An accounting control system structured on multiple objective planning models

An important problem confronting decision makers in modern organizations is how to plan and control in a multiple goal decision setting. The usual approach for attacking this problem is to assume one dominant goal and treat others as constraints for the budget planning purpose. The traditional accounting control system is a variance analysis which makes a comparison between an ex ante planning budget, a budget adjusted to the actual activity level, and actual results. The present paper describes how to set up multiple goal planning models by using goal programming and multiple objective linear programming techniques. And an opportunity cost concept of ex post accounting variance analysis (which a comparison is made between an ex ante budget, ex post optimum budget, and actual results) is used as a control device. This ex post analysis will signal a deviation in any data input parameter in the planning models.     

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.     

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.     

Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning

Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade-offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancing decision-maker learning are considered.     

Power planning in ICT infrastructure: A multi-criteria operational performance evaluation approach

It is critical for information and communications technology (ICT) companies to carry our effective power planning, in order to support the growing number of services they provide, and this traditionally relies on the tacit knowledge and experience of senior staff. The loss of such domain knowledge resulting from the retirement of staff is an important issue for organizations such as Chunghwa Telecom (CHT), the largest ICT operator in Taiwan. This study thus develops a systematic power planning model using a multi-criteria operational performance evaluation. A group version of the fuzzy repertory grid and fuzzy TOPSIS approaches is applied to elicit a set of evaluation criteria that senior staff agree on, and then the priorities of the telecom rooms are evaluated against this. In addition, a new factor, reflecting the attitudes of the decision makers with respect to the degree of strictness, is defined to determine the superiority and inferiority of each alternative compared to the others. Furthermore, a novel decision aggregation strategy regarding the degree of variation among decision makers is proposed, and a quantitative assessment is carried out to analyze its impact on the ranking results in an objective manner. The proposed model may help ICT organizations to more effectively manage their power resources, and thus obtain competitive advantages.     

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.     

一种策略层项目计划的双目标优化方法

针对产品开发项目管理的实际情况,对策略层计划优化方法进行研究。以工作包的工作量估算为基础,以资源投入水平和工期最小化为目标,考虑各种约束条件,提出一种策略层项目计划问题的混合整数规划问题模型。以非支配遗传算法NSGA-II为基础框架,设计了一种改进的双目标遗传算法。该算法针对问题的特点,提出了基于资源平滑的解码算法。参考NSGA-III的关键特征,对拥挤密度计算方法进行改进。通过企业实际项目案例,验证了算法的性能和所提出的策略层项目计划方法的有效性。     

Zero-base budgeting—Dealing with conflicting objectives

While Zero-Base Budgeting (ZBB) has received considerable attention in the public sector, virtually all the studies dealing with the system have neglected or given cursory attention to the problem of multiple conflicting objectives. This paper presents a goal programming approach as a systematic means to develop the ZBB process for multiple objectives. This approach allows the administrators to more realistically portray the decision environment as well as their judgment in their budgetary planning models, thus making the budgeting an effective and pragmatic way to implement the planning and decision making process. This study demonstrates a goal programming based ZBB system in the public sector based on real-world data.     

商品交易市场网络节点选址的双层规划模型——以安徽省砀山县为例

优化商品交易市场选址方法有利于提升城市规划布局模式,促进城市有序发展。在分析现有选址模型在应用时存在不足的基础上,采用双层规划模型构建了商品交易市场选址模型。上层规划从决策者的角度出发,通过考虑修建商品交易市场的总费用与购买者需求量之间的关系,以使总的收益最大。下层规划描述使用者的选择行为,对应于最大效用值选择商品交易市场。同时,针对所建模型,设计了启发式求解算法,并以安徽省砀山县为例进行初步应用验证。     

EDUCATION

The power of an integer programming approach—and more particularly a 0,1 programming approach—to resource allocation problems is not widely appreciated. Recent developments in mathematical programming and in problem formulation enable decision makers to deal explicitly with the special conditions which characterize real world problems in capital budgeting, scheduling, and facilities planning. In this paper, somewhat tutorial in nature, we seek to demonstrate formulation and solution of an equipment selection resource allocation problem with several special conditions. The problem can be solved on any reasonably equipped computer system. Sensitivity studies are also performed and discussed.     

Allocation of Returned Products among Different Recovery Options through an Opportunity Cost–Based Dynamic Approach*

In a make‐to‐order product recovery environment, we consider the allocation decision for returned products decision under stochastic demand of a firm with three options: refurbishing to resell, parts harvesting, and recycling. We formulate the problem as a multiperiod Markov decision process (MDP) and present a linear programming (LP) approximation that provides an upper bound on the optimal objective function value of the MDP model. We then present two solution approaches to the MDP using the LP solution: a static approach that uses the LP solution directly and a dynamic approach that adopts a revenue management perspective and employs bid‐price controls technique where the LP is resolved after each demand arrival. We calculate the bid prices based on the shadow price interpretation of the dual variables for the inventory constraints and accept a demand if the marginal value is higher than the bid price. Since the need for solving the LP at each demand arrival requires a very efficient solution procedure, we present a transportation problem formulation of the LP via variable redefinitions and develop a one‐pass optimal solution procedure for it. We carry out an extensive numerical analysis to compare the two approaches and find that the dynamic approach provides better performance in all of the tested scenarios. Furthermore, the solutions obtained are within 2% of the upper bound on the optimal objective function value of the MDP model.     

A goal programming model for paper recycling system

The conflict between economic optimization and environmental protection has received wide attention in recent research programs for waste management system planning. This has also resulted in a set of new waste management goals in reverse logistics system planning. The purpose of this analysis is to formulate a mixed integer goal programming (MIGP) model to assist in proper management of the paper recycling logistics system. The model studies the inter-relationship between multiple objectives (with changing priorities) of a recycled paper distribution network. The objectives considered are reduction in reverse logistics cost; product quality improvement through increased segregation at the source; and environmental benefits through increased wastepaper recovery. The proposed model also assists in determining the facility location, route and flow of different varieties of recyclable wastepaper in the multi-item, multi-echelon and multi-facility decision making framework. The use of the model has been illustrated through a problem of paper recycling in India.     

基于有限合作机制的群决策熵模型研究

研究一类具有合作机制的群决策问题。提出两阈值的合作函数对决策人之间的合作关系进行模糊分类,采用模糊测度方法度量决策人和决策人集的权力指数,建立合作群决策的非线性规划熵模型集结权力指数,并基于极大熵的最优化原理求解该模型。利用Choquet模糊积分计算备选方案的综合评价值,并对备选方案排序选择最优方案。最后通过算例分析并验证合作群决策模型和运用模糊积分方法求解模型的合理性、有效性。     

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.     

CONSTRAINED DECISION CRITERIA1

The choice of a suitable decision criterion is a difficult decision for many decision makers. In general, each criterion selects a different optimal act, each having its own appealing features. This paper proposes an approach allowing the decision maker to incorporate multiple criteria into an easily solved mathematical programming model whose solution is the optimal act. Optimality is defined by the objective function used in the model. The constraints embody the decision maker's anxieties and minimal desires.     

Development of simulation-based production execution system in a shipyard: a case study for a panel block assembly shop

The management of a panel block shop in a shipyard is a complex process that entails the largest amount of work and in which many decisions are involved. Shipbuilders have considered the process as a bottleneck since every panel for every ship must be processed through the shop. The objective of this research is to carry out a materials flow analysis to maximise process productivity and to place simulation optimisation technology in the hands of decision makers, such as production planners and supervisors. In this article, a production execution planning system is proposed for panel block operations utilising discrete-event simulation and simulated annealing. The simulation model was validated using a real production scenario and the comparison showed a very favourable agreement between the actual panel shop and the simulation model. The proposed system supports production planners by general dispatching rules and optimisation to make better scheduling decisions on the shop floor. The system will provide a complete schedule that is at least as clear and accurate as any schedule currently obtained.     

Modelling decentralized decision making in the energy sector

Large linear programming models, which have been widely used to determine the optimal structure of national energy systems, are based on the assumption that there is an absolutely centralized decision making process within the energy system. In this paper an attempt is made to match the real decision structure of a given energy system, by decomposing an LP energy model into smaller models, with the corresponding system decision centres. This is done by applying the ‘transfer price’ algorithm of Dantzing and Wolfe. The ‘master’ problem corresponds to the central planning unit, i.e. a Ministry of Energy, whereas the subproblems correspond to peripheral operating units, i.e. enterprises, usually state owned, which produce and distribute the energy carriers. The optimal plans of the peripheral units are submitted to the central unit, which through the mechanism of pricing of both common resources, inputs and energy services outputs, co-ordinates the overall planning of the energy system. An illustrative example is given referring to the Hellenic national energy system. The research reported is placed within a wider research endeavour, whose objectives and main line of work are also given.     

A multiple criteria decision-making model for justifying the acceptance of rush orders

Rush orders are immediate customer demands that exceed the expectation of a currently effective MPS (master production schedule). Decision-makers are often hesitant in the decision of accepting such orders. This paper presents a multiple criteria decision-making model for justifying the acceptance of rush orders for an assembly-to-order production system. Four criteria or production objectives are simultaneously considered and a multiple objective programming technique, the e-constraints approach, is adopted to solve the decision-making problem. This model could give the cost estimation for producing a rush order under various combinations of production objectives. The computed cost value could serve as a valuable reference for justifying the economics of accepting the rush order, and help to determine its pricing strategy.     

Multiobjective investment planning under uncertainty

In this paper we consider the relevance of various planning methods and decision criteria to multiobjective investment planning under uncertainty. Assuming that a natural reaction to uncertainty is to operate so as to leave open as many good options as possible (as opposed to maximizing subjective expected utility) we argue that the planning process should concentrate on analyzing the effects of the initial decision, and that for this exercise the classical methods of mixed integer programming are inappropriate. We demonstrate how the technique of dynamic programming can be extended to take account of multiple objectives and use dynamic programming as a framework in which we analyze the robustness of an initial decision in the face of various types of uncertainty. In so doing we also analyze the risks involved in both the planning and decision making functions.     

A Goal Programming Model for Effective Segment Determination: A Comment and Application

This note presents a model for the effective market segment determination problem. The integer goal programming model was developed for a nonprofit health care organization. The approach is an alternative application of a model presented by McClure and Wells [7]. The model includes input from the organization's clients as well as the organization's decision makers. A distinctive element is the simultaneous development of market segments and the consideration of management, institutional, and resource constraints. The purpose of the model was to aid the decision makers in determining if effective market segments exist which can be reached with information and educational materials concerning infant nutrition and breastfeeding. The model was tested and found to be effective in helping the decision makers segment their clients.