Shengbin Wang

Operations scheduling with multiple resources and transportation considerations

We study a scheduling problem with the operations that require renewable as well as non-renewable resources. After an operation has been completed, the non-renewable resource is depleted whereas the renewable resource can be made available for the next operation. Of both the renewable and the non-renewable resources limited amounts are available and they need to be transported to the locations where they are needed. The operations have deadlines, and the availability of the renewable resources depends on the sequence of the operations. Such operations scheduling problems are commonly encountered in the practices of emergency logistics that deliver medical services to the affected areas after a disaster, where renewable resources typically refer to medical teams and non-renewable resources refer to medical supplies. We present a complexity classification for our problem and show where the borderline lies between NP-hardness and polynomial time solvability. We analyse the structural properties of our problem, provide strongly polynomial-time solutions for four special cases and list the cases that are computationally intractable. Finally, we propose a framework of heuristic procedures for solving more general versions of this problem.

Personnel scheduling and supplies provisioning in emergency relief operations

The practice of emergency operations often involves the travelling of medical teams and the distribution of medical supplies. In an emergency, such as an earthquake, a medical team often has to visit various hospitals (the customers) one after another in a predetermined sequence in order to perform on-site operations that require certain amounts of medical supplies. Because of their perishable nature, the medical supplies are typically shipped in batches from upstream suppliers and kept at multiple distribution centers during the disaster relief process. The scheduling of the medical teams and the provisioning of the medical supplies give rise to a scheduling problem that involves the timely dispatching of supplies from distribution centers to hospitals in coordination with the scheduling of medical teams so as to minimize the total tardiness of the completions of the operations to be performed. We introduce a mathematical programming based rolling horizon heuristic that is able to find near optimal solutions for networks of up to 80 hospitals very fast. We also report on empirical observations with regard to the computational performance of the heuristic; we consider 5420 randomly generated test cases as well as a case that is based on an actual hospital-distribution center network in the greater New York metropolitan area. Managerial insights are drawn from numerical studies regarding the benefits of pre-positioning medical supplies at the distribution centers.

A Survey of Privacy-Aware Supply Chain Collaboration: From Theory to Applications

ABSTRACT: In the contemporary information era, the ubiquitous collection of data from different parties frequently accommodates significant mutual benefits to the involved participants. However, data is a double-bladed sword. Inappropriate access or use of data by the recipients may pose serious privacy issues that explicitly harm the data owners. In the past decade, swiftly increasing privacy concerns arise in many business processes such as supply chain management. How to protect the private information of different participants in the supply chain has become a key multidisciplinary research problem in information systems, production and operations management, computer science, and mathematics. Specifically, in the real world, manufacturers, distributors, and retailers commonly collaborate with each other to cater to the demands of supplying and marketing. In their traditional cooperation, all the parties completely share their proprietary information so as to jointly optimize their operations (e.g., ma...

On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions

In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive–reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive–reactive approach and the algorithm hybridization strategy.

An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem

The classical model of vehicle routing problem VRP generally minimizes either the total vehicle travelling distance or the total number of dispatched vehicles. Due to the increased importance of environmental sustainability, one variant of VRPs that minimizes the total vehicle fuel consumption has gained much attention. The resulting fuel consumption VRP FCVRP becomes increasingly important yet difficult. We present a mixed integer programming model for the FCVRP, and fuel consumption is measured through the degree of road gradient. Complexity analysis of FCVRP is presented through analogy with the capacitated VRP. To tackle the FCVRP’s computational intractability, we propose an efficient two-objective hybrid local search algorithm TOHLS. TOHLS is based on a hybrid local search algorithm HLS that is also used to solve FCVRP. Based on the Golden CVRP benchmarks, 60 FCVRP instances are generated and tested. Finally, the computational results show that the proposed TOHLS significantly outperforms the HLS.

Urgent Epidemic Control Mechanism for Aviation Networks

In the current century, the highly developed transportation system can not only boost the economy, but also greatly accelerate the spreading of epidemics. While some epidemic diseases may infect quite a number of people ahead of our awareness, the health care resources such as vaccines and the medical staff are usually locally or even globally insufficient. In this research, with the network of major aviation routes as an example, we present a method to determine the optimal locations to allocate the medical service in order to minimize the impact of the infectious disease with limited resources. Specifically, we demonstrate that when the medical resources are insufficient, we should concentrate our efforts on the travelers with the objective of effectively controlling the spreading rate of the epidemic diseases.

Operations scheduling with renewable and non-renewable resources

OF THE DISSERTATION II