An Interactive Framework for Multi-Person,Multiobjective Decisions

Group decision making in the presence of multiple conflicting objectives is complex and difficult. This paper describes and evaluates an iterative technique to facilitate multiple objective decision making by multiple decision makers. The proposed method augments an interactive multiobjective optimization procedure with a preference ranking tool and a consensus ranking heuristic. Two multiple objective linear programming (MOLP) solution approaches, the SIMOLP method of Reeves and Franz [39] and the interactive weighted Tchebycheff procedure of Steuer and Choo [49], are recommended optimization strategies to be used independently or in concert. Computational experience suggests that the proposed framework is an effective decision-making tool. The procedure quickly located excellent compromise solutions in a series of test problems with hypothetical decision makers. In addition, human decision makers gave positive evaluations of the procedure and the production plans the procedure provided for a resource allocation case problem.     

Multiproduct cost-volume-profit model with product limits

A multiproduct cost-volume-profit model is extended to incorporate sales or product capacity limits for each product, required rate of return on sales, and the tax rate depending on profit levels as determined by the government. The model also deals with overhead costs that are traceable to a group of products but which cannot be allocated to individual products within the group to any reasonable accuracy. To solve the model, an algorithm is constructed to determine the required volume for each product that will achieve the best possible rate of return on sales revenue. Based upon the dBase Database Management System and the CVP model, a user-friendly menu-driven interactive decision support system is developed. The model algorithm and decision support system is illustrated with an example consisting of five products. Various reports generated by the interactive decision support system are also presented.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.     

A Multiperiod Audit Staff Planning Model Using Multiple Objectives: Development and Evaluation*

The audit staff planning problem, a specific type of manpower planning problem, has been modeled using goal programming and, more recently, multiple objective linear programming. Prior studies developed single-period models and did not go beyond the model building stage. This study develops a multiperiod audit staff planning model and evaluates the model using a test application involving actual decision makers (partners in public accounting firms). The multiperiod model includes seven objectives to be optimized: profit (to be maximized), late completion of work, work declined, staff augmentation, staff reduction, underutilization of the work force, and shortfall in meeting professional development targets (all to be minimized). Over a four-quarter planning horizon with one “busy season,” the model is subject to constraints with respect to the projected audit work load, ability to substitute personnel and to perform interim audit work, available staff hours (including overtime limitations), supervisory requirements, and professional development targets. Results of the test application showed that the model was capable of producing a range of values for each objective. The participants were exposed to much of that range when making their decisions. The results also showed that all objectives were important and that participants were consistent in choosing their preferred level of each objective over several runs of the model. These results and the reactions of the participants demonstrate that the model is usable by actual decision makers and has potential for a number of specific applications.     

A Model for Determining Retail Product Category Assortment and Shelf Space Allocation

We develop a category management model to aid retailers in the space constrained decisions of which products to stock (assortment) and how much shelf space to allocate to those products. The model is formulated as a constrained optimization problem with two basic decision variables: assortment and allocation of space to the items in the assortment. The non-linearities in the objective function and the zero-one decision variables disallow a closed form solution. We develop a heuristic solution procedure based on simulated annealing and test it on a problem with a known optimum. We also apply the technique to a larger problem without a known optimum. Finally, the solution found by simulated annealing is compared against a solution produced using a shelf allocation rule based on share of sales.     

ALLOCATE: A COMPUTER MODEL FOR SALES TERRITORY PLANNING

Recent years have witnessed a renewal of interest in the application of management science techniques to personal selling related problems. Some early applications are described in [2] [7]. Cloonan has employed simulation in examination of the salesman routing problem [3] [4]. Lodish, in CALLPLAN, has devised an interactive call planning system designed to assist sales management and/or salesmen in allocating sales call time more efficiently [6]. Armstrong has devised a system he labels SCHEDULE which estimates the value of calls on accounts [1]. Hess and Samuels have designed a computer based sales districting model which is an analogue of a legislative apportionment model [5]. The objective of this paper is to explore the nature of a call planning system entitled ALLOCATE. ALLOCATE was designed to be employed by upper sales management either as an input device for sales management decisions such as sales-territory-size, or as a vehicle for determining the effects of alternative call allocation strategies on territorial revenue over multiple time periods.     

RANK GENERATION,PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS

Decision making has the objective of finding the best alternative or set of alternatives by considering a number of goals, objectives, criteria, competitors, and other important factors. The analytic hierarchy process is a decision aid used to assist a decision maker in sorting out the complexity of a decision problem and making use of his or her judgments. A decision maker must be assured that the arithmetic operations of any such decision process are the right ones—that they surface the correct ranking and values of the alternatives and preserve or alter ranks appropriately when new alternatives are added or deleted. In this paper it will be shown that with absolute measurement, rank always is preserved, with relative measurement, rank changes with nspect to scveral criteria only because of the structural dependence (involving both numbers and measurements) of criteria on alternatives. A discussion of the effect on rank of replicas and near replicas of the alternatives also is given.     

INCORPORATING SALES FORCE PREFERENCES IN A GOAL PROGRAMMING MODEL FOR THE SALES RESOURCE ALLOCATION PROBLEM

This note presents a model for the sales territory assignment and resource allocation problem. The integer-goal-programming model includes input from the sales representatives in the form of preference values along with organizational goal values from management. The approach integrates the multiple objectiive inputs both for individual sales reprresentatives and for the organization into a single model by employing the approaches of multiattribute utility theory and multicriteria decision making. The purpose of the model is to provide a vehicle for testing various strategies and assessing the impact of those strategies on the sales representatives’utilities and the organization's goals.     

Managing Retail Budget Allocation between Store Labor and Marketing Activities

The performance of a retail store depends on its ability to attract customer traffic, match labor with incoming traffic, and convert the incoming traffic into sales. Retailers make significant investments in marketing activities (such as advertising) to bring customers into their stores and in‐store labor to convert that traffic into sales. Thus, a common trade‐off that retail store managers face concerns the allocation of a store's limited budget between advertising and labor to enhance store‐level sales. To explore that trade‐off, we develop a centralized model to allocate limited store budget between store labor and advertising with the objective of maximizing store sales. We find that a store's inherent potential to drive traffic plays an important role, among other factors, in the relative allocation between advertising and store labor. We also find that as advertising instruments become more effective in bringing traffic to stores, managers should not always capitalize this effectiveness by increasing their existing allocations to advertising. In addition, we discuss a decentralized setting where budget allocation decisions cannot be enforced by a store manager and present a simple mechanism that can achieve the centralized solution. In an extension, we address the budget allocation problem in the presence of marketing efforts to shift store traffic from peak to off peak hours and show that our initial findings are robust. Further, we illustrate how the solution from the budget allocation model can be used to facilitate store level sales force planning/scheduling decisions. Based on the results of our model, we present several insights that can help managers in budget allocation and sales force planning.     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

基于突变的项目组合共享资源竞争拥挤研究

对于以项目方式进行管理或生产的企业来说,共享资源在多项目、特别是项目组合(project portfolio,PP)中的合理配置是企业运营所需要解决的重要问题,对企业实现可持续发展起着关键的支撑作用。本文将突变理论引入项目组合管理问题中,在类比交通系统中车辆对交通资源竞争的基础上,提出了项目组合系统共享资源竞争拥挤概念,并对其基础变量进行了详细分析;其次,本文分析了项目组合共享资源竞争拥挤势函数和竞争稳定性,构建了以系统效率最大化为决策目标的项目组合共享资源竞争拥挤模型;最后,通过HD集团的案例分析对项目组合共享资源竞争拥挤模型的可实践性进行了验证,并以此为基础,针对项目组合管理提出了共享资源的改进管理方案。     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study

The estimated cost of fire in the United States is about $329 billion a year, yet there are gaps in the literature to measure the effectiveness of investment and to allocate resources optimally in fire protection. This article fills these gaps by creating data‐driven empirical and theoretical models to study the effectiveness of nationwide fire protection investment in reducing economic and human losses. The regression between investment and loss vulnerability shows high R² values (≈0.93). This article also contributes to the literature by modeling strategic (national‐level or state‐level) resource allocation (RA) for fire protection with equity‐efficiency trade‐off considerations, while existing literature focuses on operational‐level RA. This model and its numerical analyses provide techniques and insights to aid the strategic decision‐making process. The results from this model are used to calculate fire risk scores for various geographic regions, which can be used as an indicator of fire risk. A case study of federal fire grant allocation is used to validate and show the utility of the optimal RA model. The results also identify potential underinvestment and overinvestment in fire protection in certain regions. This article presents scenarios in which the model presented outperforms the existing RA scheme, when compared in terms of the correlation of resources allocated with actual number of fire incidents. This article provides some novel insights to policymakers and analysts in fire protection and safety that would help in mitigating economic costs and saving lives.     

Sales territory alignment for sparse accounts

A sales territory alignment problem for sparse accounts where travel time is a major component of the salesman's worktime is presented and analyzed. The objective is to minimize total driving distance of salesmen while equalizing driving distance between salesmen. A mixed integer programming formulation and an interactive heuristic algorithm in the context of a practical application are discussed.     

Nature-inspired metaheuristics for multiobjective activity crashing

Many project tasks and manufacturing processes consist of interdependent time-related activities that can be represented as networks. Deciding which of these sub-processes should receive extra resources to speed up the whole network (i.e., where activity crashing should be applied) usually involves the pursuit of multiple objectives amid a lack of a priori preference information. A common decision support approach lies in first determining efficient combinations of activity crashing measures and then pursuing an interactive exploration of this space. As it is impossible to exactly solve the underlying multiobjective combinatorial optimization problem within a reasonable computation time for real-world problems, we have developed proper solution procedures based on three major (nature-inspired) metaheuristics. This paper describes these implementations, discusses their strengths, and provides results from computational experiments.     

多性质多目标模糊指派模型在市政工程开发商选择中的应用

考虑了指派问题中涉及到多个目标要求的情况,包括定量目标和定性目标。首先,使用模糊数学的方法将定量目标下的效率值矩阵转化为优选决策矩阵,同时提出了定性目标的量化方法,并建立了定性目标的优选决策矩阵。然后,将各个目标要求下的优选决策矩阵进行了合成,考虑了综合效益对于“优”和“劣”之间多个等级的相对隶属度,建立了多性质、多目标模糊数学模型,得到了分级特征值矩阵。最后,与传统指派模型相结合形成了多性质、多目标模糊指派数学模型,使用匈牙利算法对该模型进行了求解,并结合市政工程在开发商之间分配的例子进行了分析说明。     

The Interaction Effects Between General Thinking Skills and an Interactive Graphics-Based DSS to Support Problem Structuring*

An interactive graphics-based problem-structuring aid, GISMO, based on concepts from structural modeling and motivated by research in imagery theory and cognitive psychology was incorporated into a decision support system. A laboratory experiment was conducted to investigate the relationships between three individual general thinking skills (GTS) and the effectiveness of the problem-structuring tool. A theoretical model of the roles of GTS and GISMO in problem formulation and a methodology for testing the model are presented. The model posits that the effectiveness of visual problem-structuring aids in formulating or understanding complex problems is influenced by specific cognitive skills (i.e., visual-thinking, verbal, and logical-reasoning skills). The results indicate GISMO use is related to higher levels of problem-structure understanding, regardless of verbal and logical-reasoning skills. Also, the results indicate a strong interaction effect between GISMO use and visual-thinking skill. Subjects classified as high visual thinkers benefited more from the use of GISMO than the low visual-thinking subjects. The results of the experiment lend support for the findings of a previous study of GISMO reported in the literature, and the image theorist view that the ability to create and use visual, mental images is related to better problem-solving performance.     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.     

A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time‐indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object‐oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.     

MULTIPLE-CRITERIA DECISION MAKING INCLUDING QUALITATIVE FACTORS: THE POST-MODEL ANALYSIS APPROACH

As a method of solving multiple-criteria decision making problems with a single quantitative objective and multiple qualitative objectives, the post-model analysis (PMA) approach is proposed. The essence of PMA is to support the trade-offs between a quantitative objective and multiple qualitative objectives so that the decision maker can find a perceived most preferred nondominated solution. To this end, the optimal solution of a quantitative model is found first, without regard for qualitative factors. The solution is then evaluated in terms of qualitative objectives. When the initial quantitatively optimal solution is adjusted to allow improvement of qualitative goals, opportunity costs of achieving qualitative goals are incurred. In this process, an expert system and/or graphical display can be used. PMA therefore provides a way to incorporate quantitative models into knowledge-based expert systems.