Knowledge that Transforms

We present a pavement management expert system developed by the University of Wisconsin‐Madison and implemented within a geographical information system for the Wisconsin Department of Transportation. The system uses pavement data regularly collected on the state's 12,000 miles of highway to assist engineers, planners, and budget analysts' management decisions about pavements to be included in 6‐year improvement and 3‐year maintenance programs. The system has a three‐layer architecture. The lowest level suggests treatments for each of a large number of small segments of highway. The middle layer aggregates segments, suggests alternative treatments, and estimates the cost of each. The top layer prioritizes the projects and incorporates them into intermediate‐range plans. The geographical information system environment enables integration of existing databases within the system using a topologically structured geographic database and specialized software.

Operational‐level scheduling decisions on the factory floor include the release policy and the dispatching rule. Recent work by researchers suggests that factory performance depends heavily on the release policy. Several heuristic factory release rules have been developed that generate release decisions based on the status of the bottleneck machine. The heuristic rules perform very well in reducing the work‐in‐process inventory. However, factory output schedules generated by these rules generally fail to match the demand pattern, creating excessive finished goods inventory and/or excessive backorders. To overcome these difficulties, a computationally efficient integer programming model is proposed for release scheduling.

This is a study of a single‐machine scheduling problem with the objective of minimizing the sum of a function of earliness and tardiness called the earliness and tardiness (ET) problem. I will show that if priority weights of jobs are proportional to their processing times, and if earliness and tardiness cost functions are linear, the problem will be equivalent to the total weighted tardiness problem. This proves that the et problem is np‐hard. In addition, I present a heuristic algorithm with worst case bound for the et problem based on the equivalence relation between the two. When earliness and tardiness cost functions are quadratic, I consider the problem for a common due date for all jobs and for different job due dates. In general, the et problem with quadratic earliness and tardiness cost functions and all job weights equal to one is np‐hard. I show that in many cases, when weights of jobs are proportional to their processing times, the problem can be solved efficiently. In the published results on the et problem with quadratic earliness and tardiness cost functions other researchers have assumed a zero starting time for the schedule. I discuss the advantages of a nonzero starting time for the schedule.

Work flows in a job shop are influenced by the load per release and time interval between release factors. We focus on the latter factor, job release times. Building on Elvers' work, this study evaluates the impact of different job release time distributions on shop performance. Using a computer simulation of a random job shop and a full factorial experimental design, we demonstrate that the type of distribution does affect performance–a finding consistent with results from job shops characterized by good shop floor control practices. These findings are explained by examining the shape and variance traits of the underlying job release time distributions.

Over the past few years we have witnessed a sharp decline in the domestic and international market share of U.S. manufacturing organizations. The decline in U.S. manufacturing has provided an avalanche of criticism and attention in the literature and popular press. Recently, however, several organizations have implemented new work‐force management policies to reverse the decline. We describe the case of Rohm and Haas Kentucky, Incorporated, a plant that has implemented work teams. The outcomes of these innovations include a decline in worker grievances and turnover, an improvement in the safety record of the plant, as well as an increase in productivity.

We consider an environment where a production facility modeled as a single machine needs to assign delivery dates to several orders and find a feasible sequence. Tardy jobs are not allowed. The delivery dates are to be at prespecified fixed intervals. The objective is to minimize the due date penalty and the cost of earliness. We provide a dynamic programming‐based solution procedure that runs in polynomial time. We develop several dominance results that reduced the computational requirement by an order of magnitude in our computational study.