The robust single machine scheduling problem with uncertain release and processing times

In this work, we study the single machine scheduling problem with uncertain release times and processing times of jobs. We adopt a robust scheduling approach, in which the measure of robustness to be minimized for a given sequence of jobs is the worst-case objective function value from the set of all possible realizations of release and processing times. The objective function value is the total flow time of all jobs. We discuss some important properties of robust schedules for zero and non-zero release times, and illustrate the added complexity in robust scheduling given non-zero release times. We propose heuristics based on variable neighborhood search and iterated local search to solve the problem and generate robust schedules. The algorithms are tested and their solution performance is compared with optimal solutions or lower bounds through numerical experiments based on synthetic data

Managing inventory in global supply chains facing port-of-entry disruption risks

Ports-of-entry are critical components of the modern international supply chain infrastructure, particularly container seaports and airfreight hubs. The potential operational and economic impact resulting from their temporary closure is unknown, but is widely believed to be very significant. This paper investigates one aspect of this potential impact, focusing specifically on the use of supply chain inventory as a risk mitigation strategy for a one supplier, one customer system in which goods are transported through a port-of-entry subject to temporary closures. Closure likelihood and duration are modeled using a completely observed, exogenous Markov chain. Order lead times are dependent on the status of the port-of-entry, including potential congestion backlogs of unprocessed work. An infinite-horizon, periodic-review inventory control model is developed to determine the optimal average cost ordering policies under linear ordering costs with backlogged demand. When congestion is negligible, the optimal policy is state invariant. In the more complex case of non-negligible congestion, this result no longer holds. For studied scenarios, numerical results indicate that operating margins may decrease 10% for reasonable-length port-of-entry closures, that margins may be eliminated completely without contingency plans, and expected holding and penalty costs may increase 20% for anticipated increases in port-of-entry utilization

Motor Carrier Service Network Design

This chapter introduces service network design (SND) operations research models and solution methodologies specifically focused on problems that arise in the planning of operations in the trucking, or motor freight, industry. Consolidation carriers such as less-than-truckload and package trucking companies face flow planning problems to decide how to route freight between transfer terminals, and load planning problems to decide how to consolidate shipments into trailerloads and containerloads for dispatch. Integer programming models are introduced for these network design decision problems as well as exact and heuristic solution methods

Evaluation of an expanded satellite based mobile communications tracking system

Since the terrorist events in the United States on September 11, 2001, the Federal Motor Carrier Safety Administration has been testing and evaluating cargo tracking technologies to improve the safety, security, and efficiency of commercial motor vehicle operations. While satellite-based systems used for tracking vehicles and cargo provide sufficient geographic coverage in the majority of the United States, there remain several vital regions that are uncovered and difficult to monitor. One such region is Alaska, where officials arc particularly concerned with the hazardous materials shipments that are transported parallel to the Trans-Alaska Pipeline. This article analyzes the risks and benefits associated with adopting an Expanded Satellite-Based Mobile Communications Tracking System to monitor hazardous materials and high-value cargo in Alaska. Technical and acceptance risks are evaluated against the communication, safety, security and real time information benefits that the system provides. The findings indicate that the system provides a significant communications upgrade relative to previously available technology

Sampling Scenario Set Partition Dual Bounds for Multistage Stochastic Programs

We consider multistage stochastic programming problems in which the random parameters have finite support, leading to optimization over a finite scenario set. There has been recent interest in dual bounds for such problems, of two types. One, known as expected group subproblem objective (EGSO) bounds, require solution of a group subproblem, which optimizes over a subset of the scenarios, for all subsets of the scenario set that have a given cardinality. Increasing the subset cardinality in the group subproblem improves bound quality, (EGSO bounds form a hierarchy), but the number of group subproblems required to compute the bound increases very rapidly. Another is based on partitions of the scenario set into subsets. Combining the values of the group subproblems for all subsets in a partition yields a partition bound. In this paper, we consider partitions into subsets of (nearly) equal cardinality. We show that the expected value of the partition bound over all such partitions also forms a hierarchy. To make use of these bounds in practice, we propose random sampling of partitions and suggest two enhancements to the approach: Sampling partitions that align with the multistage scenario tree structure and use of an auxiliary optimization problem to discover new best bounds based on the values of group subproblems already computed. We establish the effectiveness of these ideas with computational experiments on benchmark problems. Finally, we give a heuristic to save computational effort by ceasing computation of a partition partway through if it appears unpromising.

Stable Matching for Dynamic Ride-sharing Systems

Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide the most convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly-stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance

The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles and individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules may substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers