Shop scheduling involves the processing of a set of jobs on a set of processing stages with a set of machines available at each stage. Depending on how the processing sequence of each job at each stage is specified, shop scheduling can be classified as job shop, flow shop, or open shop scheduling. This study presents a tabu search algorithm to solve job/flow/open shop scheduling problems with parallel and identical machines at each stage. The objective is to minimize the makespan. To represent a solution, we use a permutation vector V of size N (the number of jobs considered). Correspondingly, we develop a procedure to construct a complete schedule based on the given permutation vector V. The algorithm is tested extensively on various sets of instances. We compare the performance of the proposed algorithm with the corresponding lower bound for the problems. The experimental results show that the proposed algorithm yields excellent results. Compared with the existing approaches for solving job shop, flow shop, or open shop problems, the proposed method is advantageous in terms of its simplicity. In addition, the proposed algorithm can be implemented for job shop, flow shop, and open shop problems with parallel machines at each stage, depending on how the operating sequence of the jobs at each stage is defined.

We investigate the problem of scheduling N jobs on parallel machines in L successive stages with limited buffer capacities between stages. The primary objective is to find a schedule that would minimize the makespan. This problem is shown to be NP-hard in the strong sense. We develop a tabu search algorithm for this problem in which the search is limited to the space of permutation vectors of size N. This vector represents the order in which the given set of jobs are performed in the first stage, and we propose a procedure to construct a complete schedule associated with every permutation vector. We conduct an extensive computational experiment using randomly generated instances with different structures and different buffer capacities. We also solve several specific instances of the problem from the open literature. All empirical evidence suggest that the proposed algorithm is an effective method for solving this problem.

We study two problems in the context of scheduling N jobs on parallel machines in L successive stages with the primary objective of minimizing the makespan. In the first problem we assume that there are unlimited buffer capacities between successive stages. We refer to this problem as the FSPM problem. In the second problem, we assume that the buffer capacities are finite and given, and we refer to it as FSPM/ b problem. Both problems are shown to be NP-hard. We focus our attention in developing heuristic search procedures for solving both problems. These procedures include the local improvement, the genetic algorithm, and the tabu search. In this context, we use two different solution representations, namely the matrix representation and the vector representation. To evaluate these procedures on an empirical basis, we carry out an extensive computational experiment consisting of two different types of data sets. The problem instances in one data set are constructed in such a manner that the corresponding optimal makespan can be easily determined. The optimal makespan of the instances in the second data set are not known. We also develop a set of lower bounds for the optimal makespan of the problem. The experimental results with the procedures that we propose show that the search procedures which are based on vector representation perform reasonably well. Among the various procedures based on this representation, our tabu search implementation was the most effective. In the context of FSPM/b problem, we also conduct an experiment with a third set of data which is available in the open literature. From the experimental results, we demonstrate the characteristic of the proposed algorithms with various buffer configurations.

Because the energy demand for cooling usually occurs when solar energy is most available, using solar energy as the main energy input for cooling systems shows promise. A literature study of various available cooling technologies that can use solar energy as the main energy input shows that double-effect lithium-bromide/water (LiBr/H{sub 2}O) absorption cooling systems give the relatively high performance that is needed for reduced collector areas. To further study double-effect LiBr/H{sub 2}O absorption cooling systems, a computer model of the system was developed. This model was then coupled with a model of a solar collector system. The performance of the system is presented as the solar fraction that is the ratio of the solar energy to the total energy needed for the cooling system. Results are shown for various sky clearness indices, average ambient temperatures, and collector area to cooling system capacity ratios.

Since commercially-available, double-effect, absorption cooling systems give relatively high performance for using solar energy or other medium-temperature sources, their performance was simulated and studied. To evaluate the cooling system performance, two objective functions were established: the system performance (COP) and the system cost. The system cost was used as the objective function to determine the optimum design of the system, while the COP was used to evaluate the effects of each variable on the system performance. The system optimization shows that there is an economic optimum heat-transfer area for each heat exchanger. Further study shows that this is a global minimum cost of the system. The best COPs that could be achieved by changing the heat-transfer areas and the inlet hot water temperature vary between 1.4 and 1.5. Higher COPs of approximately 1.6 were achieved if higher chilled water inlet temperatures or lower cooling water temperatures are used. These conditions are not desirable since higher chilled water inlet temperatures are not useful for cooling, and lower cooling water inlet temperatures are not usually available.

We investigate the problem of scheduling N jobs on parallel machines in L successive stages with limited buffer capacities between stages. The primary objective is to find a schedule that would minimize the makespan. This problem is shown to be NP -hard in the strong sense. We develop a tabu search algorithm for this problem in which the search is limited to the space of permutation vectors of size N. This vector represents the order in which the given set of jobs are performed in the first stage, and we propose a procedure to construct a complete schedule associated with every permutation vector. We conduct an extensive computational experiment using randomly generated instances with different structures and different buffer capacities. We also solve several specific instances of the problem from the open literature. All empirical evidence suggest that the proposed algorithm is an effective method for solving this problem. � 2003 Elsevier B.V. All rights reserved.