Results of a beer game experiment: Should a manager always behave according to the book?

We investigate the effect of the literature suggested optimal values of the parameters of a dynamic decision‐making heuristic in the presence of semirationally managed supply chain echelons using a soft coded one‐to‐one version of The Beer Game as an experimental platform. According to the counterintuitive results obtained in this study, it is possible for a “rational manager” to obtain higher costs than the costs generated by a “semirational manager.” Thus, the results do not support the use of the well‐established decision parameter values for the echelon of concern if the other echelons' inventories are managed suboptimally. © 2015 Wiley Periodicals, Inc. Complexity 21: 190–199, 2016     

On the number of iterations of local improvement algorithms

A general model of local improvement algorithms in combinatorial optimization accurately confirms performance characteristics often observed in individual cases. The model predicts exponentially bad worst case and low order polynomial average run times for single optimum problems including some linear complementarity problems and linear programming. For problems with multiple local optima, most notably those that are NP-complete, average speed is linearly bounded but accuracy is poor.     

ON THE MINIMUM FEASIBLE GRAPH FOR FOUR SETS

ＯＮＴＨＥＭＩＮＩＭＵＭＦＥＡＳＩＢＬＥＧＲＡＰＨＦＯＲＦＯＵＲＳＥＴＳＸＵＹＩＮＦＥＮＧＡＮＤＦＵＸＩＡＯＢＩＮＧＡｂｓｔｒａｃｔ：ＧｉｖｅｎａｃｏｍｐｌｅｔｅｇｒａｐｈｗｉｔｈｖｅｒｔｅｘｓｅｔＸａｎｄｓｕｂｓｅｔｓＸ＿１，Ｘ＿２，．．．，Ｘ＿ｎ...     

An efficient method to determine the optimal configuration of a flexible manufacturing system

A frequently encountered design issue for a flexible manufacturing system (FMS) is to find the lowest cost configuration, i.e. the number of resources of each type (machines, pallets, ...), which achieves a given production rate. In this paper, an efficient method to determine this optimal configuration is presented. The FMS is modelled as a closed queueing network. The proposed procedure first derives a heuristic solution and then the optimal solution. The computational complexity for finding the optimal solution is very reasonable even for large systems, except in some extreme cases. Moreover, the heuristic solution can always be determined and is very close (and often equal) to the optimal solution. A comparison with the previous method of Vinod and Solberg shows that our method performs very well.     

The master bay plan problem: a solution method based on its connection to the three-dimensional bin packing problem

This paper addresses the problem of determining stowage plansfor containers in a ship, referred to as the Master Bay PlanProblem (MBPP). MBPP is NP-complete. We present a heuristic method for solvingMBPP based on its relation with the three-dimensional bin packingproblem (3D-BPP), where items are containers and bins are differentportions of the ship. Our aim is to find stowage plans, takinginto account structural and operational constraints relatedto both the containers and the ship, that minimize the timerequired for loading all containers on board. A validation of the proposed approach with some test casesis given. The results of real instances of the problem involvingmore than 1400 containers show the effectiveness of the proposedapproach for large scale applications.     

A Tabu Search Heuristic Procedure for Solving the Transportation Problem with Exclusionary Side Constraints

A new heuristic procedure for the transportation problem with exclusionary side constraints is developed and implemented. Tabu search, a meta-heuristic method, is used to guide the search to follow a path selectively to prevent from being trapped at local optimal solutions in order to find a global optimal or near global optimal solution. The simplex method on a graph is employed to lead the search from one solution to an adjacent solution in order to take advantage of the underlying network structure of the problem. In the procedure, net changes in cost and in infeasibility are used to measure the attractiveness of a move, and strategic oscillation is used to implement the intensification and diversification functions. A computational experiment was conducted to test the performance of the heuristic procedure and substantial computational results are reported. These results show that the new heuristic procedure finds very good quality solutions and outperforms an existing heuristic procedure in terms of both solution quality and CPU time.     

A machine learning approach to process improvement in a telecommunications company

Redesigning and improving business processes to better serve customer needs has become a priority in service industries as they scramble to become more competitive. This paper describes an approach to process improvement that is being developed collaboratively by applied researchers at US WEST, a major telecommunications company, and the University of Colorado. Motivated by the need to streamline and to add more quantitative power to traditional quality improvement processes, the new approach uses an artificial intelligence (AI) statistical tree growing method that uses customer survey data to identify operations areas where improvements are expected to affect customers most. This AI/statistical method also identifies realistic quantitative targets for improvement and suggests specific strategies (recommended combinations of actions) that are predicted to have high impact. This research, funded in part by the Colorado Advanced Software Institute (CASI) in an effort to stimulate profitable innovations, has resulted in a practical methodology that has been used successfully at US WEST to help set process improvement priorities and to guide resource allocation decisions throughout the company.     

Lotto design tables

An LD(n,k,p,t;b) lotto design is a set of b k‐sets (blocks) of an n‐set such that any p‐set intersects at least one k‐set in t or more elements. Let L(n,k,p,t) denote the minimum number of blocks in any LD(n,k,p,t;b) lotto design. We will list the known lower and upper bound theorems for lotto designs. Since many of these bounds are recursive, we will incorporate this information in a set of tables for lower and upper bounds for lotto designs with small parameters. We will also use back‐track algorithms, greedy algorithms, and simulated annealing to improve the tables. © 2002 Wiley Periodicals, Inc. J Combin Designs 10: 335–359, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jcd.10020     

Worst-case analysis of the differencing method for the partition problem

We prove that the worst-case performance ratio of the Karmarkar-Karp differencing method for the Partition Problem is 7/6     

Heuristic solutions to polynomial moment problems with some convex entropic objectives

We are asking to estimate a nonnegative density on ^m , given some of its algebraic or trigonometric moments. The maximum entropy method is to introduce an entropy-like objective function and then solve a convex minimization programming with some linear constraints. In the existing literature, Newton's method or some other iteration methods are used to solve its dual problem. In this paper, special structures of the problem have been discovered when we use some particular entropies, which include Boltzmann-Shannon entropy and Burg's entropy. Useful linear relationships among the moments help us to set up very fast and effective algorithms. Numerical computations and comparison are also presented.