计划排产在卷烟生产中的应用研究

文章针对卷烟厂混合型的卷烟生产,采用车间调度问题的方法,系统分析了卷烟生产中卷包和制丝不同生产环节的特点,得到了它们适用的计划模型以及相适应的算法。根据研究开发的计划排产信息系统,通过在武汉卷烟厂的实际应用表明,该计划排产信息系统具有优化排产、计划冲突避免的优点,取得了较好的应用效果。     

A column generation-based approach to solve the preference scheduling problem for nurses with downgrading

The demand for nurses in virtually all western countries has been outpacing the supply for more than a decade. The situation is now at the point where the rules for good practice are being stretched to the limit and patient care is in jeopardy. The purpose of this paper is to present several ideas for maximizing the use of the available staff and to quantify the resultant benefits. Two approaches are investigated for substituting nurses with higher level skills for those with lower level skills when there is sufficient idle time to do so. Idle time is usually due to scheduling constraints and contractual agreements that prevent a hospital from arbitrarily assigning nurses to shifts over the week.When the substitution is skill-related, as it is here, it is often called downgrading. The models that we develop are for preference scheduling, which means that individual preferences are taken into account when constructing monthly rosters. There are several reasons for doing this in today's environment, the most important is the need to boost staff morale and increase retention. The problem is modeled as an integer program and solved with a column generation technique that relies on intelligent heuristics for identifying good candidate schedules. The computations show that high quality solutions, as measured by the reduction in the need for non-unit nurses as well as the degree to which preferences are satisfied, can usually be obtained in a matter of minutes.     

基于供应链规划的生产安排仿真决策

以某电缆生产企业D公司为例,研究其基于供应链整体规划的理念,根据对现实生产中存在的相关生产因素不确定的情况进行数学描述,编写出仿真算法,并用EXCEL构建其数学仿真模型,进而在分析仿真数据的基础上得到合理的仿真结果,并阐述该模型的实用价值和推广意义,为生产安排决策提供参考依据。     

Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles

Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.     

Modeling social,economic, and health perspectives for optimal pandemic policy decision-making

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented ε-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.     

Home care routing and scheduling problem with teams’ synchronization

The demand for home care (HC) services has steadily been growing for two main types of services: healthcare and social care. If, for the former, caregivers' skills are of utter importance, in the latter caregivers are not distinguishable in terms of skills. This work focuses social care and models caregivers' synchronization as a means of improving human resources management. Moreover, in social care services, several visits need to be performed in the same day since patients are frequently alone and need assistance throughout the day. Depending on the patient's autonomy, some tasks have to be performed by two caregivers (e.g. assist bedridden patients). Therefore, adequate decision support tools are crucial for assisting managers (often social workers) when designing operational plans and to efficiently assign caregivers to tasks. This paper advances the literature by 1) considering teams of one caregiver that can synchronize to perform tasks requiring two caregivers (instead of having teams of two caregivers), 2) simultaneously modelling daily continuity of care and teams' synchronization, and 3) associating dynamic time windows to teams' synchronizations introducing scheduling flexibility while minimize service and travel times. These concepts are embedded into a daily routing and scheduling MIP model, deciding on the number of caregivers and on the number and type of teams to serve all patient tasks. The main HC features of the problem, synchronization and continuity of care, are evaluated by comparing the proposed planning with the current situation of a home social care service provider in Portugal. The results show that synchronization is the feature that most increases efficiency with respect to the current situation. It evidences a surplus in working time capacity by proposing plans where all requests can be served with a smaller number of caregivers. Consequently, new patients from long waiting lists can now be served by the “available” caregivers.     

An empirical investigation of scheduling performance criteria

Planning and scheduling significantly influence organizational performance, but literature that pays attention to how organizations could or should organize and assess their planning processes is limited. We extend planning and scheduling theory with a categorization of scheduling performance criteria, based on a three-stage survey research design. Particularly, the results show that, next to schedule quality, the planning process factors timeliness, flexibility, communication, and negotiation are important performance criteria, and especially so in organizations that are faced with high levels of uncertainty. The results suggest that organizational and behavioral aspects of planning and scheduling cannot be mitigated with advanced models and software that solely focus on good schedules. Rather, high quality schedules and high quality scheduling processes need to be facilitated simultaneously to attain high planning and scheduling performance.     

Diverse task scheduling for individualized requirements in cloud manufacturing

Cloud manufacturing (CMfg) has emerged as a new manufacturing paradigm that provides ubiquitous, on-demand manufacturing services to customers through network and CMfg platforms. In CMfg system, task scheduling as an important means of finding suitable services for specific manufacturing tasks plays a key role in enhancing the system performance. Customers’ requirements in CMfg are highly individualized, which leads to diverse manufacturing tasks in terms of execution flows and users’ preferences. We focus on diverse manufacturing tasks and aim to address their scheduling issue in CMfg. First of all, a mathematical model of task scheduling is built based on analysis of the scheduling process in CMfg. To solve this scheduling problem, we propose a scheduling method aiming for diverse tasks, which enables each service demander to obtain desired manufacturing services. The candidate service sets are generated according to subtask directed graphs. An improved genetic algorithm is applied to searching for optimal task scheduling solutions. The effectiveness of the scheduling method proposed is verified by a case study with individualized customers’ requirements. The results indicate that the proposed task scheduling method is able to achieve better performance than some usual algorithms such as simulated annealing and pattern search.     

汽车整车总装车间生产进度信息实时管理系统

在面向订单制造的生产模式下,借助信息化手段提高汽车总装车间生产进度信息透明化与实时化管理能力已成为汽车整车企业的重要发展趋势之一。文章结合MES(Manufacturing Execution System,MES,制造执行系统)思想,提出了一种汽车整车总装车间生产进度信息实时管理系统,研究了该系统的体系结构、功能结构、运行模式及其实现技术,并将其应用于一个汽车整车制造企业,取得了良好效果。     

Robust scheduling for large scale evacuation planning

It is crucial to develop appropriate strategies to reduce the evacuation time in a disaster situation. The negative impact of large scale disasters can be mitigated by proactive and efficient (time optimal) evacuation planning. The present study aims to develop strategies for public transit-based evacuation for better control and reduced congestion. Mathematical models are formulated for both strategic and operational aspects of evacuation planning to result in efficient, optimal evacuation. The study also presents methods to manage the external environment uncertainties, in particular, evacuation demand uncertainty, by providing robust solutions. To test the efficacy of the models, a case study for a radiological accident in a nuclear plant in India is presented. The results of the case study demonstrate that the models can provide live, efficient and robust results during actual emergencies in acceptable time.     

Recent sojourn time results for multilevel processor-sharing scheduling disciplines

Multilevel Processor-Sharing (MLPS) disciplines refer to a family of age-based scheduling disciplines introduced decades ago. A time-discretized version of an MLPS discipline is applied in the scheduler of the traditional UNIX operating system. In recent years, MLPS disciplines have been used to study the way that packet level scheduling mechanisms impact the performance perceived at the flow level in the Internet. Inspired by this latter application, many new sojourn time results have been discovered for these disciplines in the context of the M/G/1 queue. The aim of this paper was to give a consistent overview of these new results. In addition, it points out some intriguing open problems for further research.     

Signed social structure optimization for shift assignment in the nurse scheduling problem

This paper develops a mathematical framework that relies on modern social network analysis theories for treating the nurse team formation and nurse scheduling (shift assignment) problems, accounting for signed social connections. These problems lie in assigning nurses to teams/shifts such that the constraints regarding both the working regulations and nurses preferences are satisfied. Recent research indicates the dependence of nursing team performance on team social structure; however, so far, the social structure considerations have not been explicitly incorporated into the mathematical formulations of the nurse scheduling problem. The presented framework introduces models that quantitatively exploit such dependence. This paper explores instances of Nurse Team Formation Problem (NTFP) and Nurse Scheduling Problem (NSP) incorporating signed social structure with the measures based on such network structures as edges, full dyads, triplets, k-stars, balanced and unbalanced triangles, etc., in directed, signed networks. The paper presents the integer programming formulations for NTFP and NSP, and a problem-specific heuristic that performs variable-depth neighborhood search to tackle NTFP instances with signed social structures. Computational results for a real-world problem instance with 20 nurses are reported. The insights obtained from the presented framework and future research directions are discussed.     

供应商选择问题的模糊机会约束规划模型

论文以模糊机会约束规划的方法处理供应商选择问题（VSP）。在市场需求量为模糊变量,同时成本、质量、交付时间三个目标以一定的置信水平成立的前提下,提供了一种帮助决策者进行供应商供货量分配的方法,并给出模型的混合智能通用算法。     

Bi-objective decision support system for routing and scheduling of hazardous materials

This paper presents a Pareto-based bi-objective optimization of hazardous materials vehicle routing and scheduling problem with time windows and shows its application to a realistic hazardous material logistics instance. A meta-heuristic solution algorithm is also proposed, which returns a set of routing solutions that approximate the frontier of the Pareto optimal solutions based on total scheduled travel time and total risk of whole transportation process. It works in a single-step fashion simultaneously constructing the vehicle route and selecting the optimal paths connecting the routed locations from a set of non-dominated paths obtained in terms of travel time and risk value.     

一个基于Web的供应链生产规划及调度优化框架

供应链中的生产规划及调度对企业的成功至关重要。针对该问题，本文提出了一个基于Web的开放式的优化框架。该框架在缩短了生产规划及调度所需时间的同时，提高了规划和调度的质量。在实际应用中，该框架被证明能有效地提高企业的工作效率和经济效益：     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

考虑客户服务水平的原料采购、生产计划最优化联合决策模型

现代企业愈发注重生产成本的控制和客户服务水平的提高,基于上述两点建立了一个多阶段、考虑价格折扣的采购、生产计划多目标联合决策模型,并构造相应的启发式算法,在实证中具有很高的可操作性。     

A decision model for selecting protected habitat areas within migratory flyways

A decision model is developed for identifying alternative configurations of new protected habitat areas to augment and enhance major global flyways of migratory birds. New habitat areas are selected to fill in the spatial gaps between existing protected stopovers such as wildlife refuges. The methodology is applied to the US portion of the Atlantic flyway in order to develop alternative networks of habitat stepping stones extending from Maine to Florida. The model is formulated as a zero-one program and solutions are derived using a greedy adding heuristic procedure. Two ecological-economic objectives drive the model: maximizing the geographic coverage of the protected stopovers, and maximizing average stopover quality, as represented by an index based on wetland prevalence and land cost. Thirty-five alternative solutions were generated, indicating the tradeoffs between the objectives. These results are expected to be useful to natural resource agencies and conservation organizations in the development of habitat conservation, preservation, and restoration policies and programs.