Optimizing real-time vehicle sequencing of a paint shop conveyor system

A discrete event simulation model and a decision optimizer that were developed for a General Motors paint shop conveyor system are presented. The simulation model interacts with the decision optimizer at four critical points in the system, trying to regroup batches of different colored vehicles. The decision optimizer employs dynamic programming and integer programming to optimize vehicle routing policies. Simulation results of the current decision making policies are compared with those of the proposed optimized policies showing that the number of paint head changes can be significantly reduced resulting in substantial savings on paint head cleaners and paint.     

Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios

We consider the design of a multicommodity flow network, in which point-to-point demands are routed across the network subject to link capacity restrictions. Such a design must build enough capacity and diverse routing paths through the network to ensure that feasible multicommodity flows continue to exist, even when components of the network fail. In this paper, we examine several methodologies to optimally design a minimum-cost survivable network that continues to support a multicommodity flow under any of a given set of failure scenarios, where each failure scenario consists of the simultaneous failure of multiple arcs. We begin by providing a single extensive form mixed-integer programming formulation for this problem, along with a Benders decomposition algorithm as an alternative to the extensive form approach. We next investigate strategies to improve the performance of the algorithm by augmenting the master problem with several valid inequalities such as cover constraints, connectivity constraints, and path constraints. For the smallest instances (eight nodes, 10 origin–destination pairs, and 10 failure scenarios), the Benders implementation consumes only 10% of the time required by the mixed-integer programming formulation, and our best augmentation strategy reduces the solution time by another 50%. For medium- and large-sized instances, the extensive form problem fails to terminate within 2 h on any instance, while our decomposition algorithms provide optimal solutions on all but two problem instances.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

The cross-cultural adjustment process of expatriate families in a multinational organization: a family system theory perspective

This case study examined the relationship between the family flexibility of expatriates in a multinational corporation and their cross-cultural adjustment, as well as the stressors experienced by the expatriate, spouse, and children during the international transition. Family flexibility was negatively correlated with cross-cultural adjustment as perceived by the participating expatriates. All five cross-cultural adjustment dimensions (cultural, psychological, organizational, personal and relational) had a statistically significant relationship with family flexibility. Expatriate families identified cultural, relational, and psychological stressors as having the greatest impact on their cross-cultural adjustment. The components of family flexibility (roles, rules, assertiveness and leadership) played a key role in the cross-cultural adjustment of the expatriate, spouse and children. These findings provide insights to organizations and their human resource development professionals as well as to expatriates and their families on how family flexibility impacts cross-cultural adjustment – insights that could lead to the development of appropriate support and development mechanisms.     

The design of socially optimal decisions in a consensus scenario

Several situations of conflict between basic social principles can crop up during a consensus searching process. The majority principle and respect for minority groups is a possible example of a conflictive situation between two social principles. In this paper, we outline a specific consensus searching scenario, where individual preferences are expressed by “pairwise” comparison matrices. The set of compromise consensuses between the majority and minority principles is determined using a procedure based upon an adaptation of Yu's p-metric distances. Finally, we use three different theoretical approaches – utility theory, p-metric distance functions and bargaining theory – to obtain the social optimum from the set of compromise consensuses. The links and differences among the three approaches are analysed. Finally, the working of the proposed theoretical framework is illustrated with the help of a forestry case study.     

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label “Near Rational,”“Fatalist,” and “Confused.” There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.     

Price competition,cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers

This paper studies the coordination of a supply chain with one manufacturer and two competing retailers after the production cost of the manufacturer was disrupted. We consider two coordination mechanisms: an all-unit quantity discount and an incremental quantity discount. For each mechanism, we develop the conditions under which the supply chain is coordinated and discuss how the cost disruption may affect the coordination mechanisms. For the all-unit quantity discount scheme, we find that the manufacturer charges the lower-cost retailer for a lower unit wholesale price in order to induce him to order more products. If the costs of two retailers have a remarkable difference, then the all-unit quantity discount scheme cannot coordinate the supply chain with disruptions. While the cost disruption may affect the wholesale prices, order quantities as well as retail prices, it is optimal for the supply chain to keep the original coordination mechanism if the production cost change is sufficiently small. The model is also extended to the case with both cost and demand disruptions. The equilibrium strategies of the retailers are investigated when the manufacturer cannot timely react to the disruptions such that she has to keep the original mechanism. We illustrate the results by numerical examples.     

A decision-based approach: application to the automatic design of process planning

The notion of centre of decision presented in this article defines a conceptual and generic model enabling us to represent and use expert knowledge. It is suitable when the problem to be solved can be broken down into a sum of interdependent subproblems. Actions can be planned progressively and previous decisions can he modified by a backtrack mechanism. This principle is iterative. The structure is made up of many independent rules and an action plan. The customization of the application is possible even if the software evolves with future updates. This model has been applied in the LURPA-TOUR software which is a computer-aided process planning system for rotational parts.