Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation

Sequential resource allocation decision-making for the military medical evacuation of wartime casualties consists of identifying which available aeromedical evacuation (MEDEVAC) assets to dispatch in response to each casualty event. These sequential decisions are complicated due to uncertainty in casualty demand (i.e., severity, number, and location) and service times. In this research, we present a Markov decision process model solved using a hierarchical aggregation value function approximation scheme within an approximate policy iteration algorithmic framework. The model seeks to optimize this sequential resource allocation decision under uncertainty of how to best dispatch MEDEVAC assets to calls for service. The policies determined via our approximate dynamic programming (ADP) approach are compared to optimal military MEDEVAC dispatching policies for two small-scale problem instances and are compared to a closest-available MEDEVAC dispatching policy that is typically implemented in practice for a large-scale problem instance. Results indicate that our proposed approximation scheme provides high-quality, scalable dispatching policies that are more easily employed by military medical planners in the field. The identified ADP policies attain 99.8% and 99.5% optimal for the 6- and 12-zone problem instances investigated, as well as 9.6%, 9.2%, and 12.4% improvement over the closest-MEDEVAC policy for the 6-, 12-, and 34-zone problem instances investigated.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

ALIGNING ACCOUNTABILITY AND AWARENESS FOR ENVIRONMENTAL PERFORMANCE IN OPERATIONS

This study assesses internal drivers of a firm's level of environmental awareness, including methods for incorporating environmental objectives into the strategic planning of operations, communication of objectives throughout the organization, and deployment of accountability to operating personnel and managers for environmental performance. Challenges firms may encounter in motivating and holding employees and process owners accountable for environmental performance are discussed, as well as a potential for inconsistencies between management's espoused theories and theories in use. A case study of a steel manufacturer is used to determine how accountability for and awareness of environmental objectives can be operationally implemented.     

An analysis of vendor innovation capability in the contract electronics manufacturing industry

Limited academic research has been given to analysing the innovation capabilities of vendors in outsourcing contracts. This paper seeks to address this gap in the literature by enhancing our understanding of how the innovation capability of vendors is deployed to win, run and renew outsourcing contracts with their customers. Employing the resource-based view as a theoretical basis and undertaking in-depth case study analysis of three vendors in the electronic manufacturing services industry, the research shows that to achieve the outsourcing objectives of winning, running and renewing the contract, vendors can use different configurations of the competitive priorities of cost, quality, delivery and flexibility. The research aggregates the capabilities that influence the innovative capability of a vendor into the innovation-related capabilities (IRCs) of design, new product introduction and manufacturing. Three strategies are identified for vendors on how to deploy these IRCs, and a number of propositions are developed to indicate the suitability of the three deployment strategies for different operational contexts.     

Policy deployment: A case study analysis

Theaim of theresearch reported in thispaper wasto examine policy deployment in a business unit of the UK company operation of a major corporation, considered a world leader in business improvement. In this study, the application of policy deployment at an operational level was investigated to determine how managers deploy and manage direction, and assess the effectiveness of the catchball process. In the business unit studied, policy deployment could have a greater impact. For example, in deploying direction, cascade and catchball is used, however, the leadership, checks, control and communication elements could be more effective, and the respective shortcomings are described. A generic catch-reflect-improvescrutinize-pass (CRISP) approach to policy deployment is proposed by which vision, mission, goals, objectives, targets and means can be more effectively deployed.     

Single machine scheduling: A comparison of two solution procedures

Recent research on the single machine scheduling problem has focused on the treatment of multiple scheduling objectives. Most works have used some combination of mean flowtime, maximum tardiness, or total tardiness as scheduling criteria. Previous research has largely ignored earliness as a scheduling criterion. This paper presents a model that employs the criteria of flowtime as a measure of work-in-process (WIP) inventory and total job earliness to represent finished goods inventory. Total tardiness is used to represent customer satisfaction. The three criteria are used to form a single, weighted-sum objective function for guiding the choice of the best processing sequence. Two procedures are presented that might be used to solve this problem. The first is an enumeration scheme using bounding and dominance criteria that have been developed to aid efficient solution, and the second is a mixed integer linear programming (LP) formulation. Computational experience with the two models is also presented.     

Effects of ethical leadership on emotional exhaustion in high moral intensity situations

Emotional exhaustion is a threat to standard operations, particularly in organizations in which physical safety is at risk. High moral intensity is inherent in such organizations due to the magnitude of consequences associated with ethical/unethical conduct. The authors proposed a psychological process in which ethical leadership affects emotional exhaustion directly and indirectly through team cohesion. As military operational contexts typically are (or frequently have the potential to become) high moral intensity situations, the authors tested their model among 338 military personnel deployed in combat zones. They found that: (1) team cohesion partially mediated the relationship between ethical leadership and emotional exhaustion, and (2) this psychological process of direct and indirect effects of ethical leadership did not hold among individuals approaching the low end of conscientiousness.     

A Soft-Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location-Allocation Optimization

The risk of medical waste pollution and huge demand of daily medical waste disposal pose great difficulties to medical waste management. Establishing medical waste disposal centers (MWDCs) is considered one of the ways to reduce the environmental and public risk of medical waste pollution. However, how to serve the medical waste disposal demand in optimal MWDCs’ locations is a key challenge due to the complexity of the whole system and relationships among stakeholders. This article develops a soft-path solution for reducing risks as well as mitigating the related costs by optimizing the MWDC location-allocation problem. A risk mitigation-oriented bilevel equilibrium optimization model is developed for modeling the Stackelberg game behavior between the local government and the medical institutions. The objectives of the local government are minimizing the total risk of loss, the subsidy costs, and the investment cost of building the MWDCs, while minimizing the disposal and transportation costs are the objectives at the medical institution level. Fuzzy random variables are introduced by combining insufficient historical data with expert knowledge via consulting surveys to describe the coexisting uncertainties in the data. To solve the model, a hybrid approach combined with the interactive fuzzy programming technique and an Entropy-Boltzmann selection-based genetic algorithm are designed and tested. The Chengdu Medical Waste Disposal Centers Planning Project is used as a practical application. The results show that it is possible to achieve a balanced market with higher economic efficiency and significantly reduced risk through an appropriate principle of interactive actions between the bilevel stakeholders.     

A review on airport gate assignment problems: Single versus multi objective approaches

Assigning aircraft to gates is an important decision problem that airport professionals face every day. The solution of this problem has raised a significant research effort and many variants of this problem have been studied. In this paper, we review past work with a focus on identifying types of formulations, classifying objectives, and categorising solution methods. The review indicates that there is no standard formulation, that passenger oriented objectives are most common, and that more recent work are multi-objective. In terms of solution methods, heuristic and metaheuristic approaches are dominant which provides an opportunity to develop exact and approximate approaches both for the single and multi-objective problems.     

On cutting stock with due dates

Classical stock cutting calls for fulfilling a given demand of parts, minimizing raw material needs. With the production of each part type regarded as a job due within a specific date, a problem arises of scheduling cutting operations. We here propose an exact integer linear programming formulation, and develop primal heuristics, upper bounds and an implicit enumeration scheme. A computational experience carried out for the one-dimensional problem shows that our primal heuristics outperform known ones, and that the formulation has good features for finding exact solutions of non-trivial instances.     

Interactive Multiple Objective Decision Support for Sales Force Sizing and Deployment*

An interactive decision aid is introduced for the deployment of two sales resources: salespeople and sales support staff. The aid consists of a normative sales resource allocation model with five objectives and an interactive multiple objective programming solution procedure. The specific decision problem addressed involves the assignment of salespeople and sales support people to customer accounts and the allocation of the time they spend on these accounts. The authors contribute to the existing sales resource modeling literature by dealing with the deployment of two sales resources and interactively solving this problem with respect to five short-run and long-run objectives of the firm. This approach differs from existing sales force modeling efforts in which the solution is found noninteractively by optimizing a single sales resource model with respect to a single objective, often short-run sales. An application of the decision aid to the deployment problem of an industrial sales force manager is presented. Furthermore, useful extensions of the basic sales resource allocation model are discussed.     

SIMULATION FOR FIRE DEPARTMENT DEPLOYMENT POLICY ANALYSIS

This paper discusses the use of simulation in the determination and analysis of alternative deployment strategies for urban fire supression systems. The simulation model utilizes the New York City-Rand Institute simulator of fire department operations tailored for use with the Denver Fire department. A distinction is made between static deployment (where mathematical programming methods are used) and dynamic analysis (which employs the simulation model). The simulation model is described, as are its extensive data requirements. The results discuss measures of validation of the simulation and outline the recommendations resulting from this research.     

Stochastic Optimization for Natural Disaster Asset Prepositioning

A key strategic issue in pre‐disaster planning for humanitarian logistics is the pre‐establishment of adequate capacity and resources that enable efficient relief operations. This paper develops a two‐stage stochastic optimization model to guide the allocation of budget to acquire and position relief assets, decisions that typically need to be made well in advance before a disaster strikes. The optimization focuses on minimizing the expected number of casualties, so our model includes first‐stage decisions to represent the expansion of resources such as warehouses, medical facilities with personnel, ramp spaces, and shelters. Second‐stage decisions concern the logistics of the problem, where allocated resources and contracted transportation assets are deployed to rescue critical population (in need of emergency evacuation), deliver required commodities to stay‐back population, and transport the transfer population displaced by the disaster. Because of the uncertainty of the event's location and severity, these and other parameters are represented as scenarios. Computational results on notional test cases provide guidance on budget allocation and prove the potential benefit of using stochastic optimization.     

Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns

The present work aims to support tactical and operational planning decisions of reverse logistics systems while considering economic, environmental and social objectives. In the literature, when addressing such systems economic aspects have been often used, while environmental concerns have emerged only recently. The social component is the one less studied and rarely the combination of the three concerns has been analyzed. This work considers the three objectives and was motivated by the challenge of supporting decision makers when managing a real case study of a recyclable waste collection system, where strategic decisions on the number and location of depots, vehicles and containers were taken beforehand. Tactical and operational decisions are studied involving the establishment of service areas for each depot and the definition and scheduling of collection routes for each vehicle. Such decisions should represent a compromise solution between the three objectives translating a sustainable reverse logistics plan. The problem is modeled as a multi-objective, multi-depot periodic vehicle routing problem with inter-depot routes. A mathematical formulation and a solution approach are proposed. An approximation to the Pareto front is obtained for the case study and the trade-offs between the objectives are discussed. A balanced solution is proposed.     

Does customer or competitor performance drive operations prioritisation?

The purpose of this research is to identify the impact on operations managers of their perceptions of customer requirements and competitor performance on their determination of operations priorities. Data were obtained from a multiple respondent survey. Analysis involved an ordered choice model. The process of strategic prioritisation differs according to the specific performance dimension. Quality-based objectives are focused on the voice of the customer; cost-based objectives are influenced by a balance of importance to customer and performance against competitor. Other criteria (flexibility, dependability, speed) objectives are more influenced by perceptions of competitive benchmarking. This large sample survey of operations managers’ perceptions offers a valuable insight into their motivation for focusing on different operations criteria.     

The permutation flow shop problem with just in time production considerations

This research presents a variation to the permutation flow shop problem where Just In Time (JIT) production requirements are taken into account. The model developed in this research employs dual objectives. In addition to the traditional objective of minimizing the production makespan, minimization of Miltenburg's material usage rate is also incorporated. In this model, multiple units of any product are permitted in the production sequence. However, the minimization of material usage rates attempts to prevent batch scheduling of products and allows unit flow of products as required in demand flow manufacturing. A solution method is proposed for determining an optimal production sequence via an efficient frontier approach and Simulated Annealing (SA). Test problems and specific performance criteria are used to assess the solutions generated by the proposed method. Experimental results presented in this paper show that the use of the efficient frontier and SA provide solutions that approach the optimal solution for the performance measures used in this research.     

客户时间窗变化的物流配送干扰管理模型——基于行为的视角

针对客户时间窗变化而导致的物流配送难以顺利实施这一难题,运用干扰管理思想,以提高物流配送干扰管理决策过程的科学性为目标,结合行为科学中对人的行为感知的研究方法与运筹学中定量的研究手段,分析客户时间窗变化这类干扰事件对客户、物流配送运营商和配送业务员等受扰主体的影响,研究基于行为的扰动度量方法, 构建客户时间窗变化的字典序多目标干扰管理模型,并给出快速高效的求解方法。实例结果表明:本文方法比已有的全局重调度方法和局部重调度方法更实用--能够均衡各方的利益,获得扰动最小的调整方案。     

Lean and green in the transport and logistics sector – a case study of simultaneous deployment

The transport and logistics sector is of vital importance for the stimulation of trade and hence the economic development of nations. However, over the last few years, this sector has taken central stage in the green agenda due to the negative environmental effects derived from its operations. Several disciplines including operations research and sub-areas of supply chain management such as green supply chains, green logistics and reverse logistics have tried to address this problem. However, despite the work undertaken through these disciplines, theoretical or empirical research into the sequential or simultaneous deployment of the lean and green paradigms, particularly, in the road transport and logistics sector is limited. This paper presents a case study where both paradigms have been combined to improve the transport operations of a world leader logistics organisation in the metropolitan area of Monterrey, Mexico. To do this, a systematic methodology and a novel tool called Sustainable Transportation Value Stream Map (STVSM) were proposed. The results obtained from the case study indicate that the concurrent deployment of the green and lean paradigms through such methodology and the STVSM tool is an effective approach to improve both operational efficiency and environmental performance of road transport operations. The paper can be used as a guiding reference for transport and logistics organisations to undertake improvement projects similar to the one presented in this paper. Additionally, this research also intends to stimulate scholarly research into the application of lean and green paradigms in the transport and logistics sector to expand the limited research pursued in this area.     

Problem Formulation and Solution Mechanisms: A Behavioral Study of Humanitarian Transportation Planning

When dealing with urgent, ill‐defined problems, such as rapidly evolving emergency situations, operations managers have little time for problem formulation or solution. While the mechanisms by which humans formulate and solve problems have been described, mechanisms for rapid, concurrent formulating and solving are not well understood. This study investigates these mechanisms through a field study of transportation planning in a humanitarian response setting. The findings show that the problem is solved through greedy search and formulated through sensemaking, in which search enables updates to an evolving problem formulation, and the formulation directs and limits the search process. This study explores the implications of these findings for the development of better problem formulation processes and problem‐solving strategies for urgent and ill‐defined operations management problems.     

考虑服务水平与动态转移规律的共享单车投放策略研究

为实现共享单车行业的精细化、科学化的运营管理，解决共享单车的过度投放问题。首先，对单车运营系统进行分析和描述，明确投放量优化涉及的核心研究问题。将研究范围界定在单车投放环节，利用马尔可夫链与状态转移矩阵来分析和描述单车的流转过程；从需求被满足概率的角度描述共享单车系统的服务水平并据此分析各投放点的单车需求量。在分析和描述的基础上，提出单车投放量优化的核心问题。其次，基于需求量的约束与单车流转规律，构建不同调度方案下的投放量优化模型，结合可行解空间变化对目标函数最优值的影响，分析最优单车投放量的性质。得出结论认为：（1）对时空的细分，有助于更准备地把握需求，最优单车投放量也会增加。（2）可以通过调度频率的增加，减少单车的投放量，但投放量有上、下限。最后，结合案例数据，对状态转移概率矩阵和服务水平约束下节点的需求量进行了计算，并据此对优化模型进行数值求解，展示了所构建模型的应用过程，说明了模型对于解决具体问题的可操作性和有效性。文章的模型及相应的求解过程，可为解决资源的优化配置问题提供参考和借鉴。