Policy Sequencing and the Development of Rice Value Chains in Senegal

In response to the world food crisis in 2008, Senegal developed a productivist national food self‐sufficiency programme. However, the critical question is not whether the programme can meet its ambitious target of self‐sufficiency in rice production by 2015, but, if it does, how will domestic rice reach urban markets, where consumers generally prefer imported rice for its superior grain quality. Information collected through interviews and a stakeholder workshop advances the argument that policy sequencing will be crucial in order to upgrade Senegalese rice value chains progressively. Any large‐scale investments in productivity will need to be preceded by investments in post‐harvest grain‐quality infrastructure before sector‐wide marketing strategies can be adopted that enhance the chain competitiveness of domestic relative to imported rice.     

Beta approximation of ratio distribution and its application to next generation sequencing read counts

Paired sequencing data are commonly collected in genomic studies to control biological variation. However, existing data processing strategies suffer at low coverage regions, which are unavoidable due to the limitation of current sequencing technology. Furthermore, information contained in the absolute values of the read counts is commonly ignored. We propose a read count ratio processing/modification method, to not only incorporate information contained in the absolute values of paired counts into one variable, but also mitigate the discrete artifact, especially when both counts are small. Simulation shows that the processed variable fits well with a Beta distribution, thus providing an easy tool for down-stream inference analysis.     

下料问题与运输问题联合优化建模

本文研究了考虑子材运输的标准一维下料问题。建立了由生产商负责运输时,标准一维下料与运输协调优化整数规划模型,最小化母材使用成本,子材库存成本及子材运输成本。采用拉格朗日松弛技术对有关约束进行松弛和模型分解,设计基于序列规则和FFD规则的混合启发式算法求解模型。该算法由两部分组成,分别用于求解标准一维下料子问题和卖方运输子问题。通过随机产生的1800个算例,验证模型合理性与算法的有效性。与基于列生成法的两阶段算法解进行比较,平均总成本降低了17.57%,表明集成算法优于两阶段算法。     

Sequencing heuristics for dependent setups in a continuous process industry

In this paper we address the scheduling of Biaxially Oriented Polypropylene (BOPP) film production, which belongs to the plastics industry and is a continuous process. Scheduling problems in BOPP film production involve processing different thickness settings of films on a common facility. Customer orders of the same thickness setting are usually grouped together as a production batch and a setup time is incurred whenever the batch is switched. However, there is usually more than one batch for the same thickness setting because of the due date constraints of the orders. Therefore, there is a need to develop a batching and sequencing scheme that minimizes the total setup time, or equivalently the makespan, under the due date constraint. In this paper heuristics are developed to generate a near-optimal solution for the problem. The heuristics are evaluated by an optimal solution procedure and are tested by using the actual data from a plant making the BOPP film. Comparison of the heuristics with the results of the current system has demonstrated a significant reduction in total setup time.     

A note on the complexity of the problem of two-agent scheduling on a single machine

We consider a two-agent scheduling problem on a single machine, where the objective is to minimize the total completion time of the first agent with the restriction that the number of tardy jobs of the second agent cannot exceed a given number. It is reported in the literature that the complexity of this problem is still open. We show in this paper that this problem is NP-hard under high multiplicity encoding and can be solved in pseudo-polynomial time under binary encoding. When the first agent's objective is to minimize the total weighted completion time, we show that the problem is strongly NP-hard even when the number of tardy jobs of the second agent is restricted to be zero.     

Dynamic Policies for Optimal LEO Satellite Launches

Low‐earth orbit satellite (LEO) systems continue to provide mobile communication services. The issue of cost containment in system maintenance is a critical factor for continued operation. Satellite finite life‐times follow a stochastic process, and since satellite replenishment cost is the most significant on‐going cost of operation, finding optimal launch policies is of paramount importance. This paper formulates the satellite launch problem as a Markovian decision model that can be solved using dynamic programming. The policy space of the system is enormous and traditional action space dominance rules do not apply. In order to solve the dynamic program for realistic problem sizes, a novel procedure for limiting the state space considered in the dynamic program is developed. The viability of the proposed solution procedure is demonstrated in example problems using realistic system data. The policies derived by the proposed solution procedure are superior to those currently considered by LEO system operators, and result in substantial annual cost savings.     

NERJIT: Using Net Requirement Data in Kanban‐Controlled Jumbled‐Flow Shops

The bold lines that have separated the application of specific production planning and control techniques to specific production systems are being blurred by continuous advances in production technologies and innovative operational procedures. Oral communication among dispatchers and production units has given way to electronic communication between production planners and these units by continuous progress in information technologies. Current production literature alludes to the idea that, collectively, these advances have paved the way for application of Just‐In‐Time (JIT) production concepts, which were originally developed for mass production systems, in intermittent production systems. But this literature does not actually consider the possibility. This article presents a modification to JIT procedures to make them more suitable for jumbled‐flow shops. This article suggests providing real‐time information about net‐requirements for each product to each work center operator for setting production priorities at each work center. Simulation experiments conducted for this study show that using Net‐Requirements in JIT (NERJIT) reduces customer wait time by 45–60% while reducing inventory slightly. The analysis of work centers’ input and output stock‐point inventories shows that using the information about net‐requirements results in production of items that are in current demand. NERJIT results in smaller input stock‐point inventory and availability of products with higher priority in the output stock‐points of work centers.     

基于JIT装配模式的网上超市订单分拣优化模型

订单分拣是大型网上超市生存和发展的关键环节，如何在数百万种商品中将订单要求的商品高效、准确地进行拣选并包装，是亟待解决的难题。本文以提高大型网上超市订单分拣方法的科学性为目标，针对订单拣选、集货复核、包装这一作业流程，基于JIT装配流水线思想，建立大型网上超市订单成组分拣的优化模型与求解方法。在构建订单分拣流程中各工序作业时间计算模型的基础上，建立了订单分批与排序的联合优化模型。围绕缩减求解空间的思想提出两阶段启发式优化方法。通过应用实例分析和灵敏度分析证明了算法的有效性。结果表明，本文提出的网上超市订单成组分拣方法相较于传统方法可较大幅度提高订单分拣的效率，为网上零售企业的订单拣选和包装作业流程的优化提供了理论指导。     

马克思把握历史过程的双向考察方法

所谓双向考察方法,是指依据历史过程的走向而展开的不同向度的把握历史过程的方法,它包括逆向考察和顺向考察两类共四种。它们分别是从“现在”向“过去”和从“未来”向“现在”的逆向考察,从“现在”向“未来”和从“过去”向“现在”的顺向考察。马克思运用该方法,分别对历史过程中的自然次序、因果关系和发展次序进行了考察,而非单纯由果溯因的“从后思索”。在建构其理论体系、进行社会批判中,双向考察方法发挥了重要作用。     

跨境电商企业海外仓选址多目标优化研究

海外仓作为一带一路战略环境下的一种新型跨境物流模式,具有运输时间短、退换货快等特点。本文在跨境电商的背景下,综合考虑建仓成本、运输费用、运输时间和税费等因素,提出了海外仓选址多目标优化模型,随后基于分层序列法设计了二分搜索-最小费用流算法对该模型进行求解,并证明该算法在多项式时间内可以得到最优解,最后通过算例证实了该模型和算法的有效性和实用性,这为跨境电商海外仓选址提供了理论依据和决策支持。