In this paper, a REVIEW of MULTIPLE OBJECTIVE FLOWSHOP SCHEDULING Problems is introduced. Different version of the problem are classified according to different multi OBJECTIVE OPTIMIZATION approaches, FLOWSHOP assumptions, solving methods, and also OBJECTIVE functions terms. This paper can be considered as a complete and valuable reference for researchers in the field of SCHEDULING, especially MULTIPLE OBJECTIVE ones.

This paper deals with a bi-OBJECTIVE hybrid no-wait FLOWSHOP SCHEDULING problem minimizing the make span and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in a reasonable computational time by using traditional approaches and OPTIMIZATION tools is extremely difficult. This paper, therefore, presents a new multi-OBJECTIVE simulated annealing algorithm (MOSA). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-OBJECTIVE performance measures. In fact, the performance of the algorithm is carefully evaluated in terms of the available algorithms by means of multi-OBJECTIVE performance measures and statistical tools. The results show that compared with other algorithms, a variant of our proposed MOSA produces a good performance.

As the main source of income and expenses of hospitals, operating rooms (ORs) are the engines of hospitals' economics and they have a significant impact on public health. Many papers concerned regarding OR planning and SCHEDULING problems, but they have not considerably applied the simulation-based OPTIMIZATION approach to solve the problems. In OR SCHEDULING problems, there are a number of ORs and some surgeons with different specialties and each surgeon has a waiting list of some patients that each surgery should be planned and scheduled on the days when relevant surgeons are available. In this study, we consider two OBJECTIVEs: (1) minimizing the costs of overtime staffing and ORs’ idle time, and (2) minimizing the number of waiting days for patients. The mathematical model of OR SCHEDULING problem is developed and solved by both exact method and simulation-based OPTIMIZATION approach. The comparison of results obtained from exact method and simulation-based OPTIMIZATION approach indicates that the exact method is only able to solve the small-size problems in reasonable time, while simulation-based OPTIMIZATION approach find competitive solutions for both small-size and large-size problems and solve large-size problems in an acceptable time.

In this paper hybrid FLOWSHOP SCHEDULING problem where some jobs, not all, have to follow no-wait restriction (that is, the operations of that job must be processed with no stop) is examined. In the LITERATURE, all papers assume that all jobs of the shops have to follow no-wait restrictions. First, this paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The models are evaluated using two performance measures of size complexity and computational complexity. The small instances of the problem are solved using commercial software of mathematical programming. To solve larger instances of problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment including small and large instances is conducted to evaluate the models and algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing.

Job shop SCHEDULING deals with distributions between jobs in order to find a schedule with minimum possible time. To formulate job shop SCHEDULING problems, various factors such as the activity processing time and due date for delivering jobs are often ambiguously known to the analyst. In these situations, the use of fuzzy parameters and multi-OBJECTIVE goals based on fuzzy knowledge seems necessary. Since this problem is non-polynomial, we propose a method based on extremal OPTIMIZATION in order to select and modify less valuable but more probable activities. This method leads to decrease the number of less valuable activities and to increase the number of activities with the same value. In such cases, any partial change in SCHEDULING may produce big changes. For instance, it leads to escape from local optima and move toward global one. Lots of simulations on and experimental data, demonstrate proper convergence rate and good results in comparison with other methods. The correctness of given results and proposed algorithm have been proven by means of axiom of convergence, statistical t-test t and possibility error (F).

SCHEDULING problems with batch processing machines (BPM) assume that machines are continuously available, and no time is needed for their preventive maintenance (PM). In this paper, we study a realistic variant of FLOWSHOP SCHEDULING which integrates flow shop batch processing machines (FBPM) and preventive maintenance for minimizing the makespan. In order to tackle the given problem, we employ reliability concept, and develop an electromagnetism-like (EM) algorithm, as an evolutionary technique, and propose an enhanced EM algorithm, in which the EM is hybridized with a diversification mechanism, and an effective local search to enhance the efficiency of the algorithm. The proposed algorithms are evaluated by comparison against two existing well-known EMs in the LITERATURE. For this purpose, we study the behavior and investigate the impacts of the rise in problem sizes on the performance of the developed algorithm. The superiority of our EM is inferred from computational results obtained in various circumstances.