A multi-phase oscillated variable neighbourhood search algorithm for a real-world open vehicle routing problem

The aim of this study is to solve the newspaper delivery optimization problem for a media delivery company in Turkey by reducing the total cost of carriers. The problem is modelled as an open vehicle routing problem (OVRP), which is a variant of the vehicle routing problem. A variable neighbourhood search-based algorithm is proposed to solve a real-world OVRP. The proposed algorithm is tested with varieties of small and large-scale benchmark suites and a very large-scale real-world problem instance. The results of the proposed algorithm provide either the best known solution or a competitive solution for each of the benchmark instances. The algorithm also improves the real-world company’s solutions by more than 10%.     

Strategies for minimizing the effects of late truck arrivals on newspaper distribution systems

The current trend of using advanced computer technology to monitor and control newspaper distribution has brought into focus problems are unique to the newspaper industry. The distribution system not only has to deal with the rigid deadlines within which the newspaper must reach its destination, but it must also take into account and then react in an optimal manner to the uncertainties involved in such a complex operation. The complexity of the problem is multiplied when the distribution operation involves a multi-plant operation where random events in production control the distribution in a way necessitating inter-plant transfer of products. The inter-plant transfer in turn increases the sensitivity of the distribution process to the random events in truck traffic management at each plant.This paper deals with the effect of random truck arrivals on the distribution system as predicted by a model previously described. Using various scenarios of truck arrivals that emulate multiple trips by single trucks, effects of inclement weather or other traffic disrupting conditions, and rescheduling of production between plants and the associated truck rerouting; strategies for truck control are investigated that can be used to minimize disruptions in newspaper production. These strategies involve preproduction planning of truck loading orders based on truck size and uniqueness of product, the use of “sink” trucks to handle unexpected overflow, and the effectiveness of using back up distribution systems often found in newspaper plants as means for buffering production while awaiting late trucks.     

Integrated production and outbound distribution scheduling problems with job release dates and deadlines

In this paper, we study an integrated production and outbound distribution scheduling model with one manufacturer and one customer. The manufacturer has to process a set of jobs on a single machine and deliver them in batches to the customer. Each job has a release date and a delivery deadline. The objective of the problem is to issue a feasible integrated production and distribution schedule minimizing the transportation cost subject to the production release dates and delivery deadline constraints. We consider three problems with different ways how a job can be produced and delivered: non-splittable production and delivery (NSP–NSD) problem, splittable production and non-splittable delivery problem and splittable production and delivery problem. We provide polynomial-time algorithms that solve special cases of the problem. One of these algorithms allows us to compute a lower bound for the NP-hard problem NSP–NSD, which we use in a branch-and-bound (B&B) algorithm to solve problem NSP–NSD. The computational results show that the B&B algorithm outperforms a MILP formulation of the problem implemented on a commercial solver.     

一个版面智能调度与输出系统的设计与实现

报业的集团化趋势和规模化经营促使报纸印刷业务不断扩大,大型报社需要输出的版面作业成倍增长、需要同时控制的输出系统种类和数量不断增加,用手工方式进行输出控制已经不能满足用户的需要。该文通过抽象调度参数和调度规则的定义,提出了报纸版面作业的智能调度与自动输出策略,实现了一个适用于报业集团与大型印务中心的版面作业智能调度与输出系统。该系统还结合实际应用情况,解决了输出系统的自动监控、版面作业参数的远程规划与实时传递、版面数据的自动接收、版面作业状态监控等问题。     

TEXTUAL ARTICLE CLUSTERING IN NEWSPAPER PAGES

In the analysis of a newspaper page an important step is the clustering of various text blocks into logical units, i.e., into articles. We propose three algorithms based on text processing techniques to cluster articles in newspaper pages. Based on the complexity of the three algorithms and experiments on actual pages from the Italian newspaper L'Adige, we select one of the algorithms as the preferred choice to solve the textual clustering problem.     

Using genetic algorithm for lot sizing and scheduling problem with arbitrary job volumes and distinct job due date considerations

This paper considers an integrated lot sizing and scheduling problem for a production–distribution environment with arbitrary job volumes and distinct due dates considerations. In the problem, jobs are firstly batch processed on a batching machine at production stage and then delivered to a pre-specified customer at the subsequent delivery stage by a capacitated vehicle. Each job is associated with a distinct due date and a distinct volume, and has to be delivered to the customer before its due date, i.e. delay is not allowed. The processing time of a batch is a constant independent of the jobs it contains. In production, a constant set-up time as well as a constant set-up cost is required before the first job of this batch is processed. In delivery, a constant delivery time as well as a constant delivery cost is needed for each round-trip delivery between the factory and the customer. Moreover, it is supposed that a job that arrives at the customer before its due date will incur a customer inventory cost. The objective is to find a coordinated lot sizing and scheduling scheme such that the total cost is minimised while guaranteeing a certain customer service level. A mixed integer formulation is proposed for this problem, and then a genetic algorithm is developed to solve it. To evaluate the performance of the proposed genetic algorithm, a lower bound on the objective value is established. Computational experiments show that the proposed genetic algorithm performs well on randomly generated problem instances.     

Complexity results for an integrated single machine scheduling and outbound delivery problem with fixed sequence

In this paper, we consider an integrated production and outbound delivery scheduling problem. In particular, we address the situation in which the scheduling sequence and the delivery sequence are the same and predefined. A set of jobs are processed on a single machine, and finished jobs are delivered to the customers by a single capacitated vehicle. Each job has a processing time, and transportation times between customers are taken into account. Because the sequence is given, the problem consists in forming batches of jobs and our objective is to minimize the sum of the delivery times or general functions of the delivery times. The NP-hardness of the general problem is established, and a pseudopolynomial time dynamic programming algorithm is given. Some particular cases are treated, for which NP-hardness proofs and polynomial time algorithms are given. Finally, a fixed-parameter tractability result is given.     

Newspaper demand prediction and replacement model based on fuzzy clustering and rules

A problem that most newspaper companies encounter daily is how to predict the right number of newspapers to print and distribute among distinct selling points. The aim is to predict newspaper demand as accurately as possible to meet customer need and decrease loss, the number of newspaper offered but not sold. The right amount depends of the newspaper demand at different selling points and is a function of the geographical location and customer profile. Currently, demand prediction is based on values experienced in the past and on management knowledge. This paper suggests the use of predictive data mining techniques as a systematic approach to explore newspaper company database and improve predictions. Predictions require accurate forecast of the daily newspaper amount needed at each selling point. The focus of the paper is on a prediction method that uses fuzzy clustering for data base exploration and fuzzy rules together with performance scores of selling points for prediction. Experimental results using actual data show that the method is effective when compared with the current methodology, neural network-based predictors, and autoregressive forecasters. In particular, the predictive data mining technique improves on average 10% in comparison with the use of the existing approaches.     

计算机网络信息安全及防护策略初探

网络信息安全是报社内外网互联互通的重要保障.信息时代下,报社必须加强网络信息安全管理,建立完善的网络安全体系,保证报社网络系统的正常运行,及时发现、预防、处理异常网络安全问题,融合技术手段和行政手段,以报社网络信息安全为目标,加快报社网络安全建设,切实落实安全防护措施.本文对计算机网络信息安全建设与信息安全防护策略进行探讨.     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

Integrated production and delivery with single machine and multiple vehicles

This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.     

A coordinated scheduling of production-and-delivery under dynamic delivery cost environments

This paper considers a coordination scheduling of production and delivery, where delivery cost depends on time period for delivery, but not dependent on individual jobs. The objective is to find a coordinated production-and-delivery schedule to minimize sum of the scheduling cost (either one of makespan, sum of completion times, or maximum lateness) and delivery cost. The problem is proved to be strongly NP-hard for all the objective measures tested. Some restricted cases of the problem are also characterized for their complexities, for which the associated dynamic programming algorithms are derived.     

Hybrid simulation and MIP based heuristic algorithm for the production and distribution planning in the soft drink industry

This study addresses the production and distribution planning problem in the soft drink industry. The problem involves the allocation of production volumes among the different production lines in the manufacturing plants, and the delivery of products to the distribution centers (DCs). A mixed integer linear programming (MILP) model is developed for the problem.     

基于库存和在线生产的仓库发货配置方法

针对离散型加工装配企业不同订单复杂产品的零部件发货配置最优化问题,提出了一种全新的仓库发货配置方法. 它以按时交货和订单损失最小作为评价准则,以当前的生产状态为基础,找出当前产品加工和装配的工艺中余留最长的生产路线,并结合库存状态和订单的重要程度确定发货的优先程度,最终分别通过整单出厂和分步出厂两种发货模式实现了零部件的最优发货配置.     

Seeds of Cross-Media Production

We present an empirical study of an organization that has recently moved from traditional newspaper production towards cross-media production involving the integrated digital production of newspaper, television, radio and web-news. The paper focuses on the daily production rhythms of the media separately and of cross-media production. Since cross-media production is in the making, we study the instruments currently used for planning and coordination, and analyze them as seeds that will eventually make cross-media production happen. Time and timing are important in news production in general, and our analyses focus on the rhythm of the daily planning, coordination and production processes. Specifically, we analyze the temporal coordination of the activities in and around the Superdesk – the current center of coordination of the news organization, and of the persons who work specifically with planning and coordination. We demonstrate how the production rhythms of the individual media collide with that of cross-media, and how product lifecycle rhythms add to the list of causes of problems that may jeopardize cross-media production. We propose to strengthen planning and overview support elements of the Superdesk, and the main new coordinator role of the organization. We point out how the media rhythms of newspaper in particular must be backgrounded. To achieve cross-media production, the starting point must be to strengthening and developing further the cross-media rhythms, rather than supplementing individual media rhythms.

Unbounded parallel-batch scheduling with family jobs and delivery coordination

We study a scheduling problem that integrates parallel-batch production with family jobs and job delivery at the same time. The jobs are first processed on an unbounded parallel-batch machine and then delivered in batches to their specified customers by a transportation vehicle. We assume that jobs from different families (customers) cannot be processed together by the batch machine and also transported together by the vehicle. The objective is to minimize the time when the vehicle finishes delivering the last delivery batch to its customer and returns to the machine. We first show that the problem is NP-hard, and then propose for it a heuristic algorithm with a worst-case performance ratio of 3/2.     

Advertising channel selection in a segmented market

We consider a market with a finite number of segments and assume that several advertising channels are available, with different diffusion spectra and efficiencies. The problem of the choice of an advertising channel to direct the pre-launch campaign for a new product is analyzed in two steps. First, an optimal control problem is solved explicitly in order to determine the optimal advertising policy for each channel. Then a maximum profit channel is chosen. In a simulation example we consider the choice of a newspaper among six available and analyze the relations among the firm target market and the advertising channels environment which induce the optimal decision.     

Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm

This paper addresses the stochastic production demand problem in a manufacturing company. The objective of this research is to minimize the waiting time of production workstations and reduce stochastic production material problems through coordinating pickup and delivery orders in a warehouse. RFID technology is adopted to visualize the actual status of operations in production and warehouse environments. A mathematical model is developed to address this problem and a meta-heuristic algorithm using genetic algorithm (GA) is also developed to improve performance. Computational experiments are undertaken to examine the performance of the algorithm when dealing with congestion in cases of heavy and normal demand for production material. The overall result shows that the algorithm efficiently minimizes the total makespan of the production shop floor.     

Lot-sizing and scheduling problem with earliness tardiness and setup penalties

We consider the problem of determining the production lot sizes and their schedules so that the sum of setup cost, holding cost, and tardy cost is minimized. There are n orders waiting to be processed. Each order has its own due date, earliness penalty and tardiness penalty. The production is done in lots, which is the manufacturing of the same product continuously. No early delivery is allowed. Each order is delivered once, on the due date or immediately after the production of the order is completed. We present an algorithm, based on the use of the assignment problem, to optimally solve the problem with zero setup cost. For the problem with setup cost, the Backward Search algorithm is proposed to solve the small size problem. To deal with the large size problem, two heuristics which are Sequencing with Optimal Timing and Genetic Algorithm are presented.     

Web newspaper layout optimization using simulated annealing

The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths.