A generic stochastic model for supply-and-return network design

This paper presents a generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system. Such situations are typical for manufacturing/re-manufacturing type of systems in reverse logistics. The model accounts for a number of alternative scenarios, which may be constructed based on critical levels of design parameters such as demand or returns. We describe a decomposition approach to this model, based on the branch-and-cut procedure known as the integer L-shaped method. Computational results in an illustrative numerical setting show a consistent performance efficiency of the method. Moreover, the stochastic solution features a significant improvement in terms of average performance over the individual scenario solutions. A modeling and solution methodology as presented here can contribute to the efficient solution of network design models under uncertainty for reverse logistics.     

An optimization model for the container pre-marshalling problem

In most container yards around the world, containers are stacked high to utilize yard space more efficiently. In these yards, one major factor that affects their operational efficiency is the need to re-shuffle containers when accessing a container that is buried beneath other containers. One way to achieve higher loading efficiency is to pre-marshal the containers in such a way that it fits the loading sequence. In this research, we present a mathematical model for the container pre-marshalling problem. With respect to a given yard layout and a given sequence that containers are loaded onto a ship, the model yields a plan to re-position the export containers within the yard, so that no extra re-handles will be needed during the loading operation. The optimization goal is to minimize the number of container movements during pre-marshalling. The resulting model is an integer programming model composed of a multi-commodity flow problem and a set of side constraints. Several possible variations of the model as well as a solution heuristic are also discussed. Computation results are provided.     

A two-phase optimization procedure for integer programming problems

This paper presents a simple two-phase method for optimizing integer programming problems with a linear or nonlinear objective function subject to multiple linear or nonlinear constraints. The primary phase is based on a variation of the method of steepest descent in the feasible region, and a hem-stitching approach when a constraint is violated. The secondary phase zeros on the optimum solution by exploring the neighborhood of the suboptimum found in the first phase of the optimization process. The effectiveness of this method is illustrated through the optimization of several examples. The results from the proposed optimization approach are compared to those from methods developed specially for dealing with integer problems. The proposed method is simple, easy to implement yet very effective in dealing with a wide class of integer problems such as spare allocation, reliability optimization, and transportation problems.     

实时系统的面向方面模型

基于UML的实时系统面向方面模型,能够把实时关注从系统中分离出来,形成一个独立于系统的时间方面,实现时间方面的并发设计和系统时间特性的统一管理。面向方面编程（AOP）技术允许把设计好的时间方面根据特定需要重新织入系统,组合为实时系统。模型扩展了UML来表达AOP技术和时间概念,并从系统的静态结构模型、动态行为模型和时间方面的织入等几部分建模实时系统。一个电梯控制系统例子充分说明这种设计过程。     

面向可信嵌入式系统的随机实时任务能耗优化

针对可信嵌入式系统应用中将任务的最坏情况下的执行时间(WCET)作为任务的实际执行时间,导致系统资源的极大浪费的问题,提出了一种基于随机任务概率模型的方法。首先,考虑任务执行时间具有特定概率分布,并且任务具有不错过其死限的概率(NDVP)需求,同时考虑了动态电压和频率调整(DVFS)对系统可靠性的影响,利用该技术降低能耗。然后,基于动态规划算法,提出了一种具有多项式运行时间的优化算法,并进一步设计了状态剔除规则降低算法运行开销。仿真表明,所提算法与最坏执行时间模型下的最优算法相比,系统能耗降低了30%以上。实验结果表明,考虑任务的随机执行时间能在保证系统可靠性的同时大大节约系统资源。     

Decision rules for ambulance scheduling decision support systems

This paper studies some decision rules for ambulance scheduling. The scheduling decision rules embedded in the decision support systems for emergency ambulance scheduling consider the criteria on the average response time and the percentage of the ambulance requests that are responded within 15 min, which is usually ignored in traditional scheduling policies. The challenge in designing the decision rules lies in the stochastic and dynamic nature of request arrivals, fulfillment processes, and complex traffic conditions as well as the time-dependent spatial patterns of some parameters complicate the decisions in the problem. To illustrate the proposed decision rules’ usage in practice, a simulator is developed for performing some numerical experiments to validate the effectiveness and the efficiency of the proposed decision rules.     

Integer linear programming optimization of joint RRM policies for heterogeneous wireless systems

Wireless systems will be characterized by the coexistence of heterogeneous Radio Access Technologies (RATs) with different, but also complementary, performance and technical characteristics. These heterogeneous wireless networks will provide network operators the possibility to efficiently and coordinately use the heterogeneous radio resources, for which novel Joint Radio Resource Management (JRRM) policies need to be designed. In this context, this work proposes and evaluates a JRRM policy that simultaneously determines for each user an adequate combination of RAT and number of radio resources within such RAT to guarantee the user/service QoS requirements, and efficiently distribute the radio resources considering a user fairness approach aimed at maximizing the system capacity. To this aim, the JRRM algorithm, which takes into account the discrete nature of radio resources, is based on integer linear programming optimization mechanisms.     

Dependent-chance programming: A class of stochastic optimization

This paper provides a theoretical framework of dependent-chance programming, as well as dependent-chance multiobjective programming and dependent-chance goal programming which are new types of stochastic optimization. A stochastic simulation based genetic algorithm is also designed for solving dependent-chance programming models.     

A novel hybrid method for improving ambulance dispatching response time through a simulation study

Response time is the most important factor in evaluating the performance of various Emergency Medical Services (EMS). In this paper, a novel hybrid method has been proposed to improve response time for ambulance dispatching. The proposed approach uses a linear hybrid metric based on network centrality measures, nearest neighbor method and first-in first-out (FIFO) policy. Other important parameters in ambulance dispatching such as the operating environment, rate of incoming emergency calls, available resources, hospitalization probability of the patients as well as distances and locations of units are all part of information used in this proposed approach. In line with the traditional metrics used in previous works, we have adopted a linear combined metric which is adjusted according to environment parameters. Results of extensive simulation experiments show reductions in response time by as much as 42% as compared to previous methods.     

Mathematical modeling for optimal design of in-building distributed antenna systems

In-building Distributed Antenna System (IB-DAS) has proven to be one of the most promising In-Building Solutions (IBS) to provide coverage and capacity for indoor users. We consider optimal deployment of the passive IB-DAS, focusing on mathematical optimization models based on integer programming, for the topology design and optimal equipment selection of IB-DAS. The models minimize the cable cost and keep the transmit power at each antenna within a given interval defined for coverage and interference control. The models can deliver optimal solutions to systems of which the size is of practical relevance. To improve the time efficiency, we develop preprocessing techniques that integrate the building layout data into the system modeling. Application of the models to realistic IB-DAS deployment demonstrates the effectiveness of the models.     

Developing software for generating pouring schedules for steel foundries

The authors have been involved in an industry-wide project whose goal is to improve the production systems of steel foundries. As a part of this effort, we have studied the process by which the foundries develop their daily pouring schedule. Typically, the foundries are unable to consider the impact of their pouring schedule on downstream operations because developing a feasible schedule is a labor-intensive task involving numerous constraints. To address this, the authors are working with Harrison Steel to develop software that can both automate the current scheduling process and improve it by estimating the impact of the pouring schedule on downstream work-in-process (WIP) inventory levels. An integer programming model is described that minimizes a comprehensive cost function that includes the costs of pattern tooling set-up, late delivery, WIP inventory, and under-utilization of assets. The software implements a heuristic that finds multiple solutions to this integer program, each of which corresponds to a feasible schedule. Computational tests reveal that the software is capable of handling realistically sized scheduling problems in a reasonable amount of time. Upon its completion, this system could be extended for implementation at many other steel foundries.     

A modified particle swarm optimization for aggregate production planning

Particle swarm optimization (PSO) originated from bird flocking models. It has become a popular research field with many successful applications. In this paper, we present a scheme of an aggregate production planning (APP) from a manufacturer of gardening equipment. It is formulated as an integer linear programming model and optimized by PSO. During the course of optimizing the problem, we discovered that PSO had limited ability and unsatisfactory performance, especially a large constrained integral APP problem with plenty of equality constraints. In order to enhance its performance and alleviate the deficiencies to the problem solving, a modified PSO (MPSO) is proposed, which introduces the idea of sub-particles, a particular coding principle, and a modified operation procedure of particles to the update rules to regulate the search processes for a particle swarm. In the computational study, some instances of the APP problems are experimented and analyzed to evaluate the performance of the MPSO with standard PSO (SPSO) and genetic algorithm (GA). The experimental results demonstrate that the MPSO variant provides particular qualities in the aspects of accuracy, reliability, and convergence speed than SPSO and GA.     

Stage- and scenario-wise Fenchel decomposition for stochastic mixed 0-1 programs with special structure

Solving stochastic integer programs (SIPs) is generally difficult. This paper considers a comparative study of stage- and scenario-wise Fenchel decomposition (FD) for two-stage SIPs with special structure. The standard FD approach is based on stage-wise or Benders’ decomposition. This work derives a scenario FD method based on decomposing the SIP problem by scenario and performs a computational study of the two approaches. In particular, two algorithms are studied, stage-wise FD (ST-FD) and scenario-wise FD (SC-FD) algorithms. The algorithms use FD cuts generated based on the scenario subproblem under each decomposition setting to iteratively recover (partially) the convex hull of integer points in the neighborhood of the optimal solution. The L-shaped method is used to solve the LP relaxation of the SIP problem in the ST-FD algorithm, while the progressive hedging algorithm (PHA) is used in the SC-FD algorithm. Computational results on knapsack test instances demonstrate the viability of both approaches towards solving large instances in reasonable amount of time and outperforming a direct solver in most cases. Overall, the ST-FD algorithm provides the best performance in our experiments.     

A note on integer-valued radial model in DEA

This note shows that the input targets of proposed model by Kuosmanen and Kazemi Matin (2009) (http://dx.doi.org/10.1016/j.ejor.2007.09.040 and http://dx.doi.org/10.1016/j.omega.2008.11.002) may not be less than the input targets of proposed model by Lozano and Villa (2006) (http://dx.doi.org/10.1016/j.cor.2005.02.031).     

Methods based on discrete optimization for finding road network rehabilitation strategies

We study a dynamic optimization problem arising in the (long-term) planning of road rehabilitation activities. In this area one seeks a pavement resurfacing plan for a road network under budget constraints. Our main approach is to model this as an integer programming problem with underlying dynamic programming structure. We investigate properties of this model and propose a solution method based on Lagrangian relaxation where one gets subproblems that are shortest path problems. Some computational experiences based on realistic data are reported.     

A two-stage stochastic programming model for electric energy producers

The bilateral contract selection and bids definition constitute a strategic issue for electric energy producers that operate in competitive markets, as the liberalized electricity ones. In this paper we propose a two-stage stochastic integer programming model for the integrated optimization of power production and trading which include a specific measure accounting for risk management. We solve the model by means of a novel enumerative solution approach that exploits the particular problem structure. Finally, we report some preliminary computational experiments.     

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.