A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

A rostering approach to minimize health risks for workers: An application to a container terminal in the Italian port of Genoa

The evolving safety regulation is pushing seaports to comply with safety measures for workers performing heavy loads handling and repetitive movements. This paper proposes a risk-aware rostering approach in maritime container terminals, i.e., it addresses the rostering problem of minimizing and balancing workers’ risk in such terminals. To this end, a mixed integer mathematical programming model incorporating workforce risks is proposed, considering constraints such as the satisfaction of the workforce demand to perform the terminal operations, the worker-task compatibility and restrictions on the sequence of tasks assigned to the same worker. The model has been successfully applied to plan workforce over a six months horizon in a real container terminal located in Northern Italy, the Southern European Container Hub (SECH) in Genoa. As the workforce demand in SECH terminal is available at most two weeks in advance, a rolling horizon planning approach is devised. Experimental tests on real data provided by SECH terminal over a six months planning horizon highlight the effectiveness of the approach - the maximum monthly risk for workers is reduced by 33.9% compared to the current planning – and suitability to other container terminal contexts. Moreover, the model is applicable to a broad range of port situations, and robust enough to need little adaptation.     

Facility Location: A Robust Optimization Approach

In this research, we apply robust optimization (RO) to the problem of locating facilities in a network facing uncertain demand over multiple periods. We consider a multi‐period fixed‐charge network location problem for which we find (1) the number of facilities, their location and capacities, (2) the production in each period, and (3) allocation of demand to facilities. Using the RO approach we formulate the problem to include alternate levels of uncertainty over the periods. We consider two models of demand uncertainty: demand within a bounded and symmetric multi‐dimensional box, and demand within a multi‐dimensional ellipsoid. We evaluate the potential benefits of applying the RO approach in our setting using an extensive numerical study. We show that the alternate models of uncertainty lead to very different solution network topologies, with the model with box uncertainty set opening fewer, larger facilities. Through sample path testing, we show that both the box and ellipsoidal uncertainty cases can provide small but significant improvements over the solution to the problem when demand is deterministic and set at its nominal value. For changes in several environmental parameters, we explore the effects on the solution performance.     

Careers and productivity in an internal labor market

This paper contributes to the existing literature by evaluating the effects of age and tenure on careers and productivity within an internal labor market. We use the administrative personnel records of a long balanced panel of blue-collar workers from a manufacturing firm in Germany with a distinct classification of wage groups and unique information on workers’ productivity in form of suggestions for productivity improvement. Using robust locally weighted regression, our main findings suggest that the probability of promotion is highest for younger workers and early in the career. Older workers are less likely to be promoted. The likelihood of making a rewarded suggestion is inversely u-shaped with respect to tenure and age. Furthermore, workers’ ability and firm-specific human capital determine the workers’ careers and productivity.     

Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Robust goal programming (RGP) is a recently developed, powerful new optimization modeling technique that conjoins two widely accepted operations research disciplines: robust optimization (RO) and goal programming (GP). In lieu of applying a probability distribution over possible outcomes, an approach considered by stochastic programming, RO utilizes uncertainty sets to account for data uncertainty. This characteristic of RO is an important attribute because identifying such a probability distribution is challenging, at best. Given this RO context, RGP additionally incorporates GP, traditionally a deterministic procedure, to address optimization problems having multiple objectives. As such, RGP has potential to help address a wide array of data-driven applications, ranging from financial management to engineering design.As a motivating use case for the utility of an RGP approach, this paper demonstrates the applicability of RGP by way of the data-driven United States Transportation Command (USTRANSCOM) liner rate setting problem. USTRANSCOM is responsible for the technical direction and supervision of over $7 billion [1] of annual passenger, cargo, mobility, and personal property movements in support of the Department of Defense (DoD). Transporting people and material with both organic and contracted assets, USTRANSCOM supports DoD organizations and agencies on a reimbursable basis, annually setting and charging rates for air and liner (i.e., sea) transport for their customers and reimbursing the transportation providers accordingly. The Cost Recovery Branch within TCJ8, the Financial Management and Program Analysis staff organization for USTRANSCOM, annually sets liner shipping rates specific to each combination of origin, destination, commodity type, booking terms, and container size for the upcoming fiscal year (FY). As a government entity, USTRANSCOM seeks to neither make a profit nor operate at a loss in any given FY. The current rate setting methodology assumes existing data is deterministic, resulting in process inaccuracies that contribute to unexpected surpluses or deficits each FY. Moreover, the current method fails to consider an additional USTRANSCOM objective: meeting customer's expectations that liner rates will change annually in accordance with industry-specific inflation. Considering the different goals and inherent parametric variance, the use case herein incorporates a decision maker's risk preference regarding parametric variability via a priori analysis to inform RGP techniques and improve the USTRANSCOM liner rate setting process.     

The shift minimisation personnel task scheduling problem: A new hybrid approach and computational insights

Assigning scheduled tasks to a multi-skilled workforce is a known NP-complete problem with many applications in health care, services, logistics and manufacturing. Optimising the use and composition of costly and scarce resources such as staff has major implications on any organisation׳s health. The present paper introduces a new, versatile two-phase matheuristic approach to the shift minimisation personnel task scheduling problem, which considers assigning tasks to a set of multi-skilled employees, whose working times have been determined beforehand. Computational results show that the new hybrid method is capable of finding, for the first time, optimal solutions for all benchmark instances from the literature, in very limited computation time. The influence of a set of problem instance features on the performance of different algorithms is investigated in order to discover what makes particular problem instances harder than others. These insights are useful when deciding on organisational policies to better manage various operational aspects related to workforce. The empirical hardness results enable to generate hard problem instances. A set of new challenging instances is now available to the academic community.     

Improving Performance in Outpatient Appointment Services with a Simulation Optimization Approach

Outpatient health care service providers face increasing pressure to improve the quality of their service through effective scheduling of appointments. In this paper, a simulation optimization approach is used to determine optimal rules for a stochastic appointment scheduling problem. This approach allows for the consideration of more variables and factors in modeling this system than in prior studies, providing more flexibility in setting policy under various problem settings and environmental factors. Results show that the dome scheduling rule proposed in prior literature is robust, but practitioners could benefit from considering a flatter, “plateau‐dome.” The plateau–dome scheduling pattern is shown to be robust over many different performance measures and scenarios. Furthermore, because this is the first application of simulation optimization to appointment scheduling, other insights are gleaned that were not possible with prior methodologies.     

An integrated approach for manpower planning in the service sector

The manpower planning problem receives considerable attention by the management of service organizations because they are typically labor intensive. Not only must optimal staff sizes be determined over an extended horizon, but optimal staff schedules which assign labor to tours of duty and specific tasks, given the available staff size, must also be found. Adding to the complexity arising from the interrelatedness of the staff-sizing and staff-scheduling problems is the existence of the conflicting multiple objectives of minimizing costs and maximizing customer service. The few methodologies reported in the literature which treat the complete manpower planning problem as stated above have several shortcomings. Some techniques place a severe strain on computational capabilities. Also, these methods confound the specification of the goal levels with the analysis of the relative goal weightings. Often the specification of the desired goal levels is made at a different level in the organizational hierarchy than the specification of the relative importance of the various goals. This paper suggests an approach which overcomes these shortcomings. The methodology utilizes a simulation model with an imbedded heuristic procedure for the staff-scheduling problem to identify realistic aggregate staff-size goals to be used in a multiple-objective staff-sizing model. The methodology is applied to the manpower planning problem of a large sectional center post office and the managerial benefits are discussed.     

Career Patterns in the U.S. Army Officer Corps

In order to examine the careers of high attaining personnel in a large organizational labor market, variation in 5,114 U.S. Army officer careers is shown to be reducible to a small number of career rhythms with similar speeds in attainment of promotions and credentials. Early promotion to captain, early awarding of a temporary higher rank during war, and career entry close to the onset of war are consistently associated with “star” careers. This finding suggests that career outcomes are driven by temporal proximity to exogenous events like wars and cumulative advantage processes in which benefits accrue to early achievements.     

Lower bounds on the adaptivity gaps in variants of the stochastic knapsack problem

We consider stochastic variants of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary distributions. Items are placed in the knapsack sequentially, and the act of placing an item in the knapsack instantiates its size. The goal is to compute a policy for insertion of the items, that maximizes the expected value of the set of items placed in the knapsack. These variants that we study differ only in the formula for computing the value of the final solution obtained by the policy. We consider both nonadaptive policies (that designate a priori a fixed subset or permutation of items to insert) and adaptive policies (that can make dynamic decisions based on the instantiated sizes of the items placed in the knapsack thus far). Our work characterizes the benefit of adaptivity. For this purpose we use a measure called the adaptivity gap: the supremum over instances of the ratio between the expected value obtained by an optimal adaptive policy and the expected value obtained by an optimal non-adaptive policy. We show that while for the variants considered in the literature this quantity is bounded by a constant there are other variants where it is unbounded.     

The Politics of competence: Views from around the Globe

Abstract

There has been little attempt to situate notions of competence historically or culturally and the definition of competence has not changed much once applied to different national workforce development policies. An international group of scholars examines this topic as one of the products of HRD research and practice and notes the need to recontextualize the construct of competence within social, political and cultural practices.     

Optimised buffer allocation to construct stable personnel shift rosters

Organisations need to construct stable baseline personnel shift rosters based on forecasts about the future personnel demand and employee availability. However, variability arises in the short-term, which renders these forecasts incorrect and affects the quality of the personnel roster. In this paper, we study how to anticipate this variability by introducing capacity buffers in the personnel shift roster. We propose a new approach by solving an equivalent deterministic formulation of a stochastic personnel shift scheduling problem. In contrast to traditional approaches, the size and position of capacity buffers are not defined in advance but are adequately determined as an endogenous variable by the proposed optimisation model to align the available personnel capacity to the stochastic demand. We propose different strategies to define the anticipated uncertainty and to allocate capacity buffers accordingly. We validate the performance of these strategies through a comparison with a deterministic minimum cost strategy and a more traditional resource buffer strategy based on a three-step methodology. This methodology makes use of simulation and optimisation to mimic the hierarchical personnel planning process.     

Discrete simulation modelling for corporate manpower planning

Manpower planners within South African companies have very few, if any, decision support tools at their disposal to assist them in the formulation of their manpower strategies. Such quantitative tools as do exist are mathematically too sophisticated to be easily understood and implemented by manpower managers who are, on the whole, non numerate. The approach proposed in this paper, namely simulation modelling, discusses and demonstrates a powerful quantitative method which is conceptually easy to understand and whose results are easily interpreted. This particular simulation model examines the impact on organisational staffing levels over time as a result of specific present/proposed manpower policies in the areas of procurement and promotion. Through manipulating the inputs, the manpower planner can, with relative ease, examine the consequences of a range of alternative policies within a very short period of time.     

Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.     

Robust Stochastic Lot‐Sizing by Means of Histograms

Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control using the estimated distributions. These approaches often lead to fragile solutions whenever the preselected family of distributions was inadequate. In this article, we propose a minimax robust model that integrates data fitting and inventory optimization for the single‐item multi‐period periodic review stochastic lot‐sizing problem. In contrast with the standard assumption of given distributions, we assume that histograms are part of the input. The robust model generalizes the Bayesian model, and it can be interpreted as minimizing history‐dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular, we analyze the robust model based on the chi‐square goodness‐of‐fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under generous conditions. Its effectiveness is also validated by numerical experiments.     

An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems

The efficient and effective management of nursing personnel is of critical importance in a hospital's environment comprising a vast share of the operational costs. The adopted nurse workforce practices and policies highly affect the nurses' working conditions and the provided quality of care. Policy decisions on the staffing level have an impact on the outcome of the scheduling level and vice versa. Isolated reasoning typically leads to suboptimal decisions often resulting in ineffective outcomes of care. In order to overcome these inefficiencies, we propose in this paper a new integrative nurse staffing and shift scheduling approach. We test and assess the benefits of our approach in a reallife environment. Moreover, we examine the impact of several personnel policies on the staffing decision.     

考虑延误因素的机组排班模型研究

机组排班是航空公司运营计划的重要环节。传统对机组排班问题的研究,通常不考虑延误对排班的影响,导致机组排班的鲁棒性较差。本文在传统机组排班模型的基础上考虑延误成本,以最小化各项任务成本和延误成本为目标,提出了考虑随机延误因素的机组排班数学规划模型。然后提出求解此模型的启发式列生成算法,该算法可有效缩小问题规模,减少求解过程中的迭代次数并提高求解质量。利用航空公司真实飞行数据进行测试,证明算法可在短时间内求解大规模机组排班问题。最后,通过仿真试验证实考虑延误的机组排班模型可有效提升排班的鲁棒性。     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

Assemble‐to‐Order Inventory Management via Stochastic Programming: Chained BOMs and the M‐System

We study an inventory management mechanism that uses two stochastic programs (SPs), the customary one‐period assemble‐to‐order (ATO) model and its relaxation, to conceive control policies for dynamic ATO systems. We introduce a class of ATO systems, those that possess what we call a “chained BOM.” We prove that having a chained BOM is a sufficient condition for both SPs to be convex in the first‐stage decision variables. We show by examples the necessity of the condition. For ATO systems with a chained BOM, our result implies that the optimal integer solutions of the SPs can be found efficiently, and thus expedites the calculation of control parameters. The M system is a representative chained BOM system with two components and three products. We show that in this special case, the SPs can be solved as a one‐stage optimization problem. The allocation policy can also be reduced to simple, intuitive instructions, of which there are four distinct sets, one for each of four different parameter regions. We highlight the need for component reservation in one of these four regions. Our numerical studies demonstrate that achieving asymptotic optimality represents a significant advantage of the SP‐based approach over alternative approaches. Our numerical comparisons also show that outside of the asymptotic regime, the SP‐based approach has a commanding lead over the alternative policies. Our findings indicate that the SP‐based approach is a promising inventory management strategy that warrants further development for more general systems and practical implementations.     

Employee referrals: A study of ‘close ties’ and career benefits in China

This study examines the relationship between employee referrals and employees’ job tenure through the lens of social capital theory. It does so by considering the tie strength (closeness of guanxi) between referrers and referred employees in the Chinese context. In particular, we examine the mediating effect of career benefits. We theorize that close guanxi has a significant and positive impact on the job tenure of referred employees, and that career benefits (such as having a managerial role) mediate the close guanxi effect on job tenure. This highlights the critical need to recognize the tie strength as between referrers and referred employees. The support for our hypotheses comes from the use of personnel records of 4030 employees over 13 years in one large privately-owned manufacturer in China. Our study has theoretical and practical implications for the relational approach to tackling voluntary turnover in the workplace.