A goal programming model for achieving racial balance in public schools

This paper develops a goal programming model for achieving racial balance in segregated public schools. The model is illustrated and offered as an improvement upon linear programming, a model previously applied in the literature which allows a single objective function only and, in comparison with goal programming, requires more variables and constraints. Goal programming, a member of the general multiple objective linear programming (MOLP) model, improves upon these among other disadvantages thereby emerging as a more efficient tool for student assignment problems.     

Planning in an uncertain environment: Stochastic goal programming using the versatility criterion

This article presents a planning model that applies the versatility criterion to goal programming problems with uncertainty about the constraints which define the set of feasible decisions. Some of the constraint parameters are assumed to be stochastic variables with a joint normal distribution. The solution sought maximizes the probability of satisfying all the constraints. A nonlinear programming model is set out which can be solved by using numerical integration at every step. An illustrative example is provided which shows the possible application of the versatility model to land-use planning.     

Allocation of research grants to university research proposals

In this paper we examine the optimal allocation of research money to incoming research proposals in a university environment. Since a university has no obvious objective to maximize when allocating research money, but rather has several, possibly conflicting, goals it would like to achieve, the method of goal programming is proposed as a solution technique, and the meaning and estimation of the priority weights in the resulting objective function in the goal programming problem, with regard to tradeoffs between goals, are discussed thoroughly. Finally, calculations based upon hypothetical sample data re presented in order to test the sensitivity of the model to different sets of priority weights.     

A polynomial goal programming approach to a class of quality control problems

The traditional quality control approach based on statistical tools has been very useful and effective when output and input qualities can be denned in terms of a single characteristic. However, in process industries such as paper, the output quality is denned in terms of two or more distinct characteristics; hence, reducing the deviation of one output characteristic from its permissible limits could result in forcing other output and/or input characteristics to deviate from their respective limits. Compounding this phenomenon is the fact that most of these industries produce substantial amounts of pollutants whose characteristics are a function of the input and output characteristics. Thus, with increasing costs of waste treatment and stringent pollution standards, there arises a notion of a trade-off between attaining market specified output characteristics and meeting federally regulated pollution standards.In this article a general process quality control problem has been formulated that reflects the above trade-off both in terms of a linear and a polynomial goal programming problem. Major advantages and differences between the two formulations are highlighted and illustrated with a practical example drawn from the paper industry.Three separate cases each with different priorities assigned to the output, pollutant and input characteristics are developed and solved under both formulations. Based on the analysis it is observed that the different solutions that result are contingent on the assumptions concerning the priorities associated with each goal and the manner by which one chooses to incorporate tradeoffs between goals in the objective function. Additionally, it is found that the solutions obtained under polynomial goal programming formulation are more conducive for implementation in practical quality control contexts.     

Goal programming solutions to transportation problems with variable supply and demand requirements

This article deals with goal programming as an aid to resolving transportation problems. The objective of this study is to demonstrate the use of goal programming as an approach to reducing total cost in transportation problems that possess variable supply and demand requirements. A generalized goal programming model is formulated and applied to a series of transportation problem situations based on the empirical data provided by a medium-size trucking firm. Comparison of the results demonstrates the cost advantage of using the goal programming model in place of the transportation method and integer interval linear programming.     

Signed social structure optimization for shift assignment in the nurse scheduling problem

This paper develops a mathematical framework that relies on modern social network analysis theories for treating the nurse team formation and nurse scheduling (shift assignment) problems, accounting for signed social connections. These problems lie in assigning nurses to teams/shifts such that the constraints regarding both the working regulations and nurses preferences are satisfied. Recent research indicates the dependence of nursing team performance on team social structure; however, so far, the social structure considerations have not been explicitly incorporated into the mathematical formulations of the nurse scheduling problem. The presented framework introduces models that quantitatively exploit such dependence. This paper explores instances of Nurse Team Formation Problem (NTFP) and Nurse Scheduling Problem (NSP) incorporating signed social structure with the measures based on such network structures as edges, full dyads, triplets, k-stars, balanced and unbalanced triangles, etc., in directed, signed networks. The paper presents the integer programming formulations for NTFP and NSP, and a problem-specific heuristic that performs variable-depth neighborhood search to tackle NTFP instances with signed social structures. Computational results for a real-world problem instance with 20 nurses are reported. The insights obtained from the presented framework and future research directions are discussed.     

A goal programming approach to public investment decisions: A case study of rural roads in Indonesia

The development planner must often face complex problems with multiple, conflicting objectives. Goal programming provides a general methodology for solving such problems. The tool is applied here to aid in the selection of rural road projects in the Indonesian Rural Works Program. Selection criteria are formalized into a set of nineteen goals which form the basis for a goal programming model. Changes in priority levels of goals and weights are used to analyze the respective effects upon the spatial distribution of investments. The approach is applicable to a wide range of problems and a variety of sensitivity analyses. Despite clear advantages, several drawbacks must be noted. First, the application of the methodology, given its degree of sophistication, is limited to a central decision making unit which has access to appropriate software. Second, the technique assumes that the planner has the ability to formulate alternative actions and consequences in a quantifiable expression.     

RPMInter-work: a multi-agent approach for planning the task-role assignments in inter-organisational workflow

ABSTRACT

Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper.     

Solving the generalized transportation problem

This paper advocates a mathematical programming approach for solving the classical transportation problem generalized to include non-linear demand and supply functions and non-linear transport costs. Such an approach is made possible by the recent public availability of a powerful optimizing system called MINOS, assembled for the solution of large programming problems for which the constraint set is linear and spatial. Primal and dual program formulations for this generalized transportation problem are set out in the paper, and brief computational experience with the software system in solving several illustrative problems is recorded.     

An alternative solution method for goal programming problems: The lexicographic goal programming case

This article provides an example of the use of an alternative goal programming solution procedure. The solution procedure is applied to solve a lexicographic goal programming problem. In the process of illustrating the problem, a number of unique technical problems and their solution procedures are discussed. This study provides a flowchart as a procedural guide to aid in its use. This article also provides a comparative analysis of GP algorithms. The results demonstrate the tableau element reduction capabilities of the proposed alternative goal programming solution procedure.     

Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.     

基于虚拟流水线的船舶分段建造调度问题研究

针对传统船舶分段制造中出现的生产等待、时间冗余等问题,应用虚拟流水线生产模式的方法进行生产系统改造。在满足约束的前提下,建立以作业班组在生产系统中的流通时间最短为目标的班组调度模型。针对分段车间调度问题应用一种基于混合蚁群遗传算法(Mixed AntColony and Genetic Algorithm,MACGA)实现了作业班组的优化调度。最后,以某造船厂分段加工车间为例,验证了虚拟流水线模式及其调度方法可以有效的提高船舶分段的生产效率。     

A technique using analytical hierarchy process in multiobjective planning models

Social and economic planning usually includes the need to consider and reconcile multiple, conflicting objectives. A number of multiple objective planning models have been proposed, using goal programming as well as other multiple objective techniques. Goal programming provides a useful tool which quickly provides a solution matching the set of ranked objective targets provided, but does not provide much exploratory analysis of planning problems. Other techniques have been used which provide generation of new alternatives for consideration, but these other techniques often are limited to original model extreme points. This paper presents an alternative means of generating planning alternatives, utilizing Analytic Hierarchy Process as a means of obtaining a good starting point, and imposition of objective attainment levels through constraints. The proposed process provides a means of decision maker learning, easily allows adjustment of objective attainment levels and is not limited to the original model extreme points.     

Multi-factor financial planning: an outline and illustration

This paper deals with the support of strategically oriented financial planning processes in business firms. In handling a financial planning problem, the decision maker has to deal with a number of complications. In this paper special attention is paid to the risk with regard to the outcomes of the financial plan and the existence of multiple, conflicting goals. An interactive approach to financial planning is presented. Risk is modeled by means of so-called multi-factor risk models and multiple goals are explicitly accounted for through the use of an interactive goal programming method. The use of the interactive approach will be numerically demonstrated by means of an exemplary planning problem.     

A polyhedral approximation approach to concave numerical dynamic programming

This paper introduces a numerical method for solving concave continuous state dynamic programming problems which is based on a pair of polyhedral approximations of concave functions. The method is globally convergent and produces computable upper and lower bounds on the value function which can in theory be made arbitrarily tight. This is true regardless of the pattern of binding constraints, the smoothness of model primitives, and the dimensionality and rectangularity of the state space. We illustrate the method's performance using an optimal firm management problem subject to credit constraints and partial investment irreversibilities.     

A satisficing method for fuzzy goal programming problems with different importance and priorities

For treating multiple objectives decision making problems with fuzzy goals and different importance, various kinds of fuzzy goal programming (FGP) models have been developed in the past three decades. Among them, two most widely used methods are: (1) weighted FGP, where the importance of the objectives is represented by weights (2) preemptive priority (also known as “Lexicographic”) FGP, where the priority levels of the goals are set in advance, and the trade-offs among goals placed at different priority levels are implemented strictly. This article presents a satisficing method based on FGP model, which holds that a more important objective has to be achieved as much as possible. The relaxed preemptive priority requirement in the proposed model provides a more efficient, flexible and practicable decision support compared to the weighted and the lexicographic models. In addition, the trade-off between optimization and importance requirement can be realized by the regulation parameter in the presented method. The performance of this method is evaluated by comparing its result with those of the six existing models in the literature.     

Evaluation of scheduling techniques for solving flowshop problems with no intermediate storage

The flowshop scheduling problem with no intermediate storage (NIS problem) was studied in this research. This problem, a modification of the classical flowshop scheduling problem, arises when a set of jobs, once started, must be processed with no wait between consecutive machines. By eliminating the need for intermediate storage, reduction of capital investment in work-in-process inventory can be achieved. This approach can be practically applied to a steel mill, in which the metal should be continuously processed in order to maintain high temperature, as well as many other similar processes.To provide insight into selecting an appropriate scheduling technique for solving the NIS problem, six methods were compared in terms of the quality and efficiency of the scheduling solutions they produced. The quality of solution was measured by makespan and the efficiency of solution was measured by the computational time requirements. The six methods examined in this study included: the Gupta algorithm, the Szwarc algorithm, an integer linear programming method, the Campbell et al. algorithm, the Dannenbring rapid access with extensive search algorithm, and a mixed integer linear programming procedure.The problem factors considered in this study were number of jobs, number of machines, and range of processing times. Relatively small-sized problems were tested with up to ten jobs, five machines, and 1–100 processing time units. Six solution techniques were selected and compared, with respect to makespan and computational time requirements, for multiple combinations of the three problem variables.The resulting test data were investigated using graphical procedures and formal statistical analyses. Initially, plots of mean values were used to graphically compare the six solution methods for the two performance criteria. Next, a multivariate analysis of variance study was conducted to investigate the quality and efficiency of the algorithms with respect to the problem factors. Then, a multiple comparison procedure was employed to analyze treatment mean differences among the six solution techniques. Results from the statistical analyses are summarized in this article.It was concluded that the two mathematical programming methods, the integer linear programming procedure and the mixed integer linear programming methods, produced the best performance in terms of makespan. These two methods, however, used a far greater amount of computational time than the other four solution techniques. Producing moderately good results as far as quality of performance, the Gupta and the Szwarc algorithms were comparable with the Campbell et al. and the Dannenbring algorithms in terms of computational efficiency. By comparison, the Campbell et al. and the Dannenbring algorithms produced the poorest performance with respect to the quality of solutions.Certain limitations were imposed for this study. The problem size considered was relatively small and the sample size was also limited to ten problems per cell. In addition, a uniform distribution function was used for generating processing times within certain ranges. These limitations were necessary in order to allow the various scheduling problems to be solved within a reasonable amount of computer time.Finally, some suggestions were provided for future research in the NIS problem area. The integer linear programming method was recommended as a standard of evaluation, owing to its best overall performance. A possible area for future research would involve the improvement of the Gupta and the Szwarc algorithms through the use of backtracking procedures within the branch-and-bound technique, so that they might be competitive with the mathematical programming methods with respect to quality of performance. Other distribution functions could be investigated in terms of the influence of the distribution of processing times on the performance measures.     

Exploring goal‐setting as a tool for leadership development

Goal‐setting theory is based on the premise that conscious goals affect action and is supported by four decades of research and more than 1,000 scholarly publications. Although the theory has been widely applied in management settings, it is an underused framework in the broader field of leadership development. This study considers the effects of goal‐setting as applied in a leadership program conducted for local board members in a nonprofit membership organization, the Florida Farm Bureau Federation. A final reduced regression model explains about 25% of the variance in individual performance by considering goal specificity, goal difficulty, education level, and gender. This study has implications for professional development programming and its application of goal‐setting theory. Additional research is needed to explore further the application of goal‐setting theory in programming for volunteer leaders.     

基于混合集合规划的多场景应急设施选址优化

多场景应急设施选址问题具有约束复杂、时效性强、规划周期短等特点。以混合集合规划为算法框架的自然约束语言NCL能对该问题进行建模与求解。以一个典型多场景应急设施选址问题为例,证明了NCL可有效求解该类问题。     

A multi-objective model for lot-sizing with supplier selection for an assembly system

Lot-sizing with supplier selection (LS-SS) is a fast-growing offspring of two major problem parents in logistics and supply chain management (‘lot-sizing’ and ‘supplier selection’). The model proposed in this paper is an attempt to extend it to an assembly system, by formulating a multi-objective model for an integrative problem of LS-SS for assembly items. The total costs of the system, consisting of purchasing, ordering, transportation, assembly, and holding, is considered the first objective function, while the total reliability of the finished products is considered the second objective function. The decision-maker aims to minimise the total costs while maximising the total reliability. Several constraints of the system (e.g. storage capacity, supplier production capacity) are taken into account. Given the complexity of the model, a heuristic evolutionary algorithm is proposed to solve the model. The results indicate which assembly items to order in which quantities, from which suppliers and in which time periods.