Parameter Selection in Genetic Algorithms

In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications

Beam search based algorithm for scheduling machines and AGVs in an FMS

This paper presents a beam search based scheduling algorithm for a random FMS. The proposed algorithm considers finite buffer capacity, routing and sequence flexibilities and generates machine and AGV schedules in varying time windows. The performance of the algorithm is measured using makespan, flow time, and tardiness criteria under various experimental conditions

Bidding-Based Distributed Scheduling

In this study, we propose three bidding-based algorithms for distributed scheduling (DS). In DS, local planners (or local agents) make their own scheduling decisions under the supervision a global planner (or manager agent). The global planner has an overall system view of the system and acts as a conflict resolver. In the first proposed algorithm (Alg-B1), the manager agent initiates the bid for each schedulable operation in the work list. In the second algorithm (Alg-B2), the local agents themselves take initiative roles in the bidding process and volunteer to perform the certain operations. In the third algorithm (Alg-C), the manager agent carries out the bidding for each job. The performances of the..

Scheduling for non regular performance measure under the due window approach

In the last two decades, Just-In-Time (JIT) production has proved to be an essential requirement of world class manufacturing. This has made schedulers most concerned about the realization of a JIT environment. The JIT concept requires not only a penalty for backorder and lateness but also for earliness. This can be translated into non-regular scheduling objectives. The most obvious objective can be to minimize the deviation of completion times. Concerning earliness/tardiness problems, researchers have usually considered systems where jobs incur no penalty for completion at a certain point of time (i.e. due date). In practice, however, job completions can also be accepted without penalty within an interval in time, which is known as the due window. This paper studies the scheduling problems in terms of the non-regular measure, mean absolute deviation (MAD), under the due window approach. The study is conducted in a dynamic job shop environment. Furthermore, we propose two new rules that perform quite effectively for the MAD measure.Job shop scheduling Due windows MAD