Scheduling a single machine with concurrent jobs for the frozen food industry

This study examines the air blast freezing process of the frozen food industry, which processes multiple products with variable processing rates. The analysis depicts a new, single machine-scheduling problem in which the machine can process multiple jobs concurrently, within its capacity. The machine processes independent jobs arriving at various times while incurring interruption costs when allowing the jobs to enter or leave the machine. A mixed integer linear programming (MILP) model and a heuristic algorithm are developed for scheduling, the objectives of which are to minimize the costs associated with machine activities including that of waiting to load, waiting to unload and interruption time. The heuristic algorithm demonstrates the high potential of the computational time savings by obtaining the solution within one-fifth of the mathematical model computational time.     

Efficient approaches for materialized views selection in a data warehouse

View materialization is an effective method to increase query efficiency in a data warehouse and improve OLAP query performance. However, one encounters the problem of space insufficiency if all possible views are materialized in advance. Reducing query time by means of selecting a proper set of materialized views with a lower cost is crucial for efficient data warehousing. In addition, the costs of data warehouse creation, query, and maintenance have to be taken into account while views are materialized. In this paper, we propose efficient algorithms to select a proper set of materialized views, constrained by storage and cost considerations, to help speed up the entire data warehousing process. We derive a cost model for data warehouse query and maintenance as well as efficient view selection algorithms that effectively exploit the gain and loss metrics. The main contribution of our paper is to speed up the selection process of materialized views. Concurrently, this will greatly reduce the overall cost of data warehouse query and maintenance.     

Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks

This article presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss–Markov process. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as a difference-of-convex program and apply the convex-concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and a model for 2D target tracking by a drone swarm for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity-promoting, and an effective sensor selection heuristic.     

ELRP多目标优化模型及其混合算法

以时间效益最大化为主要目标、成本最小化为次要目标,考虑灾后纵多不确定因素,基于系列假设和约束条件构建ELRP多目标优化模型;采用先定位分配,再安排路线的思路,首先根据时间窗、距离和路阻等因素进行应急中转站定位和救援点分配,再设计ACO-GA混合启发式算法进行全局和局部路径寻优;运用SOLOMON标准测试数据测试模型和算法的可行性,最后将其用于求解以江苏地震灾害为背景的仿真实例。研究结果表明,优化模型和改进算法具有较好性能,解的质量和稳定性有明显改进,其运算结果可以作为地方政府应急救援决策的理论支持。     

The Data Broadcast Problem with Non-Uniform Transmission Times

The Data Broadcast Problem consists of finding an infinite schedule to broadcast a given set of messages so as to minimize a linear combination of the average service time to clients requesting messages, and of the cost of the broadcast. This problem also models the Maintenance Scheduling Problem and the Multi-Item Replenishment Problem. Previous work concentrated on a discrete-time restriction where all messages have transmission time equal to 1. Here, we study a generalization of the model to a setting of continuous time and messages of non-uniform transmission times. We prove that the Data Broadcast Problem is strongly NP -hard, even if the broadcast costs are all zero, and give 3-approximation algorithms.     

Load balancing of parallelized information filters

We investigate the data-parallel implementation of a set of "information filters" used to rule out uninteresting data from a database or data stream. We develop an analytic model for the costs and advantages of load rebalancing for the parallel filtering processes, as well as a quick heuristic for its desirability. Our model uses binomial models of the filter processes and fits key parameters to the results of extensive simulations. Experiments confirm our model. Rebalancing should pay off whenever processor communications costs are high. Further experiments showed it can also pay off even with low communications costs for 16-64 processes and 1-10 data items per processor; then, imbalances can increase processing time by up to 52 percent in representative cases, and rebalancing can increase it by 78 percent, so our quick predictive model can be valuable. Results also show that our proposed heuristic rebalancing criterion gives close to optimal balancing. We also extend our model to handle variations in filter processing time per data item     

An adaptive large neighborhood search heuristic for the share-a-ride problem

The Share-a-Ride Problem (SARP) aims at maximizing the profit of serving a set of passengers and parcels using a set of homogeneous vehicles. We propose an adaptive large neighborhood search (ALNS) heuristic to address the SARP. Furthermore, we study the problem of determining the time slack in a SARP schedule. Our proposed solution approach is tested on three sets of realistic instances. The performance of our heuristic is benchmarked against a mixed integer programming (MIP) solver and the Dial-a-Ride Problem (DARP) test instances. Compared to the MIP solver, our heuristic is superior in both the solution times and the quality of the obtained solutions if the CPU time is limited. We also report new best results for two out of twenty benchmark DARP instances.     

Selection of views to materialize in a data warehouse

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In This work, we have developed a theoretical framework for the general problem of selection of views in a data warehouse. We present polynomial-time heuristics for a selection of views to optimize total query response time under a disk-space constraint, for some important special cases of the general data warehouse scenario, viz.: 1) an AND view graph, where each query/view has a unique evaluation, e.g., when a multiple-query optimizer can be used to general a global evaluation plan for the queries, and 2) an OR view graph, in which any view can be computed from any one of its related views, e.g., data cubes. We present proofs showing that the algorithms are guaranteed to provide a solution that is fairly close to (within a constant factor ratio of) the optimal solution. We extend our heuristic to the general AND-OR view graphs. Finally, we address in detail the view-selection problem under the maintenance cost constraint and present provably competitive heuristics.     

Scheduling with dependent setups and maintenance in a textile company

This paper deals with a scheduling problem with machine maintenance in a textile company. In the production system, the sequence-dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. This study presents a heuristic to minimize the completion time, or equivalently the total setup time subject to periodic maintenance and due dates. The performance of the heuristic is evaluated by comparing its solution with the solution of the branch-and-bound algorithm. The real data are used to demonstrate the effectiveness of the heuristic.     

A Simulation Based Restricted Dynamic Programming approach for the Green Time Dependent Vehicle Routing Problem

This paper addresses a Green Time Dependent Capacitated Vehicle Routing Problem that accounts for transportation emissions. The problem has been formulated and solved using Dynamic Programming approach. The applicability of Dynamic Programming in large sized problems is, however, limited due to exponential memory and computation time requirements. Therefore, we propose a generic heuristic approach, Simulation Based Restricted Dynamic Programming, based on weighted random sampling, the classical Restricted Dynamic Programming heuristic and simulation for the model to solve large sized instances. These decision support tools can be used to aid logistics decision-making processes in urban distribution planning. The added values of the proposed model and the heuristic have been shown based on a real life urban distribution planning problem between a pharmaceutical warehouse and a set of pharmacies, and ten relatively larger instances. The results of the numerical experiments show that the Simulation Based Restricted Dynamic Programming heuristic can provide promising results within relatively short computation times compared to the classical Restricted Dynamic Programming for the Green Time Dependent Capacitated Vehicle Routing Problem. The Simulation Based Restricted Dynamic Programming algorithm yields 2.3% lower costs within 93.1% shorter computation times on average, compared to the classical Restricted Dynamic Programming. Moreover, the analyses on the effect of traffic congestion in our base case reveal that 2.3% benefit on total emissions and 0.9% benefit on total routing cost could be obtained if vehicles start delivery after heavy congested period is passed.     

A system for the design of packet-switched communication networks with economic tradeoffs

This paper studies the problem of designing a packet-switched communication network with tradeoffs between link costs and response time to users. It consists of assigning capacities to links in the network and determining the routes used by messages for all communicating node pairs in order to minimize total link fixed and variable costs. A tradeoff between link costs and response time to users is achieved by including a constraint that sets an upper limit on the average link queueing delay in the network. The topology of the network and the end-to-end traffic requirements are given. The problem is formulated as a nonlinear integer programming model. Unlike most of previous models, where the best route for a communicating node pair is restricted to a set of prespecified candidate routes, our model considers all possible routes for every communicating node pair. An efficient heuristic based on a Lagrangean relaxation of the problem is developed to generate feasible solutions. The results of extensive computational experiments across a variety of previously used networks are reported. These results indicate that the solution procedure is effective for a wide range of traffic loads and cost structures.     

考虑机器检修的热轧钢管批量计划方法

本文从无缝钢管生产实际中提取并定义了周期性机器检修环境下的钢管热轧批量计划问题,基于无缝钢管生产的特殊性,将该问题抽象为一类考虑机器检修和机器调整时间的单机调度问题,并建立了以最小化机器闲置和机器调整时间为目标的数学模型.针对批量间的机器调整时间取决于钢管规格的变化这一特性,提出了最小调整时间排序规则,证明了该规则在不考虑检修计划时具有最优性.进而,以此为基础建立了循环求解框架,并设计了两阶段启发式算法.基于实际生产数据设计了多种问题规模的实验,验证了算法的有效性,并从实际应用角度对结果进行了分析.     

启发式列生成算法求解带恶化效应的同构并行机调度问题

针对实际生产中广泛存在的一类带恶化效应的同构并行机调度问题,以最小化最大完工时间为优化目标,构建该问题的整数规划模型,并提出一种启发式列生成算法(HCGA)进行求解.在HCGA中,首先,利用Dantzig-Wolfe分解方法,将原问题分解为一个主问题(MP)和多个子问题;然后,设计启发式算法获得初始列,其中每列为一台机器上的一个调度方案,基于初始列构建限制主问题(RMP)模型;接着,设计快速有效的动态规划算法求解子问题,以得到需添加至RMP的列集,同时,考虑传统列生成算法收敛速度较慢,设计一系列方法来加速列生成过程;最后,基于所获取的MP线性松弛解,设计深潜启发式算法确定原问题的整数解.HCGA与商用求解器GUROBI的对比实验结果表明,HCGA可在较短时间内获得更优的解.     

The Data Broadcast Problem with Non-Uniform Transmission Times

Abstract. The Data Broadcast Problem consists of finding an infinite schedule to broadcast a given set of messages so as to minimize a linear combination of the average service time to clients requesting messages, and of the cost of the broadcast. This problem also models the Maintenance Scheduling Problem and the Multi-Item Replenishment Problem. Previous work concentrated on a discrete-time restriction where all messages have transmission time equal to 1. Here, we study a generalization of the model to a setting of continuous time and messages of non-uniform transmission times. We prove that the Data Broadcast Problem is strongly NP -hard, even if the broadcast costs are all zero, and give 3 -approximation algorithms.     

考虑时间窗与随机航行时间的船舶航线规划

良好的航线设计是船运公司降低运营成本,提高服务质量的关键。船舶运输中存在航行时间不确定,码头资源稀缺等特点,基于此将其航线问题抽象为考虑时间窗与随机旅行时间的多重流动旅行商问题。针对航行时间的随机性,设计了线性近似方法,提出了虚拟时间窗的概念,构建了初始模型与修正模型;给出了该问题的一个算例,验证了模型与算法的有效性和合理性。     

A heuristic for the vehicle routing problem with due times

This paper studies the vehicle routing problem with due times. The vehicles are supposed to visit customers within the due times, and a penalty cost is imposed in case the vehicle arrives past the due times. The objective is to minimize the weighted sum of the traveling time of vehicles and the tardiness of the service customers receive. A mixed integer programming formulation and a heuristic based on the tabu search for a practical use are suggested. Route-perturb and route-improvement method for the neighborhood generation is proposed. Performances are compared with other heuristics appeared in the literature using the bench-mark data set modified to be fit to the model. It is shown that the suggested heuristic gives a good solution in a short computation time.     

Cost effective storage space for data cubes

View materialization is one of the most important techniques applied in multidimensional databases. The problem of selecting a set of views for materialization that minimizes queries response time under storage space constraint received significant attention over last twenty years. Many researchers concentrate on designing better view selection methods with respect to the running time or the cost of the solution. This paper summarizes our research on the problem of how much space should be allocated for views materialization to ensure good queries performance. In order to comprehensively investigate the problem and minimize the influence of untypical cases, the experiments described in this paper were done on the large data set, including large data cubes, rarely considered in previous papers. In particular, the relation between the number of data cube views and the space limit expressed as a percentage of the fully materialized data cube size and a multiple of the base view size is analysed. According to our experimental results, the allocation of large space for views materialization is not cost effective.     

Fuzzy resource‐constrained project scheduling using taboo search algorithm

This paper proposes a mathematical model to deal with project scheduling problem under vagueness and a framework of a heuristic approach to fuzzy resource‐constrained project scheduling problem (F‐RCPSP) using heuristic and metaheuristic scheduling methods. Our approach is very simple to apply, and it does not require knowing the explicit form of the membership functions of the fuzzy activity times. We first identify two typical activity priority rules, namely, resource over time and minimum slack priority rules. They are used in the F‐RCPS problem and in the initial solution of Taboo search (TS) method. We improved the TS algorithm method for the solution of F‐RCPSP. Our objective is to check the performance of these rules and metaheuristic method in minimizing the project completion time for the F‐RCPS problems. In our study, we use trapezoidal fuzzy numbers (TraFNs) for activity times and activity‐on‐nodes (AON) representation and compute several project characteristics such as earliest, latest, and slack times in terms of TraFNs. The computational experiment shows that the performance of the proposed TS is better than the evaluation and light beam search algorithms in the literature. © 2012 Wiley Periodicals, Inc.     

Problem decomposition in distributed problem-solving systems

Distributed Problem Solving (DPS) is defined as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers (agents), each of them knowing how to execute only some of the necessary tasks. This approach considers the problem-solving process as occurring in three phases: problem decomposition, subproblem solution, and answer synthesis. In the problem decomposition phase, one has to determine which tasks will be executed by each agent and when. One of the key research questions in the problem decomposition process is how to decompose a problem in order to minimize the cost of resources needed for its solution. In this article, we construct mathematical programming models in order to describe the decomposition process under the above criterion, study its complexity, and present exact and heuristic algorithms for its solution. Our work was motivated by the operation of an actual system that can be considered as a distributed problem solver for the assessment of irrigation projects design.