A two-phase mathematical-programming heuristic for flexible assignment of activities and tasks to work shifts

In the service industry, workers perform work shifts and are assigned to interruptible activities and uninterruptible tasks during their shifts. The work shifts of regular employees are often established several weeks in advance of the operations when the activity and task demands are still uncertain. Just a few days before the operations when these demands are unveiled with more certainty, the planned schedules can be slightly modified and on-call temporary employees can be scheduled to satisfy the demands as best as possible. As acceptable modifications, extending the planned shifts and moving workers’ meal breaks are considered. In this paper, we are interested in the scheduling problem encountered in this second step, which also involves assigning activities and tasks to the scheduled work shifts. To produce good-quality solutions in fast computational times for large-sized instances, we develop a two-phase heuristic. In the first phase, an approximate mixed-integer programming model is used to suggest temporary shifts and extensions to regular shifts and to schedule and assign tasks. In the second phase, a column-generation heuristic embedded in a rolling horizon procedure determines the final shifts and assigns activities to them. Computational results obtained on randomly generated instances are reported to evaluate the validity of the proposed solution method.     

Generating labor requirements and rosters for mail handlers using simulation and optimization

In this research, we introduce a dual-purpose simulation model that integrates two decision support systems used by the US Postal Service to configure and staff their mail processing and distribution centers (P&DCs). The first system is designed to optimize the daily equipment schedules and the second to optimize the size and composition of the permanent workforce. Large-scale integer programs are solved in both cases. Because some compromise is needed in the time granularity, it is important to have an independent means of validating the results. This is the first purpose of our simulation model. The second involves the generation and validation of labor requirements for a category of workers known as mail handlers. While there is a one-to-one relationship between machine operators and the equipment schedule derived from the mail arrival profiles, no such relationship exists for those responsible for moving the mail between workstations. Neither productivity measures nor formal work rules exist. To resolve this shortcoming, we use simulation again, but this time to estimate mail handler requirements and then to determine whether the weekly schedules derived from the staff optimizer are adequate to meet the facility's service standards. Holistically speaking, the simulation serves as a bridge between the two optimization systems. The procedure is demonstrated with data provided by the Dallas P&DC.     

Adaptive DSS design strategy for a regional socioeconomic balance sheet

This article describes the concept of a socioeconomic (S-E) balance sheet and its uses; the implementation of a decision support system (DSS) for a specific organization using balance-sheet data and adaptive design criteria as suggested in current DSS literature.The basic idea and application of a S-E balance sheet is similar to that of a balance sheet for a business enterprise. However, the S-E balance sheet is expanded to include a labor inventory to account for human assets and some items of intangible capital such as education and water resources which contribute directly to the total assets of the region. The term ‘socioeconomic’ is used to distinguish between the expanded concept of a public sector balance sheet and the typical balance sheet which would measure only material wealth.The S-E balance sheet enables one to ‘add-up’ those main factors—tangible and intangible—which contribute, in a positive or negative sense to the total cumulative wealth of the region.The development of a regional S-E balance sheet is viewed from a ‘systems’ standpoint where one conceives of the regional economy as a complex structural whole, with various productive elements of the economy (government, business, labor, educational institutions, foreign investments etc), contributing to asset formation while producing claimants to the regional wealth. However, claims (that is, liabilities on the balance sheet) may be held by producers and non-producers alike. The disequilibrium between assets and claimants to these assets sheds light on the structure (and structural unemployment) of resources and resource ownership in the regional economy.The S-E balance sheet can be used as a forecasting and planning tool. In particular, it is used to show past priorities in terms of allocations of various resources (assets) and to develop projections which will show possible future allocations. The balance sheet is of use to researchers, planners, decision makers in private industry and public policy makers.The implementation of a regional S-E balance sheet based DSS has been made possible through the use of a powerful database management system and decision-support software named EXPRESS.The implementation strategy for the S-E balance sheet takes into account adaptive DSS design criteria suggested in the current literature. In essence, this involves the implementation of “expanding subsets of system capabilities based on an initial nucleus of extensible features.” An initial user of the S-E balance sheet based DSS is St. Clair County in Illinois for the purpose of allocating funds across 45 communities from the Community-Development Block Grant Program.     

Interactive selection of Web services under multiple objectives

The manual composition of efficient combinations of Web services becomes almost impossible as the number of services increases dramatically. When determining an appropriate set of services, managers must take into consideration given business processes, business strategy and multiple Quality of Service (QoS) objectives while ensuring the cost-efficient usage of limited resources. Because the agility with which new business requirements are adapted has a major influence on business success and poor investment decisions may thus entail corporate failure, decision makers are experiencing growing pressure to prove the value of IT investments—but they often lack appropriate multicriteria decision support tools. This paper introduces a new decision support approach that more properly addresses these challenges. We implemented this approach into a tool and evaluated the performance of two popular methods (i.e., the Analytic Hierarchy Process and the Weighted Scoring Method) by means of a real-life case study in the social security sector. It turns out that the decision support system assists decision makers in identifying investments that more precisely target their company’s business needs by allowing them to interactively determine and continually optimize service allocation according to the corporate business processes and multiple (strategic) objectives.     

Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm

This paper presents a new model for developing a human resources portfolio based on a neuro-fuzzy approach. The adaptive neural network is constructed based on the Boston Consulting Group (BCG) portfolio matrix. The adaptive neural network was established by applying the simulated annealing algorithm. The model enables decision makers to evaluate and assess human resources potential in accordance with the environment and its circumstances. The purpose of creating this model is to enable insight into the existing potential and plan assets to improve and promote the employees’ potential in a company. The model allows the priorities of the suggested strategies to be defined, which eliminates one of the flaws of the classic BCG portfolio matrix. In this neuro-fuzzy model the input variables are described using fuzzy sets that are represented by Gaussian functions. Using expert reasoning a unique knowledge base is formed which enables employees to be scheduled by strategies. The portfolio model is tested in a realistic industrial environment.     

Cross-training in a cellular manufacturing environment

This study addresses the need for cross-training in a cellular manufacturing environment. It is demonstrated that an effective cross-training situation results if workers and machines are connected, directly or indirectly, by task assignment decisions. The connections between workers and machines (i.e. the qualifications of workers) form ‘chains’ that can be used to reallocate work from heavily loaded workers to less loaded workers. This provides the possibility of a balanced workload situation among workers, something that is desirable from a social as well as an economic viewpoint. Based on this insight, we have developed an integer programming (IP) model that can be used to select workers to be cross-trained for particular machines. The model may help in trade-offs between training costs and the workload balance among workers in a manufacturing cell. The workload balance indicates the usefulness of labor flexibility in a particular situation. A numerical example is presented to illustrate various elements and features of the model. It also provides further insight into the role of ‘chaining’ workers and machines. The industrial applicability of the model and directions for future research are also indicated.     

A hybrid approach for personalized service staff recommendation

In this study, we established a novel set of service procedures that epitomize the human-centered spirit of service. By using self-organizing maps and collaborative filtering recommendation, we developed a mechanism that links the two service procedures of selecting service staff members and how customers decide tip amounts based on perceived value. Through the proposed mechanism, the recommender system could effectively predict customer preferences regarding service staff members and assign suitable members for delivering services. In addition, this study integrated the service experiences of previous customers with local tipping cultures for calculating recommended tip amounts for the reference of customers. Under this mechanism, the customer-centered spirit can be completely integrated into service procedures for effectively enhancing customer satisfaction, increasing the job satisfaction of employees, and producing a virtuous cycle of service quality improvement.     

Interactive intuitionistic fuzzy methods for multilevel programming problems

Multilevel programming problems model a decision-making process with a hierarchy structure. Traditional solution methods including vertex enumeration algorithms and penalty function methods are not only inefficient to obtain the solution of the multilevel programming problems, but also lead to a paradox that the follower’s decision power dominates the leader’s. In this paper, both multilevel programming and intuitionistic fuzzy set are used to model problems in hierarchy expert and intelligent systems. We first present a score function to objectively depict the satisfactory degrees of decision makers by virtue of the intuitionistic fuzzy set for solving multilevel programming problems. Then we develop three optimization models and three interactive intuitionistic fuzzy methods to consider different satisfactory solutions for the requirements of expert decision makers. Furthermore, a new distance function is proposed to measure the merits of a satisfactory solution. Finally, a case study for cloud computing pricing problems and several numerical examples are given to verify the applicability and the effectiveness of the proposed models and methods.     

A feasibility test model for new telecom service development using MCDM method: A case study of video telephone service in Korea

The telecommunications industry is undergoing dramatic changes fueled by rapid technical development and regulatory changes. Especially, in Korea, the penetration rate of mobile service is almost 90%; therefore, there is not enough room for a new customer. Furthermore, today’s new services have been influenced by more factors in decision making before launching. It has been the main issue for decision makers whether a new service will be successful or not. However, a few methods have been introduced to evaluate the feasibility of a new service. And very few papers have introduced to the evaluatation of comparative advantages among competitive services, but such methods mainly depend on subjective opinion of experts. The proposed model considers the multidimensional factors influencing telecom service evaluation based on the ANP’s advantages, which helps service providers in a telecom field to select a new service with the view of their strategies. Also, the BSC perspectives are adapted in the model to generate evaluation measures. Additionally, a new video phone service in Korea is evaluated based on a proposed model.     

Evaluation of carsharing network’s growth strategies through discrete event simulation

Carsharing organizations are nowadays faced with the emergence of new markets due to the growing popularity of their services. To keep up with the growing demand, they have to constantly adapt their network and balance their stations’ capacities by implementing new strategies. These strategies involve creation of new carsharing stations, increasing the capacity of stations, merging or demerging carsharing stations etc. Currently, the decision makers rely on an intuitive strategy selection process which often results in inadequate decisions being made representing an immediate loss in resources, time and market penetration. This paper presents a discrete event simulation based decision support tool that assists the decision makers in selecting best network growth strategies to implement for meeting adequately the demand growth while maximizing the members’ satisfaction level and minimizing the number of vehicles used. Our discrete event simulation model allows modeling the activities at any given set of carsharing stations, regardless of their number and capacities. A benchmarking comparison of different potential strategies is done. An application of the proposed model on a region of Communauto’s Montréal (Québec, Canada) carsharing network is provided.     

基于属性和规则的访问控制模型

针对分布式服务环境中用户数目大量增长和资源访问策略复杂多样化的问题,分析目前访问控制模型的应用局限性,提出多用户服务模式中一种基于属性和规则的访问控制模型。该模型引入复合属性表达式和复合权限的概念,制定授权约束规则,为用户分配多组角色。给出模型实例,并将其与其他模型进行比较。     

基于客户效用最大的物流资源整合决策分析

物流资源整合是一个复杂的系统工程,也是物流领域的热门话题。针对物流资源整合过程中每个物流子任务获取最佳物流资源困难的问题,提出了基于客户效用最大的物流资源整合决策方案。该方案以时间、成本、质量、服务为决策指标,构建了物流服务生产函数,建立了以客户效用最大化为目标的物流资源整合的决策模型,应用离差最大化法求出各决策指标的加权向量,并对决策方案排序进而求出最优方案,最后以一个商品车配送任务的物流资源整合为例验证了决策模型和求解方法的有效性和可行性。     

Hadoop平台下计算能力调度算法的改进与实现

在计算能力调度算法中没有全面考虑各资源特征的分配是否满足作业多样的服务要求,提出一种基于优先级的计算能力加权调度算法,根据作业的优先级以及提交时间等因素来计算作业的权重。依据作业的权重对作业队列进行排序并分配空闲的slot给队首的作业,从而避免调度陷入局部最优也能更好地满足作业的多样性服务要求。在搭建的Hadoop平台上进行实验表明,改进后的算法能较均衡地分配系统资源减少一些作业的等待时间,并且运行全部作业的用时有所减少。     

基于多属性群决策服务组合方案选取方法

针对如何快速、准确地得出最符合用户偏好的服务组合方案的问题,提出一种基于多属性群决策理论的服务组合方案选取方法。该方法根据匹配规则计算子服务QoS属性与用户偏好的相似度,进而依据相似度选取子服务进行组合,在多个服务组合方案选取中,将用户各偏好视做决策者,利用多属性群决策理论对所有方案进行优劣排序,从而选取最优组合方案。实验结果表明,该方法所得解方案相对较优,消耗时间较合理,进一步证明了该方法的有效性和可行性。     

Machine learning advice in managerial decision-making: The overlooked role of decision makers’ advice utilization

Machine learning (ML) analyses offer great potential to craft profound advice for augmenting managerial decision-making. Yet, even the most promising ML advice cannot improve decision-making if it is not utilized by decision makers. We therefore investigate how ML analyses influence decision makers’ utilization of advice and resulting decision-making performance. By analyzing data from 239 ML-supported decisions in real-world organizational scenarios, we demonstrate that decision makers’ utilization of ML advice depends on the information quality and transparency of ML advice as well as decision makers’ trust in data scientists’ competence. Furthermore, we find that decision makers’ utilization of ML advice can lead to improved decision-making performance, which is, however, moderated by the decision makers’ management level. The study’s results can help organizations leverage ML advice to improve decision-making and promote the mutual consideration of technical and social aspects behind ML advice in research and practice as a basic requirement.     

Dimensions of Employee Satisfaction as Determinants of Organizational Commitment in the Hotel Industry

In today's dynamic environment, the role of employees and their level of organizational commitment are becoming increasingly important for business success, which is especially relevant for the service industry in general and for the tourism sector in particular because of problems associated with employee turnover, loyalty strategies with the customer, and so forth. Employee satisfaction is considered an essential ingredient for developing organizational commitment. The main purpose of this study is to analyze the influence of different facets of employee job satisfaction, that is, job conditions, reward system, relations with superiors and co‐workers, organizational human resources (HR) policies, on employees’ organizational commitment in the hotel industry. The research hypotheses are tested with a sample of 760 hotel employees on Gran Canaria, Spain. The results show the relevance of the working conditions, direct rewards, relationships with managers, and the HR policies in order to develop a high level of organizational commitment.     

一种规则驱动的觉察上下文计算模式

以面向服务的体系结构和产生式推理技术为基础,提出一种规则驱动的自适应觉察上下文计算模式。在该模式中,用户的需求及服务复合策略的语义用产生式表示。在动态普适计算运行环境中,规则推理引擎能够在运行期间,根据当前的上下文知识自动选择服务构件,动态合成应用软件完成预定目标。     

An attitude-driven web consensus support system for heterogeneous group decision making

Consensus reaching processes are applied in group decision making problems to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been developed to facilitate consensus reaching processes. However, new trends bring diverse challenges in group decision making, such as the modelling of different types of information and of large groups of decision makers, together with their attitude to achieve agreements. These challenges require the capacity to deal with heterogenous frameworks, and the automation of consensus reaching processes by means of consensus support systems. In this paper, we propose a consensus model in which decision makers can express their opinions by using different types of information, capable of dealing with large groups of decision makers. The model incorporates the management of the group’s attitude towards consensus by means of an extension of OWA aggregation operators aimed to optimize the overall consensus process. Eventually, a novel Web-based consensus support system that automates the proposed consensus model is presented.     

考虑编制受限的均衡任务覆盖人员排班模型

本文研究了考虑编制受限情况下的机场地勤人员排班问题.目的是从管理角度最小化成本,解决无法通过雇佣外界临时工以满足需求的人员紧缺问题,同时提高由不确定性因素造成计划中断的应对能力.本文建立了考虑编制受限的均衡任务覆盖混合整数优化模型,针对问题的特点设计了高效的启发式算法求解,并通过大型机场的真实算例验证算法及模型的效果.从无法覆盖的任务在排班周期内分布情况和员工间公平性两个角度分析模型在实际应用中的情况,证实模型能够很好应对用人高峰问题,提高机场运营效率,同时协助管理者在人员组成上进行决策.     

Sequential decision aggregation with social pressure

This paper proposes and characterizes a sequential decision aggregation system consisting of agents performing binary sequential hypothesis testing and a fusion center which collects the individual decisions and reaches the global decision according to some threshold rule. Individual decision makers’ behaviors in the system are influenced by other decision makers, through a model for social pressure; our notion of social pressure is proportional to the ratio of individual decision makers who have already made the decisions. For our proposed model, we obtain the following results: First, we derive a recursive expression for the probabilities of making the correct and wrong global decisions as a function of time, system size, and the global decision threshold. The expression is based on the individual decision makers’ decision probabilities and does not rely on the specific individual decision-making policy. Second, we discuss two specific threshold rules: the fastest rule and the majority rule. By means of a mean-field analysis, we relate the asymptotic performance of the fusion center, as the system size tends to infinity, to the individual decision makers’ decision probability sequence. In addition to theoretical analysis, simulation work is conducted to discuss the speed/accuracy tradeoffs for different threshold rules.