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.     

Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming

Global supplier selection is a multi-goal multi-criteria problem which needs to consider both qualitative and quantitative factors. Which suppliers are the best and how much should be purchased from the selected suppliers is an important purchasing issue for manufacturers. Traditionally, decision makers can determine the best supplier from evaluating few suppliers with qualitative supplier selection criteria by using fuzzy analytic hierarchy process (FAHP), but evaluate dozens of global suppliers simultaneously or determine the order quantity from them will be complex and difficult. Meanwhile, decision makers can determine the order quantity form the suitable suppliers by using fuzzy goal programming (FGP); however, it is not easy to decide weights for each goal of global supplier selection with different supply chain strategies. This study integrated the FAHP and FGP (FAHP-FGP) method to be a new approach for global supplier selection in considering the manufacturer’s supply chain strategies. With FAHP-FGP method, the manufacturer can consistently integrate multi-manager’ opinions in determining weights of each goal and obtain the order quantities for suitable suppliers based on manufacturer’s strategies. To demonstrate the usefulness of the proposed method, a real-world case of a digital consumer products manufacturer is presented.     

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.     

A goal programming model for facility location planning

The paper presents a goal programming model for facility location planning. Often the location-decision is coupled with multiple objectives, at times conflicting among themselves. Specifically the model considers the four major objectives: (i) necessary locations, (ii) maximum number of locations, (iii) capacity restrictions and (iv) transportation cost/walking distance minimization, simultaneously and proposes optimal locations in conjunction with the existing facilities in the region. The model developed has been illustrated with an example considering the location of community storage facilities in a specified region.     

A statistical screening procedure for goal programming algorithm selection

Many of the linear goal programming algorithms that are available today are based on a simplex type solution method that begins with an initial simplex tableau whose solution set variables (i.e. basic variables) consist of all negative deviational variables or all positive deviational variables. Prior research has shown that computational solution effort can be reduced if the appropriate all negative or all positive deviational variable algorithm is selected. This paper presents a practical statistical screening procedure that can be used in conjunction with previously published selection criteria to reduce computational effort by selecting the appropriate algorithm for all types of applied goal programming models. Results of the study reveal the accuracy of the statistical screening procedure when it is applied to a large sample of goal programming problems.     

A goal programming model for readiness and the optimal redeployment of resources

Organizations such as the military and those involved with disaster relief are vitally concerned with their ability to redeploy resources between various geographical locations in response to cataclysmic events. Measuring the effectiveness of a redeployment plan involves multiple objectives and differing priorities. The primary objective of redeployment is meeting requirements at affected locations with a secondary concern for transportation costs. In order to reflect these features, the problem is formulated as a goal programming model. Several variations of the model are investigated that can enhance its value. An example redeployment problem is formulated and solved to illustrate the approach.     

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.     

Allocation of teaching personnel: A goal programming model

This paper is concerned with the development of a model structure for allocating the teaching resources of a high school in an effective and efficient manner. In allocating these resources the multiple and often conflicting goals of the school system administrators will be considered, as well as the various operating and financial constraints of the school system.     

A goal programming model for the siting of multilevel EMS systems.

Facility siting models known as location covering techniques have proven to be useful particularly for emergency medical services (EMS) planning, given the importance of ambulances responding to demand within some maximum time constraint. These models represent a set of methods which focus the health planner's attention on the access of people to health care, since they attempt to “cover” people in need of service within some specified time standard.This research develops a technique for the locational planning of sophisticated EMS systems, characterized by multiple levels of emergency health services. Specifically, a two-tiered system with “basic life support” and “advanced life support” capabilities is modeled as a goal program.By applying location covering techniques within a goal programming framework, this study develops a method for the siting of multilevel EMS systems so that (1) each service level maximizes coverage of its own demand population, and (2) “back-up” coordination between levels is assured. The usefulness of this goal program as a health planning tool is evidenced in the model's explicit articulation of EMS policy objectives and its ability to link system levels in terms of “goal-directed behavior”. The working of this multilevel covering model is demonstrated by reference to EMS planning scenarios and related numerical examples.     

A linear goal programming method to recover risk neutral probabilities from options prices by maximum entropy

Decisions in Economics and Finance - We develop a new methodology to retrieve risk neutral probabilities (equivalent martingale measure) with maximum entropy from quoted option prices. We assume...     

基于模糊组合权的模糊TOPSIS方法在供应商选择中的应用研究

供应商的选择在供应链管理中是很重要环节,供应商选择的成功与否将直接影响到整个供应链的竞争优势,故有必要对供应商选择进行深入研究。本文将已有的模糊组合权方法和模糊TOPSIS方法相结合,提出一种基于模糊组合权的模糊TOPSIS方法,并将其应用于供应商的选择研究中。文章首先总结已有的相关研究结果,给出供应商评价的准则,接着介绍三角模糊数的相关理论,推出模糊组合权及模糊TOPSIS的算法步骤,最后通过一个算例验证方法的可行性。     

Integer goal programming for multistage lot sizing: Experimentation and implementation

This article considers the issues involved in implementing a large-scale multistage lot sizing model in a pharmaceutical manufacturing environment, and reports on a series of sensitivity experiments that subsequently examined the critical impacts of system capacity and inventory policy on the specification of a multistage schedule. The model was initially developed as an aggregate scheduling aid for a class of tablet pharmaceuticals. The manufacture of tablet products is a serial-type process characterized by batch flow. The model placed multiple resource capacity restrictions on various stages of the multistage system to ensure the feasibility of the resultant schedules. Difficulties in estimating penalty costs for shortages were circumvented by employing a multi-objective formulation of the lot-sizing problem.Various obstacles encountered during the implementation process are discussed. Model implementation encompassed the development of an approximation algorithm for efficiently solving the large-scale problem. The performance of the algorithm was evaluated by examining the closeness to optimality of the solutions obtained using the procedure. Performance statistics are presented for the sensitivity experiments discussed herein. Another essential aspect of implementation involved the timely revision of model input parameters. This facet of implementation proved to be at least as important to management as the efficient provision of a “good” solution.Model experimentation centered on variations in the capacities of bottleneck resources and changes in target inventory parameters whose values are predetermined by company policy. The first set of experiments was designed to demonstrate the importance of system capacity to the lot-sizing process and to illustrate that the location of a system bottleneck can vary over time as a function of both internal and external factors. The results indicated that lot-sizing procedures that fail to incorporate capacity information or that focus on a single bottleneck production stage in order to schedule production are severely limited with respect to practical and/or long-term applicability.The final set of inventory-related experiments indicated that the specification of target ending inventory levels was a crucial factor in the lot-sizing process. The determination of appropriate target levels must reflect the inherent trade-off between the objective of minimizing shortages and the desire to avoid excessive inventory accumulations. The generation of usable model information was found to be contingent upon the realistic definition of target level parameters.     

A fuzzy bi-level method for modeling age-specific migration

Modeling and forecasting international migration are significant research areas since migration forecasts are vital in decision making and policy design regarding economy, security, society, and resource allocation. The methods for modeling and forecasting migration rely on strict subjective or statistical assumptions which may not always be met. In addition, lack of a universally accepted definition of the term “migrant” and the ambiguities in data due to recording and collection systems result in inconsistencies and vagueness in migration modeling. Considering these, in this paper, a fuzzy bi-level age-specific migration modeling method is proposed. The bi-level structure embedded in the model makes use of the well-known Lee-Carter method as well as fuzzy regression, singular value decomposition technique, and hierarchical clustering to reflect the general characteristics of the country of concern together with the distinct emigration and immigration behaviors of the age groups. Bayesian time series models are fitted to the time-variant fuzzy parameters obtained through the proposed method to forecast future migration values. The proposed method is applied on female and male age-specific emigration and immigration counts of Finland for 1990–2010 period and Germany for 1995–2012 period, and the future values are forecasted for 2011–2025 and 2013–2025 respectively. The method is compared with an existing Bayesian approach and the numerical findings display that the proposed fuzzy method is superior to the existing one in modeling and forecasting age-specific migration values within significantly narrower prediction intervals.     

Using goal programming to determine interest group disutility for public policy choices

Within this paper a framework is developed for incorporating into the process of social choice both interest group preferences for social goals and the efficient allocation of limited resources. The social welfare function is adopted as the foundation for the framework and the goal programming algorithm is used to approximate the utility of the interest groups for their optimal policy choice. A function is defined which measures the disutility experienced by a social unit when a policy other than its optimal choice is selected. A surrogate measure of this disutility involving imposed preference weight changes for social goals is determined and its implications for use in developing the social welfare function are examined.     

A feasibility study of economic and environmental scenarios by means of interactive multiple goal programming

In this paper we describe our experiences with the application of a multiple criteria decision method (IMGP=Interactive Multiple Goal Programming) for the evaluation of economic policy in the Netherlands. An uncommon aspect of this study is that the multiple criteria decision method was not used to find an optimal solution, but rather to test the attainability of a number of contrasting scenarios for economic development. These scenarios are intended to represent the main options for the development of the Netherlands in the eighties. Within the framework of a broader research project (labeled Policy-Oriented Survey of the Future), these scenarios are linked to characteristic political views.     

Convergence problems in stochastic programming models with probabilistic constraints

We consider a stochastic programming model with probabilistic constraints and assume to have only empirical estimates of the true probability distribution of the random variables involved in the model. By using properties of semicontinuous and quasi-convex/quasi-concave limetions, we can weaken the traditional assumptions on the functions involved. We generalize both the functional form and the mathematical properties of the functions and then prove the almost sure uniform convergence of the sequence of approximate problems to the true one.     

A fuzzy multi-objective programming approach for determination of resilient supply portfolio under supply failure risks

The main contribution of this paper is to develop a new decision tool that interprets strategies for determination of resilient supply portfolio under supply failure risks. The strategic decisions include the allocation of emergency capacities to be pre-positioned at backup suppliers, the output of which can be increased in the event of mitigating a shortage caused by another supplier's failure. The model contains three objective functions – minimising the total cost, minimising the net rejected items and minimising the net late deliveries – while satisfying capacity and minimum order quantity requirement constraints. A weighted additive fuzzy multi-objective model is proposed to simultaneously consider the imprecision of information and the relative importance of objectives for determining the allocation of order quantity and emergency capacity to each supplier. The application of the proposed model is illustrated using an example case of global supply chains with different supplier characteristics.