Ludo Van der Heyden

Fair Process: Striving for Justice in Family Business

The social science and business literatures on procedural justice or fair process attest that improvements in procedural fairness can be expected to improve both a firms performance and the commitment and trust of the individuals involved with it. This article examines the relevance of procedural justice for family business. When a family is an influential component of a particular business system, the application of justice is typically rendered more complex than might be the case for nonfamily firms. Different criteria (need, merit, and equality) guide the application of distributive justice among families, firms, and shareholders. This divergence in criterion also lies at the heart of many conflicts inside the family business. In this article, we argue that the application of procedural justice reduces occurrences of conflict and, in some cases, may eliminate conflict altogether. We propose a definition of fair process that extends and enriches the one existing in the literature. We offer five fundamental criteria essential to the effectiveness of fair process in family firms. We conclude with a series of case studies that illustrate typical questions faced inside family businesses. We show that a lack of fairness in the decision and managerial processes governing these businesses and their associated families is a source of conflict. We describe how increasing fair process practices improves the performance of these businesses while also increasing the satisfaction of those associated with them.

A variable dimension algorithm for the linear complementarity problem

A variable dimension algorithm is presented for the linear complementarity problems − Mz = q; s,z ≥ 0; sizi = 0 fori = 1,2, ⋯ ,n. The algorithm solves a sequence of subproblems of different dimensions, the sequence being possibly nonmonotonic in the dimension of the subproblem solved. Every subproblem is the linear complementarity problem defined by a leading principal minor of the matrixM. Index-theoretic arguments characterize the points at which nonmonotonic behavior occurs.

Scheduling Jobs with Exponential Processing and Arrival Times on Identical Processors so as to Minimize the Expected Makespan

In this note we study a problem related to the scheduling of jobs on p identical processors p ≥ 2. Jobs arrive randomly, interarrival times being exponentially distributed. The processing times are also exponential with mean drawn upon arrival from an arbitrary distribution function. Preemptions are allowed. The objective is to minimize the expected time until this M/G/p queuing system is first empty. We prove that an optimal policy processes jobs currently in the system in decreasing order of expected processing time.

Index Policies for Stochastic Search in a Forest with an Application to R&D Project Management

This paper concerns a stochastic search problem in a forest. As motivation, consider the issue of investing in a research-and-development project. Each activity that could be undertaken in the project is represented as an edge in a forest. Each edge has a cost of being attempted and a probability of success. An edge can be attempted if each of its predecessors has been attempted, and if each of those attempts has succeeded. The overall project succeeds if a path is found from a stem to a leaf of the forest all of whose edges are successful. Overall success yields an economic benefit. The problem is to find an investment strategy that maximizes expected utility, either with a linear or an exponential utility function. This problem will be shown to have a simple solution. Each edge will be assigned an index such that expected utility is maximized by attempting, at each opportunity, an edge whose index is most positive, terminating the search when no edges remain whose indices are positive. These indices are nested in a way that makes them quick to compute.

Algorithms for the Linear Complementarity Problem Which Allow an Arbitrary Starting Point

The linear complementarity problem with data q ɛ Rn and M ɛ Rn×n consists in finding two vectory s and z in Rn such that(1.1)

Scarf's Procedure for Integer Programming and a Dual Simplex Algorithm

{\text{s = Mz + q ,}}

A Refinement Procedure for Computing Fixed Points Using Scarf's Primitive Sets

(1.1) (1.2)