The problem of predicting interregional commodity movements and the regional prices of these commodities has intrigued economists, geographers and operations researchers for years. In 1838, A. A. Cournot (1838) discussed the equilibrium of trade between New York and Paris and noted how the equilibrium prices depended upon the transport costs. Enke (1951) recognized that this problem of predicting interregional flows and regional prices could be formulated as a network problem, and in 1952, . Paul Samuelson (1952) used the then recent advances in mathe­ matical programming to formalize the spatial price equilibrium problem as a nonlinear optimization problem. From this formula­ tion, Takayama and Judge (1964) derived their quadratic program­ ming representation of the spatial price equilibrium problem, which they and other scholars then applied to a wide variety of problem contexts. Since these early beginnings, the spatial price equilibrium problem has been widely studied, extended and applied; the paper by Harker (1985) reviews many of these results. In recent years, there has been a growing interest in this problem, as evidenced by the numerous publications listed in Harker (1985). The reasons for this renewed interest are many. First, new applications of this concept have arisen which challenge the theoretical underpinnings of this model. The spatial price equilibrium concept is founded on the assumption of perfect or pure competition. The applications to energy markets, steel markets, etc. have led scholars to rethink the basic structure of this model.

The problem of predicting interregional commodity movements and the regional prices of these commodities has intrigued economists, geographers and operations researchers for years. In 1838, A. A. Cournot (1838) discussed the equilibrium of trade between New York and Paris and noted how the equilibrium prices depended upon the transport costs. Enke (1951) recognized that this problem of predicting interregional flows and regional prices could be formulated as a network problem, and in 1952, . Paul Samuelson (1952) used the then recent advances in mathe­ matical programming to formalize the spatial price equilibrium problem as a nonlinear optimization problem. From this formula­ tion, Takayama and Judge (1964) derived their quadratic program­ ming representation of the spatial price equilibrium problem, which they and other scholars then applied to a wide variety of problem contexts. Since these early beginnings, the spatial price equilibrium problem has been widely studied, extended and applied; the paper by Harker (1985) reviews many of these results. In recent years, there has been a growing interest in this problem, as evidenced by the numerous publications listed in Harker (1985). The reasons for this renewed interest are many. First, new applications of this concept have arisen which challenge the theoretical underpinnings of this model. The spatial price equilibrium concept is founded on the assumption of perfect or pure competition. The applications to energy markets, steel markets, etc. have led scholars to rethink the basic structure of this model.

Management science is a di scipl ine dedicated to the development of techniques that enable decision makers to cope with the increasing complexity of our world. The early burst of excitement which was spawned by the development and successful applications of linear programming to problems in both the public and private sectors has challenged researchers to develop even more sophisticated methods to deal with the complex nature of decision making. Sophistication, however, does not always trans 1 ate into more complex mathematics. Professor Thomas L. Saaty was working for the U. S. Defense Department and for the U. S. Department of State in the late 1960s and early 1970s. In these positions, Professor Saaty was exposed to some of the most complex decisions facing the world: arms control, the Middle East problem, and the development of a transport system for a Third­ World country. While having made major contributions to numerous areas of mathematics and the theory of operations research, he soon realized that one did not need complex mathematics to come to grips with these decision problems, just the right mathematics! Thus, Professor Saaty set out to develop a mathematically-based technique for analyzing complex situations which was sophisticated in its simplicity. This technique became known as the Analytic Hierarchy Process (AHP) and has become very successful in helping decision makers to structure and analyze a wide range of problems.

The problem of predicting interregional commodity movements and the regional prices of these commodities has intrigued economists, geographers and operations researchers for years. In 1838, A. A. Cournot (1838) discussed the equilibrium of trade between New York and Paris and noted how the equilibrium prices depended upon the transport costs. Enke (1951) recognized that this problem of predicting interregional flows and regional prices could be formulated as a network problem, and in 1952, . Paul Samuelson (1952) used the then recent advances in mathe­ matical programming to formalize the spatial price equilibrium problem as a nonlinear optimization problem. From this formula­ tion, Takayama and Judge (1964) derived their quadratic program­ ming representation of the spatial price equilibrium problem, which they and other scholars then applied to a wide variety of problem contexts. Since these early beginnings, the spatial price equilibrium problem has been widely studied, extended and applied; the paper by Harker (1985) reviews many of these results. In recent years, there has been a growing interest in this problem, as evidenced by the numerous publications listed in Harker (1985). The reasons for this renewed interest are many. First, new applications of this concept have arisen which challenge the theoretical underpinnings of this model. The spatial price equilibrium concept is founded on the assumption of perfect or pure competition. The applications to energy markets, steel markets, etc. have led scholars to rethink the basic structure of this model.

The problem of predicting interregional commodity movements and the regional prices of these commodities has intrigued economists, geographers and operations researchers for years. In 1838, A. A. Cournot (1838) discussed the equilibrium of trade between New York and Paris and noted how the equilibrium prices depended upon the transport costs. Enke (1951) recognized that this problem of predicting interregional flows and regional prices could be formulated as a network problem, and in 1952, . Paul Samuelson (1952) used the then recent advances in mathe­ matical programming to formalize the spatial price equilibrium problem as a nonlinear optimization problem. From this formula­ tion, Takayama and Judge (1964) derived their quadratic program­ ming representation of the spatial price equilibrium problem, which they and other scholars then applied to a wide variety of problem contexts. Since these early beginnings, the spatial price equilibrium problem has been widely studied, extended and applied; the paper by Harker (1985) reviews many of these results. In recent years, there has been a growing interest in this problem, as evidenced by the numerous publications listed in Harker (1985). The reasons for this renewed interest are many. First, new applications of this concept have arisen which challenge the theoretical underpinnings of this model. The spatial price equilibrium concept is founded on the assumption of perfect or pure competition. The applications to energy markets, steel markets, etc. have led scholars to rethink the basic structure of this model.

Management science is a di scipl ine dedicated to the development of techniques that enable decision makers to cope with the increasing complexity of our world. The early burst of excitement which was spawned by the development and successful applications of linear programming to problems in both the public and private sectors has challenged researchers to develop even more sophisticated methods to deal with the complex nature of decision making. Sophistication, however, does not always trans 1 ate into more complex mathematics. Professor Thomas L. Saaty was working for the U. S. Defense Department and for the U. S. Department of State in the late 1960s and early 1970s. In these positions, Professor Saaty was exposed to some of the most complex decisions facing the world: arms control, the Middle East problem, and the development of a transport system for a Third­ World country. While having made major contributions to numerous areas of mathematics and the theory of operations research, he soon realized that one did not need complex mathematics to come to grips with these decision problems, just the right mathematics! Thus, Professor Saaty set out to develop a mathematically-based technique for analyzing complex situations which was sophisticated in its simplicity. This technique became known as the Analytic Hierarchy Process (AHP) and has become very successful in helping decision makers to structure and analyze a wide range of problems.