Decision making under uncertainty: A semi-infinite programming approach

The subject of this paper is the formulation and discussion of a semi-infinite linear vector optimization problem which extends multiple objective linear programming problems to those with an infinite number of objective functions and constraints. Furthermore it generalizes in some way semi-infinite programming. Besides the statement of some immediately derived results which are related to known results in semi-infinite linear programming and vector optimization, the problem mentioned above is interpreted as a decision model, under risk or uncertainty containing continuous random variables. Thus we treat the case of an infinite number of occuring states of nature. These types of problems frequently occur within aspects of decision theory in management science.     

Pairwise-comparison methods in multiple objective programming,with applications in a long-term energy-planning model

We propose to use pairwise comparisons within the framework of ideal-point or reference-point methods for multi-objective programming. The decision makers are requested to estimate the ratios which are acceptable for deviations from the ideal vector. Thereafter, we seek the nearest feasible solution using the Tchebycheff norm. In this paper we sketch the pairwise-comparison methods, some aspects of magnitude scaling, and the ideal-point methods. We show the results of our numerical experiments in long-term energy planning with nine objective functions. Finally, we present a preliminary evaluation of the combined method on the basis of its possible contribution to interactive decision analysis.     

Analysis and simulation of a distribution system for flowers

One of the largest flower auctions in the world is located in the Netherlands. Because of an expected expansion of the auction, the capacity of its distribution system was investigated. Discrete event simulation was applied to study the complex system. The simulation results show that the present system cannot handle the expected expansion. However, the analysis also shows ways for improving the system's efficiency, thereby enlarging its capacity.     

A flexible MILP model for multiple-shift workforce planning under annualized hours

Flexibility in workforce planning is one of the best ways to respond to fluctuations of the demand. This paper proposes a flexible mixed integer linear programming (MILP) model to solve a multiple-shift workforce planning problem under annualized working hours. The model takes into account laws and collective agreements that impose constraints on overtime and holidays. We consider possible gradual hiring of full time and partial time workers. Several objectives are pursued such as balancing the workload of the employees or minimizing the workforce size. Computational experiments on a real life problem demonstrate the effectiveness of the model.     

Multiple criteria linear programming model for portfolio selection

The portfolio selection problem is usually considered as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In the classical Markowitz model the risk is measured with variance, thus generating a quadratic programming model. The Markowitz model is frequently criticized as not consistent with axiomatic models of preferences for choice under risk. Models consistent with the preference axioms are based on the relation of stochastic dominance or on expected utility theory. The former is quite easy to implement for pairwise comparisons of given portfolios whereas it does not offer any computational tool to analyze the portfolio selection problem. The latter, when used for the portfolio selection problem, is restrictive in modeling preferences of investors. In this paper, a multiple criteria linear programming model of the portfolio selection problem is developed. The model is based on the preference axioms for choice under risk. Nevertheless, it allows one to employ the standard multiple criteria procedures to analyze the portfolio selection problem. It is shown that the classical mean-risk approaches resulting in linear programming models correspond to specific solution techniques applied to our multiple criteria model. This revised version was published online in June 2006 with corrections to the Cover Date.     

A goal programming approach to strategic resource allocation in acute care hospitals

This paper describes a methodology for allocating resources in hospitals. The methodology uses two linear goal-programming models. One model sets case mix and volume for physicians, while holding service costs fixed; the other translates case mix decisions into a commensurate set of practice changes for physicians. The models allow decision makers to set case mix and case costs in such a way that the institution is able to break even, while preserving physician income and minimizing disturbance to practice. The models also permit investigation of trade-offs between case mix and physician practice parameters. Results are presented from a decision-making scenario facing the surgical division of Toronto's Mount Sinai Hospital after the announcement of a 3-year, 18% reduction in funding.     

A fuzzy optimization approach for procurement transport operational planning in an automobile supply chain

We consider a real-world automobile supply chain in which a first-tier supplier serves an assembler and determines its procurement transport planning for a second-tier supplier by using the automobile assembler’s demand information, the available capacity of trucks and inventory levels. The proposed fuzzy multi-objective integer linear programming model (FMOILP) improves the transport planning process for material procurement at the first-tier supplier level, which is subject to product groups composed of items that must be ordered together, order lot sizes, fuzzy aspiration levels for inventory and used trucks and uncertain truck maximum available capacities and minimum percentages of demand in stock. Regarding the defuzzification process, we apply two existing methods based on the weighted average method to convert the FMOILP into a crisp MOILP to then apply two different aggregation functions, which we compare, to transform this crisp MOILP into a single objective MILP model. A sensitivity analysis is included to show the impact of the objectives weight vector on the final solutions. The model, based on the full truck load material pick method, provides the quantity of products and number of containers to be loaded per truck and period. An industrial automobile supply chain case study demonstrates the feasibility of applying the proposed model and the solution methodology to a realistic procurement transport planning problem. The results provide lower stock levels and higher occupation of the trucks used to fulfill both demand and minimum inventory requirements than those obtained by the manual spreadsheet-based method.     

Patient mix optimization in tactical cardiothoracic surgery planning: a case study

Cardiothoracic surgery planning involves different resourcessuch as operating theatre (OT) time, medium care beds, intensivecare beds and nursing staff. Within cardiothoracic surgery differentcategories of patients can be distinguished with respect totheir requirements of resources. The mix of patients is, therefore,an important aspect of decision making for the hospital to managethe use of these resources. A master OT schedule is used atthe tactical level of planning for deriving the weekly OT plan.It defines for each day of a week the number of OT hours availableand the number of patients operated from each patient category.We develop a model for this tactical level planning problem,the core of which is a mixed integer linear program. The modelis used to evaluate scenarios for surgery planning at tacticalas well as strategic levels, demonstrating the potential ofinteger programming for providing recommendations for change.     

A compromise procedure for the multiple objective linear fractional programming problem

A generalization of a well-known multiple objective linear fractional programming (MOLFP) problem, the multiple objective fractional programming (MOFP) problem, is formulated. A concept of multiple objective programming (MOP) problem corresponding to MOFP is introduced and some relations between those problems are examined. Based on these results, a compromise procedure for MOLFP problem is proposed. A numerical example is given to show how the procedure works.     

A simulation model for emergency evacuation

This paper describes the development of a prototype spatial decision support system for use by emergency planners in developing contingency plans for evacuations from disaster areas. It links together a geographical information system (ARC/INFO) with a specially written object-oriented micro-simulator via a windowing computer operating system. The details of the system are described, its limitations are discussed and potential enhancements are identified.     

AN ITERATIVE MULTILEVEL APPROACH TO NATURAL RESOURCE OPTIMIZATION: A TEST CASE

Forest planners face a dilemma. On the one hand, they desire more detail than they currently have in their planning optimization models, and on the other hand, these models are already extremely large and complex. This sort of problem is common in other natural resource management situations as well. This paper investigates an iterative multilevel approach that would allow districts within the forest to have models approaching the size and complexity of current forest models, but still approximate a forest-level optimum. A specific procedure based on equating shadow prices across districts is developed and tested with a case example where a global optimum is determinable as a standard of comparison. The procedure shows promise, but difficulties in recognizing optimality are indicated.     

The maximum covering/shortest path problem: A multiobjective network design and routing formulation

In this paper the authors introduce the maximum covering/shortest path problem and the maximum population/shortest path problem, a special case of the former model. Both models are formulated as two objective integer programs. A summary of the results of a sample problem for the latter formulation is given. Possible modifications to, and extensions and applications of both models are also presented. With these formulations the authors extend the concept of ‘coverage’ from facility location analysis to network design and routing analysis.     

Industrial petrochemical production planning and expansion: A multi-objective linear programming approach

This paper considers a strategic model planning for the petrochemical industry. It concerns with the expansion in a firm producing multiple products in several regions of a country. The expansion of the existing facilities and the new ones are considered. It also exists a large amount of interdependencies among the firm’s products, because the output of one particular plant can be used as an input to the production of another plant in the same or different regions and to satisfy the final demand. The decision makers involved in the planning process should identify several objectives. Then, multiple objective programming is used for making trade-offs among the economic and operational factors considered. To define the interval criteria weights into the model we utilized the Analytic Hierarchy Process to bring them closer to the decision makers preferences. This work was sponsored by the Institut National Polytechnique de Toulouse, France, when the author was Associate Professor at the Département Génie des Systèmes Industriels.     

A study of high-level managerial decision processes, with implications for MCDM research

We study six real-world major strategic decisions and discuss the role that analytic Multiple Criteria Decision Making (MCDM) models could play in helping decision makers structure and solve such problems. We have interviewed successful and well-educated managers who had access to quantitative decision models, but did not use them as part of their decision process. Our approach is a clinical one that takes a close look at the decision processes. We believe that the normative MCDM framework is oversimplified and does not always fit well with complex, real-world organizational decision processes. This may be one reason why decision tools are not used more widely for solving high-level decision problems. We believe that it would be worthwhile to revise some of the MCDM mainstream postulates and practices to make existing models and tools more suitable for practical purposes. The MCDM mainstream research has until today focused on the choice among alternatives. One should realize that MCDM models could also be used in creating alternatives, in assessing the importance of criteria, in providing the decision makers with “post-commitment support”, and as part of a devil's advocate approach.     

Criterion function for accessibility,uncertainty problems in regional land-use planning

One objective in regional planning is the creation of communities with great accessibility. Thus we should plan the locations of inhabitants and the activities of the region so that the total accessibility will be maximized subject to some restrictions. This is a quadratic programming problem, which can be solved by quadratic programming techniques, but we cannot then take into account the uncertainties of the problem.In this paper a new criterion function is proposed for accessibility, uncertainty problems in regional land-use planning. It is derived from Hurwicz's generalized maximin principle. Many advantages are gained, for the planning problem is separated into linear programming problems, the uncertainties are taken into consideration as in game theory and the methods of parametric programming are available.A simplified problem of the populations of three town areas is studied and the method is generalized for problems of many activities and areas.     

An all-linear programming relaxation algorithm for optimizing over the efficient set

The problem (P) of optimizing a linear function over the efficient set of a multiple objective linear program has many important applications in multiple criteria decision making. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, it appears that no precisely-delineated implementable algorithm exists for solving problem (P) globally. In this paper a relaxation algorithm is presented for finding a globally optimal solution for problem (P). The algorithm finds an exact optimal solution to the problem after a finite number of iterations. A detailed discussion is included of how to implement the algorithm using only linear programming methods. Convergence of the algorithm is proven, and a sample problem is solved.Research supported by a grant from the College of Business Administration, University of Florida, Gainesville, Florida, U.S.A.     

Combined MCDA–IP Approach for Project Selection in the Electricity Market

The European policy target of substantially increasing the share of renewable energy in electricity generation, in combination with national subsidization support schemes, has strongly motivated private investors towards this business sector. In Greece, this interest is particularly apparent in the case of wind energy exploitation, due to a favourable legislative framework and the important wind potential available in several Greek regions. In such endowed regions, a very high number of applications were submitted to the competent authority, most of which compete for the same most attractive (windy) sites. The selection among these applications is a multicriteria problem that has been solved with the support of a Decision-Aid tool combining the multicriteria classification method ELECTRE-TRI with Integer Linear Programming. The developed approach takes into account both, the performances of the applications to the evaluation criteria, as well as a number of technical and policy constraints.     

A large scale machine loading problem in flexible assembly

Flexible manufacturing is characterized by versatile work stations with minimum change over times and a versatile material handling system. The loading problem in flexible manufacturing is to assign tools, material, operations and jobs to work stations in order to minimize the total number of job-to-work station assignments. In this paper, we describe a special case of the general loading problem applied to flexible assembly and develop a discrete optimization model. We then discuss approaches for obtaining good heuristic solutions and present results for a large scale study.