Tiravat Assavapokee

A Capacitated Network Flow Optimization Approach for Short Notice Evacuation Planning

We present a capacity constrained network flow optimization approach for finding evacuation paths, flows and schedules so as to maximize the total evacuees for short notice evacuation planning (SNEP). Due to dynamic nature of this optimization problem, we first construct a time-expanded network that expands the static network over the planning horizon for every time interval. Since the resulting evacuation networks become extremely large to solve, we have developed Evacuation Scheduling Algorithm (ESA) to expedite the solution process. ESA utilizes Dijkstra’s algorithm for finding the evacuation paths and a greedy algorithm for finding the maximum flow of each path and the schedule to execute the flow for each time interval. We show that the complexity of ESA is O(|Nc|·n2)+O(|Nc|·m·T). Numerical experiments show a tremendous advantage of ESA over an exact algorithm (CCEP) in computation time by running up to 41,682 faster than CCEP. In many test network instances, CCEP failed to find a solution within 12hours while ESA converged to a solution in less than 0.03seconds.

Planning the e-Scrap Reverse Production System Under Uncertainty in the State of Georgia: A Case Study

Due to legislative requirements, environmental concerns, and market image, the disposition of end-of-life e-scrap is attracting tremendous attention in many parts of the world today. Effective management of returned used product flows can have a great impact on the profitability and resulting financial viability of associated e-scrap reverse production systems. However, designing efficient e-scrap reverse production systems is complicated by the high degree of uncertainty surrounding several key factors. Very few examples of this complex design problem are documented in the academic literature. This paper contributes as analysis of a new, large-scale application that designs an infrastructure to process used televisions, monitors, and computer central processing units (CPUs) in the state of Georgia in the U.S. The case study employs a scenario-based robust optimization model for supporting strategic e-scrap reverse production infrastructure design decisions under uncertainty. A mixed integer linear programming (MILP) model is used to maximize the system net profit for specified deterministic parameter values in each scenario, and then a min-max robust optimization methodology finds a robust solution for all of the scenarios

Scenario relaxation algorithm for finite scenario-based min-max regret and min-max relative regret robust optimization

Most practical decision-making problems are compounded in difficulty by the degree of uncertainty and ambiguity surrounding the key model parameters. Decision makers may be confronted with problems in which no sufficient historical information is available to make estimates of the probability distributions for uncertain parameter values. In these situations, decision makers are not able to search for the long-term decision setting with the best long-run average performance. Instead, decision makers are searching for the robust long-term decision setting that performs relatively well across all possible realizations of uncertainty without attempting to assign an assumed probability distribution to any ambiguous parameter. In this paper, we propose an iterative algorithm for solving min-max regret and min-max relative regret robust optimization problems for two-stage decision-making under uncertainty (ambiguity) where the structure of the first-stage problem is a mixed integer (binary) linear programming model and the structure of the second-stage problem is a linear programming model. The algorithm guarantees termination at an optimal robust solution, if one exists. A number of applications of the proposed algorithm are demonstrated. All results illustrate good performance of the proposed algorithm.

Reverse production system infrastructure design for electronic products in the state of Texas

Rapid technology advances have shortened the lifecycle of electronic products, resulting in the increasing number of discarded products in recent years. Due to the growing environmental concerns, several state governments have passed new regulations in order to reduce the amount of waste stream, to divert the discarded products from landfills, and to dispose the retired electronic products properly. As a result, an effective reverse logistics infrastructure is required to support the product recovery activities. In this paper, we propose a solution methodology for designing the infrastructure of the reverse production system by utilizing the mixed integer linear programming (MILP) model. A case study for designing the reverse production system in the state of Texas is also presented. Statistical analyses are carefully utilized to estimate design parameters in the case study from the available historical information from previous studies. Finally, discussions, recommendations, and insight information in designing and operating the reverse production system are presented.

Thromboembolism in High-Risk Aesthetic Surgery: Experience With 17 Patients in a Review of 3871 Consecutive Cases

BACKGROUND Pulmonary embolism (PE) represents the third most frequent cause of postoperative death in the United States. In recent years, there has been an increasing demand among plastic surgeons for patient safety guidelines that specifically address the complications of deep venous thrombosis (DVT) and PE in relation to aesthetic surgery. OBJECTIVE In this study, we review 3871 consecutive major body contouring procedures performed over the last 8 years in our surgery center in an attempt to identify common factors that could have contributed to the onset of DVT/PE in 17 of these patients. METHODS We conducted a retrospective chart review to identify common factors associated with the occurrence of DVT/PE in high risk patients who undergo aesthetic surgery. RESULTS Among these patients, we calculated the following incidence rates: 0.46% for DVT and 0.08% for PE. We discovered that a culmination of factors working synergistically play a significant role in the development of DVT/PE. CONCLUSIONS We conclude that a carefully planned, comprehensive, appropriately enforced protocol is necessary to reduce the rate of thromboembolic events. Practical safety measures and technical recommendations are presented that strongly encourage the use of thromboprophylaxis during the pre-, intra-, and postoperative phases of aesthetic surgical procedures. We feel that DVT and PE prevention should involve a partnership between patient and surgeon.

Modelling the supply chain swap problem in the petroleum industry

This paper is motivated by the need for developing well-structured methodologies and mechanisms that enable an efficient coordination of swap/exchange transactions among same-level supply chain partners in the petroleum and energy industries. A mathematical model that can capture the complexity in the swap transactions between two companies is presented. The model is then applied to a real case study from the petrochemicals industry, and the solutions of the model are evaluated and their performances are compared to those of current industry practices.

A Relative Robust Optimization Approach for Full Factorial Scenario Design of Data Uncertainty and Ambiguity

This chapter presents a relative robust optimization algorithm for two-stage decision making under uncertainty (ambiguity) where the structure of the first-stage problem is a mixed integer linear programming model and the structure of the second-stage problem is a linear programming model. In the structure of the considered problem, each uncertain parameter can take its value from a finite set of real numbers with unknown probability distribution independently of other parameters’ settings. This structure of parametric uncertainty is referred to in this chapter as the full-factorial scenario design of data uncertainty. The algorithm is shown to be efficient for solving large-scale relative robust optimization problems under this structure of the parametric uncertainty. The algorithm coordinates three computational stages to efficiently solve the overall optimization problem. Bi-level programming formulations are the main components in two of these three computational stages. The main contributions of this chapter are the theoretical development of the robust optimization algorithm and its applications in robust strategic decision making under uncertainty (e.g., supply chain network infrastructure design problems).

Simulation-Based Optimization: A Case Study for Airline's Cargo Service Call Center

In this chapter, we introduce the basic concept of the simulation-based optimization and illustrate its usefulness and applicability for generating the manpower planning of airlines cargo service call center. Because of the continuous increase in oil prices, and combined with many other factors, the airline industry is currently facing new challenges to keep its customers satisfied. One of the most important drivers of the customer satisfaction is the customer service. The excellent customer service can give an airline company the edge over its competitors. Airline companies need to insure the appropriate level of staffing at their service call centers in order to maintain a high level of customer satisfaction with the appropriate level of the overall cost. With the high level of uncertainty in the customer demand and a number of complicated factors in the problem, it becomes necessary to apply the simulation-based optimization technique to help managers generate the efficient staffing policy for the airlines cargo service call center. In this work, the technique called reinforcement learning and Markov decision process are used to build and solve the mathematical model to determine the appropriate staffing policy at the airlines cargo service call center on the monthly basis. Simulation and optimization models are incorporated together so as to solve the overall problem. The results of the case study are thoroughly analyzed, discussed, and compared with the current staffing policies. All results illustrate the impressive performance of the recommended staffing policies over the current staffing policies.