V. Jorge Leon

ROBUSTNESS MEASURES AND ROBUST SCHEDULING FOR JOB SHOPS

Abstract A robust schedule is defined as a schedule that is insensitive to unforeseen shop floor disturbances given an assumed control policy. In this paper, a definition of schedule robustness is developed which comprises two components: post-disturbance make-span and post-disturbance makespan variability. We have developed robustness measures and robust scheduling methods for the case where a “right-shift” control policy is used. On occurrence of a disruption, the right-shift policy maintains the scheduling sequence while delaying the unfinished jobs as much as necessary to accommodate the disruption. An exact measure of schedule robustness is derived for the case in which only a single disruption occurs within the planning horizon. A surrogate measure is developed for the more complex case in which multiple disruptions may occur. This surrogate measure is then embedded in a genetic algorithm to generate robust schedules for job-shops. Experimental results show that robust schedules significantly outper...

Strength and adaptability of problem-space based neighborhoods for resource-constrained scheduling

In this paper, Resource Constrained Scheduling (RCS) consists of scheduling activities on scarce resources, each activity may require more than one resource at a time, and each resource is available in the same quantity throughout the planning period. This paper described a methodology for RCS that can be easily adapted to consider different regular measures of performance. The solution approach is local search using a recent development published in the literature; namely, problem-space based neighborhoods. Computational results are encouraging when searching these spaces using simple local search techniques. Further improvements are explored through the use of a genetic algorithm. In both cases, close-to-optimal solutions are found for standard problems from the literature. The adaptability of the methodology is demonstrated using makespan and mean tardiness as performance measures.ZusammenfassungIn dieser Arbeit behandeln wir Probleme der Netzplantechnik bzw. des Projektmanagements mit knappen Ressourcen. Dabei steht jede Ressource während des Planungszeitraumes in derselben Quantität zur Verfügung, und jede Aktivität kann zu jedem Zeitpunkt mehr als eine Ressource beanspruchen. Wir beschreiben eine Vorgehensweise, die sich leicht auf verschiedene reguläre Zielsetzungen übertragen läßt (hier werden die Minimierung der Zykluszeit sowie die Minimierung der mittleren Terminüberschreitung als Ziele betrachtet). Die Methode basiert auf einem lokalen Suchverfahren unter Verwendung problemspezifischer Nachbarschaften. Mit Hilfe einfacher Suchverfahren erhält man bereits gute Ergebnisse; weitere Verbesserungen lassen sich mit genetischen Algorithmen erzielen. In beiden Fällen haben wir für Standardprobleme aus der Literatur Lösungen erhalten, deren Zielfunktionswerte nahe an den jeweiligen Optima liegen.

An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations

In this study, we present a new formulation of the generalized flow-refueling location model that takes vehicle range and trips between origin–destination pairs into account. The new formulation, based on covering the arcs that comprise each path, is more computationally efficient than previous formulations or heuristics. Next, we use the new formulation to provide managerial insights for some key concerns of the industry, such as: whether infrastructure deployment should focus on locating clusters of facilities serving independent regions or connecting these regions by network of facilities; what is the impact of uncertainty in the origin–destination demand forecast; whether station locations will remain optimal as higher-range vehicles are introduced; and whether infrastructure developers should be willing to pay more for stations at higher-cost intersections. Experiments with real and random data sets are encouraging for the industry, as optimal locations tend to be robust under various conditions.

AN ADAPTABLE PROBLEM-SPACE-BASED SEARCH METHOD FOR FLEXIBLE FLOW LINE SCHEDULING

Problem-space-based neighborhoods have been recently suggested in the literature for the approximate solution of scheduling problems. This paper explores how effectively these neighborhoods can be adapted to different regular measures of performance in the context of flexible flow line scheduling. Specifically, makespan and mean tardiness are used in the experiments. Near-optimal solutions and significant improvements in the performance of single-pass heuristics are found when searching these spaces with simple local search techniques for industrial and randomly generated problems.

Replanning and analysis of partial setup strategies in printed circuit board assembly systems

This paper considers the operation of component placement equipment for the assembly of printed circuit boards (PCBs) in a medium-volume, medium-variety manufacturing environment. It focuses on the setup management and operational planning issues associated with productive use of these expensive resources. The concept of replanning is introduced to adapt to changes in the production environment by explicitly considering the initial state of the system. The partial setup strategy is suggested as a means of efficient adaptation and as a strategy that subsumes other setup strategies encountered in practice and the literature. These concepts are applied to the optimization of a single-placement machine producing multiple products. The results of using partial setups are compared with other commonly used strategies. Experimental results suggest significant gains at the singlemachine level. Future research is being pursued to improve the solution procedures and extend these replanning concepts to the line level.

Decision-making and cooperative interaction via coupling agents in organizationally distributed systems

This paper develops a methodology for decision-making in organizationally distributed systems where decision authorities and information are dispersed in multiple organizations. Global performance is achieved through cooperative interaction and partial information sharing among organizations. The information shared among organizations is contrived using modified Lagrangian relaxation techniques. Novel to the methodology is that no single master problem with a global view of the system is required to guide the decision process. Rather, multiple artificial decision entities, termed Coupling Agents, are associated with subsets of coupling constraints. The proposed generic model can be applied to decision-making problems with a variety of mathematical structures. In this paper the methodology is applied to parameter design problems to illustrate the behavior of the proposed methodology in the realm of non-linear optimization.

Single-vendor multi-buyer inventory coordination under private information

This paper considers the problem of coordinating a single-vendor multi-buyer inventory system when there are privacy restrictions in the information required to solve the problem. The objective function and cost parameters of each facility are regarded as private information that no other facilities in the system have access to. Moreover, each facility is responsible to specify its own replenishment policy. The objective is to minimize the total average setup/ordering and inventory-related cost. Solution methodologies under private and global information are developed to find two types of nested power-of-two stationary policies. The first policy assumes all the buyers must replenish simultaneously. The second policy is a more general case where the common replenishment assumption is relaxed. A simple form of information exchange is uncovered that allows the solution methodologies for private and global information yield the same results. The experimental results suggest that the performance of the proposed heuristics is comparable or better than an existing method.

A distributed scheduling methodology for a two-machine flowshop using cooperative interaction via multiple coupling agents

This paper presents a distributed scheduling methodology for a two-machine flowshop problem. It is assumed that the decision authorities and information are distributed in multiple subproduction systems that must share two machines to satisfy their demands. The associated scheduling problems are modeled using 0/1 integer formulations, and the problem is solved using Lagrangian relaxation techniques modified to work in an environment where very limited information sharing is allowed. Specifically, no global upper-bound is known, no single decision entity has complete view of all the constraints that couple the participating subproduction systems, and there is no disclosure of local objectives and constraints. The main objective of the proposed algorithm is to find a compromise state where all coupling constraints and local constraints are satisfied, and the total sum of weighted completion time of jobs is minimized. The proposed methodology showed promising experimental results when compared to the traditional Lagrangian relaxation with subgradient method.

Scalable methodology for supply chain inventory coordination with private information

This paper considers the problems of coordinating serial and assembly inventory systems with private information where end-item demands are known over a finite horizon. In a private information environment, the objective function and cost parameters of each facility are regarded as private information that no other facilities in the system have access to. The solution approach decomposes the problem into separable subproblems such that the private information is partitioned as required. Global optimality is sought with an iterative procedure in which the subproblems negotiate the level of material flows between facilities. At the core of the solution procedure is a supplier-buyer link model that can be used as a building block to form other supply chain configurations. Experimental results show that the proposed methodology provides promising results when compared to competing methodologies that disregard information privacy.

Case study on culture and the implementation of manufacturing strategy in Mexico

Even under the best circumstances, organizational change generates resistance. When the change results from a company in one country purchasing a plant in a different country, the complexity of change management and the resistance to change increase considerably. This case analysis examines one such change effort. Initially, the effort failed because the management of the acquiring (U.S.) firm communicated ineffectively with workers in the acquired (Mexican) plant, failed to establish a reward system that encouraged acceptance of change, and ignored cultural and subcultural differences. However, management quickly learned from its mistakes and made the adjustments necessary to turn failure into success. Implications for cross-cultural change management are discussed.