Adjusted 0-1 Knapsack Problem in Cargo Flow by Using Artificial Bee Colony Algorithm

This study describes the problem with the knapsack that occurred in the cargo flow. The problem of the knapsack is the problem of optimisation used to illustrate the problem and the solution in which each set of items has its own specific value and weight. With its total value as much as possible, the number of items that may become less or at least equal to or equal to the limit. Therefore, the aim of this study is to determine the minimum total cost of 30 shipments based on volume by using Artificial Bee Colony (ABC) algorithm in order to achieving the highest profit. ABC algorithm was derived from the bee colony which consists of four phases of initialisation, employed bees, onlooker bees and scout bees. Based on the result obtained, the total cost of the shipment is 402.377 tons per km which starting from the Shipment 25 with 2 560 000 tons per year for 0.111 tons per km and ends with Shipment 21 with 2 250 000 tons per year for 0.129 tons per km

Optimization Models for Cost Efficient and Environmentally Friendly Supply Chain Management

This dissertation aims to provide models which will help companies make sustainable logistics management and transportation decisions. These models are extensions of the economic lot sizing model with the availability of multiple replenishment modes. The objective of the models is to minimize total replenishment costs and emissions. The study provides applications of these models on contemporary supply chain problems. Initially, the impact of carbon regulatory mechanisms on the replenishment decisions are analyzed for a biomass supply chain under fixed charge replenishment costs. Then, models are extended to consider multiple-setups replenishment costs for age dependent perishable products. For a cost minimization objective, solution algorithms are proposed to solve cases where one, two or multiple replenishment modes are available. Finally, using a bi-objective model, tradeoffs in costs and emissions are analyzed in a perishable product supply chain

Optimization-Based Energy Management for Multi-energy Maritime Grids

This open access book discusses the energy management for the multi-energy maritime grid, which is the local energy network installed in harbors, ports, ships, ferries, or vessels. The grid consists of generation, storage, and critical loads. It operates either in grid-connected or in islanding modes, under the constraints of both power system and transportation system. With full electrification, the future maritime grids, such as all-electric ships and seaport microgrids, will become “maritime multi-energy system” with the involvement of multiple energy, i.e., electrical power, fossil fuel, and heating/cooling power. With various practical cases, this book provides a cross-disciplinary view of the green and sustainable shipping via the energy management of maritime grids. In this book, the concepts and definitions of the multi-energy maritime grids are given after a comprehensive literature survey, and then the global and regional energy efficiency policies for the maritime transportation are illustrated. After that, it presents energy management methods under different scenarios for all-electric ships and electrified ports. At last, the future research roadmap are overviewed. The book is intended for graduate students, researchers, and professionals who are interested in the energy management of maritime transportation

Heuristically Driven Search Methods for Topology Control in Directional Wireless Hybrid Network

Information and Networked Communications play a vital role in the everyday operations of the United States Armed Forces. This research establishes a comparative analysis of the unique network characteristics and requirements introduced by the Topology Control Problem (also known as the Network Design Problem). Previous research has focused on the development of Mixed-Integer Linear Program (MILP) formulations, simple heuristics, and Genetic Algorithm (GA) strategies for solving this problem. Principal concerns with these techniques include runtime and solution quality. To reduce runtime, new strategies have been developed based on the concept of flow networks using the novel combination of three well-known algorithms; knapsack, greedy commodity filtering, and maximum flow. The performance of this approach and variants are compared with previous research using several network metrics including computation time, cost, network diameter, dropped commodities, and average number of hops per commodity. The results conclude that maximum flow algorithms alone are not quite as effective as previous findings, but are at least comparable and show potential for larger networks

An Advanced Tabu Search Approach to Solving the Mixed Payload Airlift Load Planning Problem

This paper presents a new tabu search based two-dimensional bin packing algorithm which produces high quality solutions to the Mixed Payload Airlift Load Planning (MPALP) problem using C-5 and C-17 aircraft. This algorithm, called Mixed Payload Airlift Load Planning Tabu Search (MPALPTS), surpasses previous research conducted in this area because, in addition to pure pallet cargo loads, MPALPTS can accommodate rolling stock cargo (i.e. tanks, trucks, HMMMVs, etc.) while still maintaining aircraft feasibility with respect to aircraft center of balance, mandatory cargo separations, aircraft floor structural limitations, etc. Furthermore, while this research is currently restricted to C-5 and C-17 aircraft, MPALPTS is capable of modeling nearly any type of cargo aircraft and requires a limited number of assumptions thereby making it applicable to operational missions. To demonstrate its effectiveness, the load plans generated by MPALPTS are directly compared to those generated by the Automated Air Load Planning Software (AALPS) for a given cargo set; AALPS is the load planning software currently mandated for use in all Department of Defense load planning. While more time consuming than AALPS, MPALPTS required the same or fewer aircraft than AALPS in all test scenario

Making decisions about screening cargo containers for nuclear threats using decision analysis and optimization

One of the most pressing concerns in homeland security is the illegal passing of weapons-grade nuclear material through the borders of the United States. If terrorists can gather the materials needed to construct a nuclear bomb or radiological dispersion device (RDD, i.e., dirty bomb) while inside the United States, the consequences would be devastating. Preventing plutonium, highly enriched uranium (HEU), tritium gas or other materials that can be used to construct a nuclear weapon from illegally entering the United States is an area of vital concern. There are enormous economic consequences when our nation\u27s port security system is compromised. Interdicting nuclear material being smuggled into the United States on cargo containers is an issue of vital national interest, since it is a critical aspect of protecting the United States from nuclear attacks. However, the efforts made to prevent nuclear material from entering the United States via cargo containers have been disjoint, piecemeal, and reactive, not the result of coordinated, systematic planning and analysis. Our economic well-being is intrinsically linked with the success and security of the international trade system. International trade accounts for more than thirty percent of the United States economy (Rooney, 2005). Ninety-five percent of international goods that enter the United States come through one of 361 ports, adding up to more than 11.4 million containers every year (Fritelli, 2005; Rooney, 2005; US DOT, 2007). Port security has emerged as a critically important yet vulnerable component in the homeland security system. Applying game theoretic methods to counterterrorism provides a structured technique for defenders to analyzing the way adversaries will interact under different circumstances and scenarios. This way of thinking is somewhat counterintuitive, but is an extremely useful tool in analyzing potential strategies for defenders. Decision analysis can handle very large and complex problems by integrating multiple perspectives and providing a structured process in evaluating preferences and values from the individuals involved. The process can still ensure that the decision still focuses on achieving the fundamental objectives. In the decision analysis process value tradeoffs are evaluated to review alternatives and attitudes to risk can be quantified to help the decision maker understand what aspects of the problem are not under their control. Most of all decision analysis provides insight that may not have been captured or fully understood if decision analysis was not incorporated into the decision making process. All of these factors make decision analysis essentially to making an informed decision. Game theory and decision analysis both play important roles in counterterrorism efforts. However, they both have their weaknesses. Decision analysis techniques such as probabilistic risk analysis can provide incorrect assessments of risk when modeling intelligent adversaries as uncertain hazards. Game theory analysis also has limitations. For example when analyzing a terrorist or terrorist group using game theory we can only take into consideration one aspect of the problem to optimize at a time. Meaning the analysis is either analyzing the problem from the defenders perspective or from the attacker’s perspective. Parnell et al. (2009) was able to develop a model that simultaneously maximizes the effects of the terrorist and minimizes the consequences for the defender. The question this thesis aims to answer is whether investing in new detector technology for screening cargo containers is a worthwhile investment for protecting our country from a terrorist attack. This thesis introduces an intelligent adversary risk analysis model for determining whether to use new radiological screening technologies at our nation’s ports. This technique provides a more realistic risk assessment of the true situation being modeled and determines whether it is cost effective for our country to invest in new cargo container screening technology. The optimal decision determined by our model is for the United States to invest in a new detector, and for the terrorists to choose agent cobalt-60, shown in Figure 18. This is mainly due to the prominence of false alarms and the high costs associated with screening all of these false alarms, and we assume for every cargo container that sounds an alarm, that container is physically inspected. With the new detector technology the prominence of false alarms decreases and the true alarm rate increases, the cost savings associated with this change in the new technology outweighs the cost of technical success or failure. Since the United States is attempting to minimize their expected cost per container, the optimal choice is to invest in the new detector. Our intelligent adversary risk analysis model can simultaneously determine the best decision for the United States, who is trying to minimize the expected cost, and the terrorist, who is trying to maximize the expected cost to the United States. Simultaneously modeling the decisions of the defender and attacker provides a more accurate picture of reality and could provide important insights to the real situation that may have been missed with other techniques. The model is extremely sensitive to certain inputs and parameters, even though the values are in line with what is available in the literature, it is important to understand the sensitivities. Two inputs that were found to be particularly important are the expected cost for physically inspecting a cargo container, and the cost of implementing the technology needed for the new screening device. Using this model the decision maker can construct more accurate judgments based on the true situation. This increase in accuracy could save lives with the decisions being made. The model can also help the decision maker understand the interdependencies of the model and visually see how his resource allocations affect the optimal decisions of the defender and the attacker