A new model for the hazardous waste location-routing problem

Hazardous waste management involves the collection, transportation, treatment and disposal of hazardous wastes. In this paper a new multiobjective location-routing model is proposed. Our model also includes some constraints, which were observed in the literature but were not incorporated into previous models. The aim of the proposed model is to answer the following questions: where to open treatment centers and with which technologies, where to open disposal centers, how to route different types of hazardous waste to which of the compatible treatment technologies, and how to route waste residues to disposal centers. The model has the objective of minimizing the total cost and the transportation risk. A large-scale implementation of the model in the Central Anatolian region of Turkey is presented.     

Liquid-waste injection planning via goal programming

In this paper, applications of linear goal programming techniques to the analysis of design, management and operational problems in the deep-well injection industry are investigated. Deep-well injection is a method of hazardous waste disposal where liquid or liquefiable wastes are pumped under controlled rate and pressure into confined formations that have no other potential use. A typical liquid waste injection system is modeled as a linear goal program to study and resolve the effects of conflicting objectives such as cost, environmental regulations, equipment utilization and waste-water quality requirements before injection.

The model is validated using actual data obtained from a waste-water disposal complex in Odessa, Texas. The goal program is solved for the best disposal system design and operating policies that would not only enable the plant to meet the Environmental Protection Agency's standards, but best minimize capital investment, annual operating expense and deviations from the waste-water quality requirements before injection. Justification of the use of the model is presented by comparing the actual design and operating policies of the system with the optimal design obtained from the solution of the model. This comparison showed 40% savings in favor of the design predicted by the model.     

I/O-efficient join dependency testing,Loomis–Whitney join,and triangle enumeration

We revisit two fundamental problems in database theory. The join-dependency (JD) testing problem is to determine whether a given JD holds on a relation r. We prove that the problem is NP-hard even if the JD involves only relations each of which has only two attributes. The JD-existence testing problem is to determine if there exists any non-trivial JD satisfied by r. We present an I/O-efficient algorithm in the external memory model, which in fact settles the closely related Loomis–Whitney enumeration problem. As a side product, we solve the triangle enumeration problem with the optimal I/O-complexity, improving a recent result of Pagh and Silvestri in PODS'14.     

DELFIN: An efficient deadlock detection tool for CCS processes

Model checking is a formal technique for proving the correctness of a system with respect to a desired behavior. However, deadlock detection via model checking is particularly difficult for the following two problems: (i) the state explosion problem, due to the exponential increase in the size of a finite state model as the number of system components grows; and (ii) the output interpretation problem, as often counter-examples are so long that they are hard to understand. The aim of this paper is to solve both problems by using heuristic-based search strategies. We have realized DELFIN⁺ (DEadLock FINder) a tool supporting efficient deadlock detection in CCS processes. We have used this tool to verify a sample of CCS processes, in order to evaluate the method on them.     

Modelling of atmospheric pollution by explosions

A mathematical model of air pollution by gaseous and particulate substances caused by near-ground explosions is developed. The model describes the evolution of a hot cloud of explosion products and the transport of the contaminants by upward convective flows. A software package APEX (Atmospheric Pollution by EXplosions) is developed for IBM PC/386/486 computers. The capability of the model is demonstrated on two problems - pollution of the atmosphere by fine dispersed particles carried aloft by a nuclear burst cloud (a large-scale thermal) and air pollution after an explosion and release of a hazardous particulate contaminant (a small-scale cloud). The calculated time-dependent evolution of a hot cloud and the transport of the pollutants by the rising thermal are demonstrated. Final distributions of the particulate matter in the atmosphere after the rise of the hot cloud are presented.     

Exact approaches for static data segment allocation problem in an information network

In a large distributed database, data are geographically distributed across several separate servers (or data centers). This helps in distributing load in the access network. It also helps to serve data locally where it is required. There are various approaches based on the granularity of data for efficient data distribution in a communication network. The file allocation problem (FAP) locates files to servers, the segment allocation problem (SAP) locates database segments, and the mirror location problem (MLP) locates replicas of the entire database. The placement of such data to multiple servers can be modeled as an optimization problem. The major decisions influencing optimization involves the location of servers, allocation of content and assignment of users. In this paper, we study the segment allocation problem (SAP), which is also known as the partial mirroring problem. This approach is more tractable than the file allocation problem in realistic cases and also eliminates the overhead of (constant) update costs that is incurred in the mirror location problem. Our contribution is two-fold: Firstly, earlier works on SAP assume pre-defined segments. We build a data partitioning method using well-known facility location models. We quantify the performance of the partitioning method. We show that the method partitions the database within a reasonable limit of error. Secondly, we introduce a new model for the segment allocation problem in which the segments are completely connected to each other by high-bandwidth links and contains a cost benefit for inter-segment traffic flows. We formulate this problem as an MILP and build exact solution approaches to solve large scale problems. We demonstrate some structural properties of the problem that make it solvable, using a Benders decomposition algorithm. Computational results validate the superiority of the decomposition approach.     

A self-stabilizing transformer for population protocols with covering

Developing self-stabilizing solutions is considered to be more challenging and complicated than developing classical solutions, where a proper initialization of the variables can be assumed. Hence, to ease the task of the developers, some automatic techniques have been proposed to design self-stabilizing algorithms. In this paper, we propose an automatic transformer for algorithms in an extended population protocol model. Population protocols is a model that was introduced recently for networks with a large number of resource-limited mobile agents. We use a variant of this model. First, we assume agents having characteristics (e.g., moving speed, communication radius) affecting their intercommunication “speed”, which is reflected by their cover times. Second, we assume the existence of a special agent with an unbounded memory, the base station. The automatic transformer takes as an input an algorithm solving a static problem (and meeting some additional rather natural requirements) and outputs a self-stabilizing algorithm for the same problem. The transformer is built using a re-execution approach (the technique consisting of executing an algorithm repeatedly in order to obtain its self-stabilizing version). We show that in the model we use, a transformer based on such an approach is impossible without the assumption of an unbounded memory agent.     

A Stackelberg game model for construction waste transportation

The transportation of construction and demolition waste (CDW) is an essential problem in waste management. The CDW generated in large construction projects is typically transported by designated trucks. However, the disposal of scattered CDW produced by individuals or small projects is typically not planned, leading to its illegal dumping. Hong Kong aims to recycle scattered CDW by an appointment-recycling mechanism that involves a three-layered transportation network consisting of construction sites, recycling locations, and disposal facilities. In this study, we develop a two-stage Stackelberg game model to minimize the social costs of this three-layered transportation network. In the first stage, we consider the optimal decisions regarding the recycling locations, assigned trucks, and transport routes from the perspective of the government. In the second stage, the delivery routes for small projects are identified based on the government decisions. We transform the proposed model into a single-level integer program (IP) and conduct numerical experiments to demonstrate its efficiency and effectiveness. To the best of our knowledge, this is the first study to focus on the collection of scattered CDW. Thus, our study contributes to the literature on CDW transportation. Moreover, this study helps address a practical problem, i.e., the illegal dumping of scattered CDW, which is a common concern in many cities. In particular, even small amounts of CDW must be appropriately treated because it may contain hazardous materials.     

基于GIS的城市生活垃圾收运管理系统

针对城市生活垃圾快速增长所带来的收运成本增加以及居民生活环境恶化等问题,提出基于地理信息系统(GIS)的垃圾收运管理系统设计方案,包括系统结构、数据库模型等,采用扫描与分支定界相结合的算法实现垃圾收运的调度优化。选择某地区的一部分收运线路进行实验,结果表明,系统能方便有效地进行垃圾管理并减少收运成本。     

A two-dimensional vector packing model for the efficient use of coil cassettes

We consider the problem of efficiently packing steel products, known as coils, into special containers, called cassettes for shipping. The objective is to minimize the number of cassettes used for packing all the given coils where each cassette has capacity limits on both total payload weight and size. We model this problem as a two-dimensional vector packing problem and propose a heuristic. We also analyze the worst-case performance of the proposed algorithm under a special condition which, in fact, holds for the particular real-world case that we handled. Our computational experiment with real production data shows that the proposed algorithm performs quite satisfactorily in practice.     

Modelling PM2.5 impact indicators in Europe: Health effects and legal compliance

Long-term exposure to fine particulate matter (PM_2.5) has been shown to have significant negative impacts on human health. It is estimated that current levels of air pollution shorten the statistical life expectancy of European citizens by several months. The GAINS integrated assessment model calculates shortening of life expectancy from population exposure to PM_2.5 using epidemiologically-derived health impact functions. In addition, GAINS estimates PM_2.5 concentrations at 1875 air quality monitoring stations located in diverse environments ranging from remote background locations to busy street canyons. In this article, different approaches to dealing with the PM_2.5 pollution problem are compared. We assess for the present and future the attainment of EU and WHO air quality standards for PM_2.5 and estimate the loss of life expectancy under different policy scenarios developed for the ongoing revision of the EU Air Quality Legislation.     

Fuzzy parametric programming model for multi-objective integrated solid waste management under uncertainty

Solid waste management is increasingly becoming a challenging task for the municipal authorities due to increasing waste quantities, changing waste composition, decreasing land availability for waste disposal sites and increasing awareness about the environmental risk associated with the waste management facilities. The present study focuses on the optimum selection of the treatment and disposal facilities, their capacity planning and waste allocation under uncertainty associated with the long-term planning for solid waste management. The fuzzy parametric programming model is based on a multi-objective, multi-period system for integrated planning for solid waste management. The model dynamically locates the facilities and allocates the waste considering fuzzy waste quantity and capacity of waste management facility. The model addresses uncertainty in waste quantity as well as uncertainties in the operating capacities of waste management facilities simultaneously. It was observed that uncertainty in waste quantity is likely to affect the planning for waste treatment/disposal facilities more as compared with the uncertainty in the capacities of the waste management facilities. The relationship between increase in waste quantity and increase in the total cost/risk involved in waste management is found to be nonlinear. Therefore, it is possible that a marginal change in waste quantity could increase the total cost/risk substantially. The information obtained from the analysis of modeling results can be effectively used for understanding the effect of changing the priorities and objectives of planning decisions on facility selections and waste diversions.     

A response to a critique of “A novel electronic cash system with trustee-based anonymity revocation from pairing,” by Chen,Chou, Sun and Cho (2011)

We provide a brief rebuttal of Chang (2012), who suggested that there were flaws in our article, Chen et al. (2011), that deserved further investigation and comment. We believe that these criticisms are unfounded, and offer some additional details related to the intractable discrete elliptic curve discrete logarithm problem to further support the case we wish to make.     

The worst-case chip problem

In the system level, adaptive fault diagnosis problem we must determine which components (chips) in a system are defective, assuming the majority of them are good. Chips are tested as follows: Take two chips, say x and y, and have x report whether y is good or bad. If x is good, the answer is correct, but if x is bad, the answer is unreliable. One way to identify all defective chips is to identify a single good chip which can then be used to diagnose the other chips; the chip problem is to identify a single good chip. We show that the chip problem is closely related to a modified majority problem in the worst case and use this fact to obtain upper and lower bounds on algorithms for the chip problem.     

A non-linear optimization model for water treatment planning of a river basin

The primary objective of this study is to develop a long-range planning model for water treatment management of a river basin.

In order to satisfy environmental standards concerning river water pollution, it is fundamentally necessary to construct area-wide sewerages and sewage treatment plants. Hero, an optimal planning problem to construct these public sewage treatment systems is formulated as a non-linear optimization one. In addition the water supply planning problem is taken into consideration by assuming that waste water treated by public tertiary treatment plants can be roused for industrial purposes. The objective function adopted in this model is the total cost of both construction and operation necessary for public sewage treatment systems, and the optimal solution which minimizes this objective function is determined using the generalized reduced gradient (GRG) algorithm.

Through the study of the Yodo river basin in Japan, it is ascertained that various information necessary for planning can be obtained from the model developed in this study.     

Scheduling training for a tank battalion: How to measure readiness

This paper shows how to measure the readiness for a military unit, with the example of a tank battalion. We first show that training readiness can be measured by the minimum makespan of the training schedule. We call this the train-up problem. Second, the effect of a peacetime budget on the training readiness can be calculated by solving the readiness budget problem. Third, the resources required for training can be determined with the readiness capacity problem. To solve these problems, we give a dynamic program (DP) for one unit such as a company, and a column generation algorithm for an aggregate unit such as a battalion.     

Optimizing fuel consumption and pollutant emissions of gasoline-HEV with catalytic converter

Because of more and more stringent vehicle emission standards, Hybrid Electric Vehicles (HEV) are developed. Gasoline-HEV are equipped with 3-Way Catalytic Converter (3WCC). So the energy management systems of such vehicles, which must reduce not only fuel consumption, but also vehicle pollutant emissions, have to consider the 3WCC heating. A pollutant constrained energy management strategy is presented. A 3WCC multi-0D model is built from physical equations, with a good complexity-performances compromise. An off-line optimal strategy allows the joint minimization of pollution and fuel consumption with only one parameter to tune, while considering all the standardized pollutant emissions. This strategy reduces significantly the vehicle emissions for a minor fuel consumption increase and leads to define 3WCC smart heating. Thus an on-line smart heating strategy is implemented in a HyHIL (Hybrid Hardware In the Loop) test bench, reducing the pollutant emissions of the classical charge sustaining strategy by 30% for CO and 10% for NO_X.     

Canonical D. C. programming techniques for solving a convex program with an additional constraint of multiplicative type

We consider a nonconvex programming problem of minimizing a linear functioncx over a convex setX?? ⁿ with an additional constraint ofmultiplicative type \(\prod _{i = 1}^p \psi _i (x) \leqslant 1\) , where the functionsψ _i are convex and positive onX. The main idea of our approach is to transform this problem, by usingp additional variables, into acanonical d.c. programming problem with the special structure that thereverse convex constraint involved does only depend on the newly introduced variables. This special structure suggests modifying certain techniques in d.c. programming in a way that the operations handling the nonconvexity are actually performed in the space of the additional variables. The resulting algorithm works very well whenp is small (in comparison withn).     

Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities

This paper presents an indirect approach to interval type-2 fuzzy logic system modeling to forecaste the level of air pollutants. The type-2 fuzzy logic system permits us to model the uncertainties among rules and the parameters related to data analysis. In this paper, we propose an indirect method to create an interval type-2 fuzzy logic system from a historical data, where Footprint of Uncertainties of fuzzy sets are extracted by implementation of an interval type-2 FCM algorithm and based on an upper and lower value for the level of fuzziness m in FCM. Finally, the proposed model is applied for prediction of carbon monoxide concentration in Tehran air pollution. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to type-1 fuzzy logic systems in terms of two performance indices.