Linear Parametric Sensitivity Analysis of the Constraint Coefficient Matrix in Linear Programs

Sensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an optimal solution are investigated, and the optimal solution is studied on a so-called critical range of the initial data, in which certain properties such as the optimal basis in linear programming are not changed. Linear one-parameter perturbations of the objective function or of the so-called ”right-hand side” of linear programs and their effect on the optimal value is very well known and can be found in most college textbooks on Management Science or Operations Research. In contrast, no explicit formulas have been established that describe the behavior of the optimal value under linear one-parameter perturbations of the constraint coefficient matrix. In this paper, such explicit formulas are derived in terms of local expressions of the optimal value function and intervals on which these expressions are valid. We illustrate this result using two simple examples

Mathematical models for planning support

In this paper we describe how computer systems can provide planners with active planning support, when these planners are carrying out their daily planning activities. This means that computer systems actively participate in the planning process by automatically generating plans or partial plans. Active planning support by computer systems requires the application of mathematical models and solution techniques. In this paper we describe the modeling process in general terms, as well as several modeling and solution techniques. We also present some background information on computational complexity theory, since most practical planning problems are hard to solve. We also describe how several objective functions can be handled, since it is rare that solutions can be evaluated by just one single objective. Furthermore, we give an introduction into the use of mathematical modeling systems, which are useful tools in a modeling context, especially during the development phases of a mathematical model. We finish the paper with a real life example related to the planning process of the rolling stock circulation of a railway operator

The Value of Information in Container Transport: Leveraging the Triple Bottom Line

Planning the transport of maritime containers from the sea port to final destinations while using multiple transport modes is challenged by uncertainties regarding the time the container is released for further transport or the transit time from the port to its final destination. This paper assesses the value of information in container transport in terms of multiple performance dimensions, i.e. logistics costs, reliability, security, and emissions. The analysis is done using a single period model where a decision maker allocates arriving containers to two transport modes (slow, low price, no flexible departure times, versus fast, high price, flexible departure times). We construct a frontier of Pareto optimal decisions under each of the information scenarios and show that these frontiers move in a favorable direction when the level of information progresses. Each of the Pareto frontiers help strike the balance between the aforementioned performance dimensions. The mathematical results are illustrated using two numerical examples involving barge transport and train transport

Joint Design and Pricing of Intermodal Port - Hinterland Network Services: Considering Economies of Scale and Service Time Constraints

Maritime container terminal operating companies have extended their role from node operators to that of multimodal transport network operators. They have extended the gates of their seaport terminals to the gates of inland terminals in their network by means of frequent services of high capacity transport modes such as river vessels (barges) and trains.

How Much is Location Information Worth? A Competitive Analysis of the Online Traveling Salesman Problem with Two Disclosure Dates

In this paper we derive the worst-case ratio of an online algorithm for the Traveling Salesman Problem (TSP) with two disclosure dates. This problem, a variant of the online TSP with release dates, is characterized by the disclosure of a job’s location at one point in time followed by the disclosure of that job’s release date at a later point in time. We present an online algorithm for this problem restricted to the positive real number line. We then derive the worst-case ratio of our algorithm and show that it is best-possible in two contexts – the first, one in which the amount of time between the disclosure events and release time are fixed and equal for all jobs; and a second in which the time between disclosure events va

Intelligent Personalized Trading Agents that facilitate Real-time Decisionmaking for Auctioneers and Buyers in the Dutch Flower Auctions

In this case the Dutch Flower Auctions (DFA) are discussed. The DFA are part of the supply network in which flowers are produced, stocked, and then sold through either mediation or auctioning. This case focuses on the buyers’ and auctioneers’ positions when flowers are traded through auctions. This case deals with the application of personalized agents as part of a Decision Support System which empowers the decision maker. The decision makers discussed in this case are the auctioneers who control the auction process, and the buyers who bid at the clock auction. Agents are defined as software programs that sense their environment and react autonomously on their environment in order to maximize a certain outcome. The agents, as envisioned in this case, are able to determine users’ preferences and based on these preferences agents can proactively make recommendations. Agents as applied to the auction process could empower the auctioneers in their decisions. Another type of agent could empower the buyer, since buyers have the high-pressure task of buying at the clock auction

Collaborative fleet deployment and routing for sustainable transport

Efficient multi-modal transportation in the hinterland of seaport terminals depends on consolidation of container volumes in support of frequent services of high capacity means of transport, such that sustainable multi-modal transport can compete with uni-modal road transport in cost and time. The tactical design of barge scheduled transport services involves fleet selection and routing through the inland waterway network. The resulting network service design should meet expected demand and service time requirements set by the shippers. We develop a tight MILP formulation for the Fleet Size and Mix Vehicle Routing (FSMVRP) especially adapted for the Port-Hinterland multi-modal barge network design. Also, an analytical model is developed to help understand important design trade-offs made. We consider the case of horizontal cooperation of dry port

Planning of Truck Platoons: a Literature Review and Directions for Future Research

A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution

Crowdsourced Delivery: A Dynamic Pickup and Delivery Problem with Ad-hoc Drivers

The trend towards shorter delivery lead-times reduces operational efficiency and increases transportation costs for internet retailers. Mobile technology, however, creates new opportunities to organize the last-mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place to make deliveries. We consider a peer-to-peer platform that automatically creates matches between parcel delivery tasks and ad-hoc drivers. The platform also operates a fleet of backup vehicles to serve the tasks that cannot be served by the ad-hoc drivers. The matching of tasks, drivers and backup vehicles gives rise to a new variant of the dynamic pick-up and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the various subproblems. In order to investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the environment and the behavior of the ad-hoc drivers. The results suggest that the use of ad-hoc drivers has the potential to make the last-mile more cost-efficient and can reduce the system-wide vehicle-miles