Retail store scheduling for profit

In spite of its tremendous economic significance, the problem of sales staff schedule optimization for retail stores has received relatively scant attention. Current approaches typically attempt to minimize payroll costs by closely fitting a staffing curve derived from exogenous sales forecasts, oblivious to the ability of additional staff to (sometimes) positively impact sales. In contrast, this paper frames the retail scheduling problem in terms of operating profit maximization, explicitly recognizing the dual role of sales employees as sources of revenues as well as generators of operating costs. We introduce a flexible stochastic model of retail store sales, estimated from store-specific historical data, that can account for the impact of all known sales drivers, including the number of scheduled staff, and provide an accurate sales forecast at a high intra-day resolution. We also present solution techniques based on mixed-integer (MIP) and constraint programming (CP) to efficiently solve the complex mixed integer non-linear scheduling (MINLP) problem with a profit-maximization objective. The proposed approach allows solving full weekly schedules to optimality, or near-optimality with a very small gap. On a case-study with a medium-sized retail chain, this integrated forecasting–scheduling methodology yields significant projected net profit increases on the order of 2–3% compared to baseline schedules.     

An integrated staff-sizing approach considering feasibility of scheduling decision

This paper presents an integrated staff-sizing system for analyzing and determining workforce management policies with consideration of staff flexibility in service organizations, which addresses and captures the integrated requirements between long-term manpower planning and short-term staff scheduling in the service sector. Multiple Objective Linear Programming (MOLP) is applied to optimize several diversified goals. Solution methods to the MOLP models for the staff planning and staff scheduling are developed respectively, then a solution approach is proposed to iteratively revise the unacceptable staff-sizing plan or scheduling plan. Finally, an example of nurse sizing is analyzed and computational studies are carried out to investigate managerial insights.     

智能制造环境下的备件生产与运输协同调度问题研究

智能制造和即时配送环境下的备件生产与运输协同调度问题是目前国内研究的一大热点,这是因为备件供应链响应速度已成为当前备件制造企业赢得客户的关键因素。为了提高客户满意度,尽可能缩短从客户下达定制化生产订单到订单配送完成的时间,本文建立了以所有客户总等待时间最短为目标的混合整数规划模型和集合覆盖模型,推导了最优解性质,并设计改进的分支定价算法求得最优解。通过将小规模算例结果与CPLEX进行对比,验证了模型和算法的有效性。多组算例测试结果表明,所提出的模型和算法可以有效提升智能制造环境下的备件供应链运作效率。     

蔬果类商品网上直销包装作业工序优化模型研究

针对蔬果类商品网上直销模式下,其标准销售单元包装作业问题规模大、商品品类多、订单个性化强、生产配送周期多等特点,基于批量流水作业生产、JIT准时制生产及周期调度的思想,研究该类商品标准销售单元包装作业的生产调度问题,建立蔬果类商品网上直销包装作业优化模型,并设计改进的“模拟增压——退火算法”对其进行求解,以便制定出合理的包装作业计划,有效衔接采摘和订单分拣作业以及后续装车作业,缩短包装时间,保证蔬菜的新鲜性。最后,通过应用实例验证模型和算法的有效性,结果表明,本文周期调度方法得到的调度方案比一般的非周期调度方法大大节约了包装作业成本,为蔬果类商品网上直销企业生成包装作业计划提供了理论指导。     

A Goal Programming Model for Staff Scheduling at a Telecommunications Center

In this paper, a mathematical model is developed to solve a staff scheduling problem for a telecommunications center. Currently, weekly schedules are manually produced. The manual nature of the process and the large number of constraints and goals lead to a situation where the used schedules are both inefficient and unfair. A zero-one linear goal programming model is suggested to find an optimized cyclical schedule. The center objectives as well as the engineers’ preferences are taken into account. The developed model had to produce balanced schedules that provide the required coverage while satisfying fairness considerations, in terms of weekends off, working night shifts, isolated days on, and isolated days off. A staffing analysis and mathematical properties have been developed to find the optimal parameters of the staff scheduling model. A 6-week scheduling period has been suggested instead of the current weekly period. Work patterns have been suggested to improve schedules quality. These work patterns have been mathematically formulated as a set of soft constraints. The suggested mathematical model has been implemented using Lingo software. The optimal cyclical schedule has been found. It significantly increases both efficiency and staff satisfaction. The suggested approach can be used for any similar staff scheduling problem.     

Maximizing the profit in customer’s order acceptance and scheduling problem with weighted tardiness penalty

The customer’s order acceptance and scheduling problem in a single machine environment has long been an appealing research subject. In this paper, a situation where a pool of customers exists and each customer tends to place all his orders to a single company is addressed. Hence, the customer’s orders will be entirely either accepted or rejected. In this work, decisions on rejection or acceptance of customers and sequencing of the accepted orders are simultaneously considered. The goal is to maximize the total net profit obtained from accepted orders revenues contributed by tardiness penalty. In response to the computational complexity of the problem, a heuristic algorithm and two optimal branch and bound procedures with upper bound, lower bound, and dominance rules are developed. Computational results demonstrate that the proposed methods perform well in a timely manner.     

Integer programming approach to reactive scheduling in make-to-order manufacturing

New algorithms based on mixed integer programming formulations are proposed for reactive scheduling in a dynamic, make-to-order manufacturing environment. The problem objective is to update a long-term production schedule subject to service level and inventory constraints, whenever the customer orders are modified or new orders arrive. Different rescheduling policies are proposed, from a total reschedule of all remaining and unmodified customer orders to a non-reschedule of all such orders. In addition, a medium restrictive policy is considered for rescheduling only a subset of remaining customer orders awaiting material supplies. Numerical examples modeled after a real-world scheduling/rescheduling of customer orders in the electronics industry are presented and some results of computational experiments are reported.     

A matheuristic algorithm for stochastic home health care planning

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances.     

An effective mathematical formulation for the unidirectional cluster-based quay crane scheduling problem

The quay crane scheduling problem plays an important role in the paradigm of port container terminal management, due to the fact that it closely relates to vessel berthing time. In this paper, we focus on the study of a special strategy for the cluster-based quay crane scheduling problem that forces quay cranes to move unidirectionally during the scheduling. The scheduling problem arising when this strategy is applied is called the unidirectional quay crane scheduling problem in the literature. Different from other researches attempting to construct more sophisticated searching algorithms, in this paper, we seek for a more compact mathematical formulation of the unidirectional cluster-based quay crane scheduling problem that can be easily solved by a standard optimization solver. To assess the performance of the proposed model, commonly accepted benchmark suites are used and the results indicate that the proposed model outperforms the state-of-the-art algorithms designed for the unidirectional cluster-based quay crane scheduling problem.     

基于企业网络舆情的客户满意度分析及管理方法

社交网络平台的迅速发展,促使网络舆情成为企业获取商业情报、扩大竞争优势的重要信息来源。本文针对网络舆情环境下的企业客户关系管理问题展开研究。通过构建企业客户推动式信息反馈模型,描述了企业客户、网络用户与企业网络舆情间的联系,并依据信息反馈模型,提出变尺度聚类算法。该算法将传统聚类方法的求解过程由单一尺度分析扩展到多尺度分析,克服了实际数据聚类应用过程中的聚类结果特征不显著问题。本文选取新浪微博作为数据源,利用企业网络舆情数据集和企业客户数据集进行数据分析实验。实验结果表明,企业可以通过获取与其主营业务相关的网络舆情信息,实现客户满意度预测;同时,变尺度聚类算法结果能够为企业进一步制定销售战略和销售战术提供决策支持。     

Stochastic optimal budget decision for advertising considering uncertain sales responses

Firms are faced with uncertain sales responses even though they advertise appropriately. To help marketing managers make optimal budget decisions in this situation, we develop a stochastic model, depicting the problem of advertising budget decision as a special Markov decision process where a new objective, maximizing expected market utility, is proposed. In the model we introduce a two-dimension state variable including accumulative sales, which vary randomly with advertising budget, and the predicted probability that an advertising campaign obtains a full sales response. We make an analysis of the model on the premise of growing infinite market potential, deriving the property of optimal policies and that of optimal value function. These results are successfully used to make advertising budget decisions for a private university in Xi’an, China.     

Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model

The general aim of this study is to provide a guide to the future marketing decisions of a firm, using a model to predict customer lifetime values. The proposed framework aims to eliminate the limitations and drawbacks of the majority of models encountered in the literature through a simple and industry-specific model with easily measurable and objective indicators. In addition, this model predicts the potential value of the current customers rather than measuring the current value, which has generally been used in the majority of previous studies. This study contributes to the literature by helping to make future marketing decisions via Markov decision processes for a company that offers several types of products. Another contribution is that the states for Markov decision processes are also generated using the predicted customer lifetime values where the prediction is realized by a regression-based model. Finally, a real world application of the proposed model is provided in the banking sector to show the empirical validity of the model. Therefore, we believe that the proposed framework and the developed model can guide both practitioners and researchers.     

Column generation heuristics for multiple machine, multiple orders per job scheduling problems

Front opening unified pods (FOUPs) are used to store and transport silicon wafers in 300-mm semiconductor wafer fabs. To achieve production efficiencies, wafers are grouped together in FOUPs without regard to the originating customer placing the order. In the resulting multiple orders per job (moj) scheduling problem, scheduling is performed at the FOUP (i.e., aggregated order) level, while scheduling performance is assessed per individual customer order. Column generation heuristics are presented for single and parallel machine moj scheduling problems to minimize total weighted order completion time. The proposed heuristics obtain near-optimal solutions very quickly, outperforming competing approaches in the literature.     

销售量预测的REM-GM(1,1)模型及群预测方法研究

库存管理是商业企业经营管理的重要组成部分,其管理应以销售出库量为主要依据,而客户需求的不确定性一直是困扰管理者的首要难题.提出在灰色GM(1,1)模型的基础上,结合残差时刻的灰灾变预测,建立了REM-GM(1,1)模型,并采用群预测的方法,对商业企业中的销售出库量进行了预测.经实证结果检验,表明该模型对商业企业的销售出库量的预测不论是单步预测还是多步远期预测,其预测精度与传统GM模型相比均有较大提高,且误差的波动较为稳定,其预测的结果是令人满意的.     

A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints

Aerial robotics can be very useful to perform complex tasks in a distributed and cooperative fashion, such as localization of targets and search of point of interests (PoIs). In this work, we propose a distributed system of autonomous Unmanned Aerial Vehicles (UAVs), able to self-coordinate and cooperate in order to ensure both spatial and temporal coverage of specific time and spatial varying PoIs. In particular, we consider an UAVs system able to solve distributed dynamic scheduling problems, since each device is required to move towards a certain position in a certain time. We give a mathematical formulation of the problem as a multi-criteria optimization model, in which the total distances traveled by the UAVs (to be minimized), the customer satisfaction (to be maximized) and the number of used UAVs (to be minimized) are considered simultaneously. A dynamic variant of the basic optimization model, defined by considering the rolling horizon concept, is shown. We introduce a case study as an application scenario, where sport actions of a football match are filmed through a distributed UAVs system. The customer satisfaction and the traveled distance are used as performance parameters to evaluate the proposed approaches on the considered scenario.     

考虑维修时间的总质保需求预测模型

激烈的市场竞争迫使制造商们逐渐向以顾客需求为中心的公司转变。在近 20 年内,作为影响顾客满意度的主要因素,产品的质保服务管理的相关研究开始成为学术界的焦点。良好的质保服务会给企业节省较多的运营成本,故对于刚投入市场的新产品而言,准确地预测质保需求对制造商合理分配资金等具有重要意义。以往对质保需求的预测模型都局限于分析长期意义上一个产品的总质保成本,忽略了产品的维修时间和动态销售过程对准确预测产品的总质保需求及成本的影响。为此,以销售期内的产品所产生的维修需求为主要的研究对象,深入探讨维修时间对预测质保需求的影响。模型中,利用非齐次泊松过程模拟产品的动态销售过程,并利用复合随机过程中的交错更新理论来刻画维修时间对总质保需求的影响。最后的参数分析,为企业更好地管理质保服务提供了重要的现实依据。     

Minimizing customer order lead-time in a two-stage assembly supply chain

Coordination across different process stages of the supply chain is becoming more common as the information needed for this coordination is easier to obtain and share. With the availability of this information, managers are beginning to recognize that there can be benefits to scheduling processes in a coordinated fashion. Thus, finding good schedules for the entire supply chain has added importance to today’s managers. Coordination of the material as it moves from one stage to the next should lead to improved customer order lead-time performance for the whole chain and thus better customer service overall. We look at a two-stage assembly supply chain with the objective of minimizing the average customer order lead-time. Minimizing lead-time is becoming increasingly important as customers demand quicker response. But beyond this better customer service objective, minimizing lead-time is consistent with keeping inventory costs low. We introduce a number of properties of optimal solutions, results for special problem cases, and a series of lower bounds. We also provide a number of intuitive heuristics for coordinated supply chain scheduling and test them to determine their effectiveness.     

基于即时配送和收益激励的众包物流运力调度研究

基于参与众包物流配送模式的人员闲散、积极性不高,以及客户对即时配送要求并非完全刚性的特征,引入模糊时间窗,将客户满意度量化为众包物流配送人员到达客户位置时刻的模糊隶属度函数。在一定客户满意度下,以最大化众包物流配送人员收益为目标,构建了基于即时配送和收益激励的众包物流运力调度问题模型,考虑到机会、逾时、超载惩罚成本,利用带有动态权重的粒子群算法,通过算例验证分析,结果表明该模型在保证客户满意度和提高众包物流配送人员积极性方面具有可行性和有效性。     

Output-specific input-assurance regions in DEA

The use of assurance region (AR) constraints to restrict multipliers in data envelopment analysis (DEA) is well-established, and has been discussed at length in the literature. The conventional assumption in imposing such restrictions is that they apply universally. Specifically, AR constraints on input multipliers are intended to control the relative importance of the individual inputs in terms of how they impact the entire bundle of outputs. In many settings the relative importance of inputs is different for some of the outputs than for others. A typical example of this in the financial services sector is where the importance of sales staff versus service staff is different in regard to sales outputs than is true for service outputs. In this paper we develop a general DEA framework that incorporates multiple input-AR structures that cater to multiple output classes. We examine the cases of both divisible and indivisible inputs, and as well as mutually exclusive and overlapping output sets. The concepts are applied to a financial services situation.