniques.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

Constraint satisfaction problems: Algorithms and applications

A constraint satisfaction problem (CSP) requires a value, selected from a given finite domain, to be assigned to each variable in the problem, so that all constraints relating the variables are satisfied. Many combinatorial problems in operational research, such as scheduling and timetabling, can be formulated as CSPs. Researchers in artificial intelligence (AI) usually adopt a constraint satisfaction approach as their preferred method when tackling such problems. However, constraint satisfaction approaches are not widely known amongst operational researchers. The aim of this paper is to introduce constraint satisfaction to the operational researcher. We start by defining CSPs, and describing the basic techniques for solving them. We then show how various combinatorial optimization problems are solved using a constraint satisfaction approach. Based on computational experience in the literature, constraint satisfaction approaches are compared with well-known operational research (OR) techniques such as integer programming, branch and bound, and simulated annealing.     

An efficient mixed integer programming model for pairing containers in inland transportation based on the assignment of orders

The inland transportation takes a significant portion of the total cost that arises from intermodal transportation. In addition, there are many parties (shipping lines, haulage companies, customers) who share this operation as well as many restrictions that increase the complexity of this problem and make it NP-hard. Therefore, it is important to create an efficient strategy to manage this process in a way to ensure all parties are satisfied. This paper investigates the pairing of containers/orders in drayage transportation from the perspective of delivering paired containers on 40-ft truck and/or individual containers on 20-ft truck, between a single port and a list of customer locations. An assignment mixed integer linear programming model is formulated, which solves the problem of how to combine orders in delivery to save the total transportation cost when orders with both single and multiple destinations exist. In opposition to the traditional models relying on the vehicle routing problem with simultaneous pickups and deliveries and time windows formulation, this model falls into the assignment problem category which is more efficient to solve on large size instances. Another merit for the proposed model is that it can be implemented on different variants of the container drayage problem: import only, import–inland and import–inland–export. Results show that in all cases the pairing of containers yields less cost compared to the individual delivery and decreases empty tours. The proposed model can be solved to optimality efficiently (within half hour) for over 300 orders.     

The hub location and network design problem with fixed and variable arc costs: formulation and dual-based solution heuristic

Many air, less-than-truck load and intermodal transportation and telecommunication networks incorporate hubs in an effort to reduce total cost. These hubs function as make bulk/break bulk or consolidation/deconsolidation centres. In this paper, a new hub location and network design formulation is presented that considers the fixed costs of establishing the hubs and the arcs in the network, and the variable costs associated with the demands on the arcs. The problem is formulated as a mixed integer programming problem embedding a multi-commodity flow model. The formulation can be transformed into some previously modelled hub network design problems. We develop a dual-based heuristic that exploits the multi-commodity flow problem structure embedded in the formulation. The test results indicate that the heuristic is an effective way to solve this computationally complex problem.