Stochastic models for air cargo terminal manpower supply planning in long‐term operations

In this research, based on two deterministic‐demand planning models, we established two long‐term stochastic‐demand planning models by incorporating the stochastic disturbances of manpower demands that occur in actual operations. The models are formulated as mixed integer linear programs that are solved using a mathematical programming solver. To compare the performance of the two stochastic‐demand and two deterministic‐demand planning models under the stochastic demands that occur in actual operations, we further develop a simulation‐based evaluation method. Finally, we perform numerical tests using real operating data from a Taiwan air cargo terminal. The preliminary results show that the stochastic models could be useful for planning air cargo terminal manpower supply. Copyright © 2008 John Wiley & Sons, Ltd.     

Corporate manpower planning: a review of models

Manpower planning has achieved maturity during the past decade. Personnel practitioners have become conversant with the analysis of manpower systems, the use of quantitative techniques, and have developed a sound approach to the whole question of manpower management. But, whilst they no longer rely on the management scientist for using those models and other techniques now widely available, there is a continued contribution to be made in advancing the field. This paper reviews the practical relecance of existing models. It concentrates on techniques for evaluating manpower supply and career management problems, distinguishing especially between the roles played by exploratory and normative models. The author hopes that this article will give management scientists a realistic appraisal of current practice and provide pointers as to where developments are most needed.     

Optimal recruitment strategies in a multi-level manpower planning model

Markov manpower planning models have extensively been analysed in the past in order to find an optimal personnel strategy for which the stocks of the manpower system evolve towards desirable ones. So far, those models do not take into account interactions among different organizational decision levels. In this paper, a multi-level manpower planning model is presented that considers, besides the desirable stock vector at overall level, proposals for the departmental stocks from lower organizational levels. Attainability of the stock vectors at departmental level is examined under control by recruitment and interdepartmental transitions. A multi-level optimization algorithm is presented to determine an optimal recruitment strategy resulting in attainable and acceptable stocks that are a compromise between the proposal from the top and the proposals from the departments.     

Tactical Manpower Planning via Programming Under Uncertainty

This paper presents models for different types of manpower pooling policies. A multi-grade manpower system with mutually exclusive skills is considered. The work load imposed is a random variable characterized by the known joint distribution of the number of jobs to be performed and of the time to do a job. The basic models are developed as a tool for manpower planning in the jobbing workshops of an oil company. The resulting "two-stage programmes under uncertainty" are shown to reduce to mixed-integer linear programmes. The models are then generalized to permit their use in a larger class of manpower planning problems.     

On the Asymptotic Behaviour of Age Distributions in Manpower Systems

The literature on supply models for manpower planning shows that an important consideration is the size of the discrepancy between the age or length of service distribution of the population and the age distribution which would be reached if present policies were continued indefinitely. In the present paper we study the asymptotic behaviour of the age distribution in any manpower system. An application to a decision problem in a university system is given.     

A stochastic manpower planning model under varying class sizes

Solution related to different types of manpower planning problems arising in different industries and organizations are very much helpful for proper planning and implementation of different objectives. Previously those type of problems are mostly solved under the deterministic set up. Gradually several scientists have developed different types of stochastic models appropriate for solving such types of problems. The present study is an attempt to develop a stochastic manpower planning model under the set up where the classes are of varying sizes and promotion occurs only on the basis of seniority. The work of second author was supported by a research fellowship from Council of Scientific and Industrial Research (Sanction No. 9/28(611)/2003-EMR-I), India.     

A modeling framework and local search solution methodology for a production-distribution problem with supplier selection and time-aggregated quantity discounts

Supplier selection with quantity discounts has been an active research problem in the literature. In this paper, we focus on a new real-world quantity discounts scheme, where suppliers are selected in the beginning of a strategic planning period (e.g., 5 years). Monthly orders are placed from the selected suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate this type of cost structure in a multi-period, multi-product, multi-echelon supply chain planning problem, and develop a mixed integer linear programming (MIP) model for it. Our model is highly intractable; leading commercial solvers cannot construct high quality feasible solutions for realistic instances even after multiple hours of solution time. We develop an algorithm that constructs an initial feasible solution and a large neighborhood search method that combines two customized iterative algorithms based on MIP-based local search and improves such solution. We report numerical results for a food supply chain application and show the efficiency of using our methodology in getting very high quality primal solutions quickly.     

A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains

In this paper, we investigate the lot and delivery scheduling problem in a simple supply chain where a single supplier produces multiple components on a flexible flow line (FFL) and delivers them directly to an assembly facility (AF). It is assumed that all of parameters such as demand rates for the components are deterministic and constant over a finite planning horizon. The main objective is to find a lot and delivery schedule that would minimize the average of holding, setup, and transportation costs per unit time for the supply chain. We develop a new mixed integer nonlinear program (MINLP) and an optimal enumeration method to solve the problem. Due to difficulty of obtaining the optimal solution in medium and large-scaled problems, a hybrid genetic algorithm (HGA) is also developed. The proposed HGA incorporates a neighborhood search (NS) into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods are compared on randomly generated problems, and computational results show that the performance of HGA is very promising because it is able to find an optimal or near-optimal solution for majority of the test problems.     

Modelling heterogeneity in a manpower system: a review

Manpower planning is an essential methodology for business and industry; it allows managers to make more efficient use of human resources. However, human behaviour is highly variable and it is therefore essential for manpower planning that population heterogeneity is successfully modelled. In this paper we review methods of incorporating population heterogeneity into manpower modelling. The analysis of differentials in a manpower system is emphasized since they are a source of aggregation error in stochastic models. Two strategies have been stressed, the use of observable sources of heterogeneity as they affect wastage, and the latent sources which cannot be identified precisely but are known to affect the key parameters of most models. Copyright © 2000 John Wiley & Sons, Ltd.     

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.     

Human resources management at a marine container terminal

We consider the manpower planning problem in the real context of a marine container terminal. The main features of this problem are the uncertainty of workforce demand and the need of ensuring a time continuous efficiency of the terminal, which enforces to decompose the problem into two phases: a long-period planning first and then a daily planning.We propose mathematical programming models for both problems and suitably tailor them to the container terminal at the Gioia Tauro port. We derive solution algorithms by exploiting the mathematical properties of the models: a heuristic approach to a set-covering type problem for the long-term planning, and a branch-and-bound algorithm for the short-term planning. Finally, we report computational results on some real instances.     

航空机务维修的优化管理

在航空机务维修工作中,科学的管理、人力资源的合理配置对及时完成维修任务,保障训练作战计划至关重要.从装备完好率和完成任务的及时性出发,分别建立了数学优化配置模型,并给出了这两种情况下效益(成本)矩阵的构造方法,进而将优化模型转化为最优线性指派问题来处理,从而为航空机务维修工作中人力资源的优化配置提供了一种科学、合理的决策方法.     

On maintenance scheduling of production units

A typical maintenance scheduling problem is presented as a large-scale mixed integer nonlinear programming case. Several relaxations of the conditions of variables and constraints are discussed. The optimal solution of the models based on these relaxations is viewed as the lower bound of the optimal solution in the original problem. A combined implicit enumeration and branch-and-bound algorithm is used. Typical dimension of the problems for which computational experience is reported is 25 production units in the system. 19 of these are to be maintained and a planning horizon of 52 weeks with 5 types of hours per week. The corresponding dimensions of the model are about 5700 constraints, 700 binary variables and 6500 nonlinear separable variables.     

A Survey of Manpower Planning Models and Their Application

Since the late 1950s, much work has been done on developing models of manpower systems which may be used for the purposes of planning. Many organizations have made successful use of such models, but in spite of these successes, manpower planning models are only gradually coming into widespread use. The aim of this paper is to review the models which have been developed, concentrating on their assumptions and applications rather than on mathematical or statistical details. A common theme of successful applications is that good presentation of results and ease of use are more important to users than theoretical sophistication.     

A Dynamic Programming Approach to Determine Optimal Manpower Recruitment Policies

The manpower planning models available in the literature have dealt with how changes take place in a manpower planning system, under various operating and policy constraints. However, none of these models has identified the manpower system costs. In this paper we have identified various manpower system costs. Further, we have developed a manpower planning model with the objective of minimizing the manpower system costs. The model has been found to be analogous to the Wagner-Whitin model in production/inventory management. A numerical example has been given to illustrate the model.     

Lagrangian relaxation based algorithm for trigeneration planning with storages

Trigeneration is a booming power production technology where three energy commodities are simultaneously produced in a single integrated process. Electric power, heat (e.g. hot water) and cooling (e.g. chilled water) are three typical energy commodities in the trigeneration system. The production of three energy commodities follows a joint characteristic. This paper presents a Lagrangian relaxation (LR) based algorithm for trigeneration planning with storages based on deflected subgradient optimization method. The trigeneration planning problem is modeled as a linear programming (LP) problem. The linear cost function poses the convergence challenge to the LR algorithm and the joint characteristic of trigeneration plants makes the operating region of trigeneration system more complicated than that of power-only generation system and that of combined heat and power (CHP) system. We develop an effective method for the long-term planning problem based on the proper strategy to form Lagrangian subproblems and solve the Lagrangian dual (LD) problem based on deflected subgradient optimization method. We also develop a heuristic for restoring feasibility from the LD solution. Numerical results based on realistic production models show that the algorithm is efficient and near-optimal solutions are obtained.     

Multi-Item, Multi-Facility Supply Chain Planning: Models, Complexities, and Algorithms

We propose a planning model for products manufactured across multiple manufacturing facilities sharing similar production capabilities. The need for cross-facility capacity management is most evident in high-tech industries that have capital-intensive equipment and a short technology life cycle. We propose a multicommodity flow network model where each commodity represents a product and the network structure represents manufacturing facilities in the supply chain capable of producing the products. We analyze in depth the product-level (single-commodity, multi-facility) subproblem when the capacity constraints are relaxed. We prove that even the general-cost version of this uncapacitated subproblem is NP-complete. We show that there exists an optimization algorithm that is polynomial in the number of facilities, but exponential in the number of periods. We further show that under special cost structures the shortest-path algorithm could achieve optimality. We analyze cases when the optimal solution does not correspond to a source-to-sink path, thus the shortest path algorithm would fail. To solve the overall (multicommodity) planning problem we develop a Lagrangean decomposition scheme, which separates the planning decisions into a resource subproblem, and a number of product-level subproblems. The Lagrangean multipliers are updated iteratively using a subgradient search algorithm. Through extensive computational testing, we show that the shortest path algorithm serves as an effective heuristic for the product-level subproblem (a mixed integer program), yielding high quality solutions with only a fraction (roughly 2%) of the computer time.     

Numerical modeling of baffle location effects on the flow pattern of primary sedimentation tanks

Computational fluid dynamics (CFD) is used extensively by engineers to model and analyze complex issues related to hydraulic design, planning studies for future generating stations, civil maintenance and supply efficiency. In order to find the optimal position of a baffle in a rectangular primary sedimentation tank, computational investigations are performed. Also laboratory experiments are conducted to verify the numerical results and the measured velocity fields which were by Acoustic Doppler Velocimeter (ADV) are used. The GMRES algorithm as a pressure solver was used in the computational modeling. The results of computational investigations performed in the present study indicate that the favorable flow field (uniform in the settling zone) would be enhanced for the case that the baffle position provide small circulation regions volume and dissipate the kinetic energy in the tank. Also results show that the GMRES algorithm can obtain the good agreement between the results of numerical models and experimental tests.     

Optimizing yard assignment in an automotive transshipment terminal

This paper studies a yard management problem in an automotive transshipment terminal. Groups of cars arrive to and depart from the terminal in a given planning period. These groups must be assigned to parking rows under some constraints resulting from managerial rules. The main objective is the minimization of the total handling time. Model extensions to handle application specific issues such as a rolling horizon and a manpower leveling objective are also discussed. The main features of the problem are modeled as an integer linear program. However, solving this formulation by a state-of-the-art solver is impractical. In view of this, we develop a metaheuristic algorithm based on the adaptive large neighborhood search framework. Computational results on real-life data show the efficacy of the proposed metaheuristic algorithm.     

A Lagrangian relaxation approach to multi-period inventory/distribution planning

We consider a multi-period inventory/distribution planning problem (MPIDP) in a one-warehouse multiretailer distribution system where a fleet of heterogeneous vehicles delivers products from a warehouse to several retailers. The objective of the MPIDP is to minimise transportation costs for product delivery and inventory holding costs at retailers over the planning horizon. In this research, the problem is formulated as a mixed integer linear programme and solved by a Lagrangian relaxation approach. A subgradient optimisation method is employed to obtain lower bounds. We develop a Lagrangian heuristic algorithm to find a good feasible solution of the MPIDP. Computational experiments on randomly generated test problems showed that the suggested algorithm gave relatively good solutions in a reasonable amount of computation time.