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Reports on a benchmarking study conducted to quantify how well companies operating in various sectors of Turkish industry match up to best practice, both in the practices they adopt and in the operational outcomes that result, and to test the hypothesis that the closer a company is to best practice, the more likely it is for that company to achieve higher business performance. The survey conducted in 1997 and 1998 included 82 companies from the Turkish electronics, cement, automotive sectors and part and component suppliers to the appliance industry. For data gathering, employs the Competitive Strategies and Best Practices Benchmarking Questionnaire, supported by some follow‐up interviews and one‐day site visits. Classifies two small groups of companies as leaders and laggers, depending on how close they were to best practice. Shows that the leaders have performed better than the laggers in adopting best manufacturing practices and in the achievement of high performance levels. The leaders also have achieved substantially higher business performance than the laggers. Furthermore, observes that large‐sized companies outperform the rest both in terms of their success in implementing best manufacturing practices and in achieving high operational outcomes and that there is no appreciable difference between industrial sectors in implementing best manufacturing practices and in achieving high operational outcomes.

Proposes a heuristic iterative scheduling algorithm for the
resource constrained project scheduling problem. Considers a general
model where activities are represented by multiple operating modes and
each operating mode is constituted of different activity durations and
resource requirements. The performance measures considered are the
minimization of project duration and the maximization of net present
value (NPV). In the cash flow model assumed, activity expenditures take
place at their start times and the project payment is made on its
completion. The iterative scheduling algorithm consists of forward/
backward scheduling passes, where consecutive scheduling passes are
linked by updated activity time windows. The iterative algorithm is
supported by a conflict‐based activity selection technique called local
constraint based analysis (LCBA). A considerable amount of improvement
in both performance criteria is observed when the results of the
iterative algorithm are compared with the results given by the initial
forward schedule.

Considers the resource‐constrained project scheduling problem where cash inflows and outflows are tied to the occurrence of events. The objective is the maximization of the project net present value (NPV) as well as the minimization of project tardiness in the presence of a project due date. Develops hybrid scheduling rules with both NPV and tardiness considerations to enhance both objectives. Experiments extensively with a set of benchmark problems originally designed for the objective of minimizing the project duration. Demonstrates that thje hybrid rules developed here are superior in performance with respect to both objectives when compared with well known rules which are developed for the two objective of minimizing the project duration. Demonstrates that the hybrid rules developed here are superior in performance with respect to both objectives when compared with well‐known rules which are developed for the two objectives taken individually. Furthermore, the iterative algorithm improves the performance of all tested rules significantly.

AbstractThe client‐contractor bargaining problem addressed here is in the context of a multi‐mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity‐on‐Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two‐party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009