Workload control theory seeks to align capacity and demand to improve delivery performance. However, workload control researchers mainly focused on input control, which regulates the input of work to the production system, thereby neglecting output control, which uses capacity adjustments to regulate the outflow of the work. Moreover, few existing studies on output control investigate a temporarily increase in capacity. This paper introduces a new search direction for output control which does not require an increase in capacity – labour flexibility. Idle operators can move from their workstation to another, thus temporarily increasing the output of that workstation without extra capacity. Using simulation of a five workstations flow shop line, we highlight the positive performance effect of labour flexibility. However, this comes at the cost of high labour movement. Introducing a load-based constraint on when workers are allowed to significantly reduces labour movement, while realizing most of the performance improvement observed for unconstrained labour movement. This has important implications for future research and practice.

In this paper, the flow shop with blocking and sequence and machine dependent setup time problem aiming to minimize the makespan is studied. Two mixed-integer programming models are proposed (TNZBS1 and TNZBS2) and two other mixed-integer programming models, originally proposed for the no setup problem, are adapted to the problem. Furthermore, an Iterated Greedy algorithm is proposed for the problem. The permutation flow shop with blocking and sequence and machine dependent setup time is an underexplored problem and the authors did not find the use of mixed-integer programming models for the problem in any other work. To compare the models, a database of 80 problems was generated, which vary in number of machines and jobs. For the small sized problems, the adapted MILP model obtained the best results. However, for bigger problems, both proposed MILP models obtained significantly better results compared to the adapted models, proving the efficiency of the new models. When comparing the Iterated Greedy algorithm with the MILP models, the former outperformed the latter.

Flow shop scheduling problems with rudimentary criteria of minimum makespan are the most important investigated problems in the field of scheduling. Generally during the process of generating an optimal sequence, multiple partial sequences claiming the optimal value of makespan are observed. In this paper a novel tie-breaking rule to select one of the best optimal sequences out of all possible partial sequences is developed which then applied to Nawaz-Enscore-Ham (NEH) heuristic to solve the scheduling problems in permutation flowshop without increasing the computational complexity. The performance of proposed heuristic is tested with other existing tie-breaking heuristics of similar complexity over Taillard and VRF's instances. Computational results reveal that in terms of solution quality, the proposed heuristic outperforms over the other NEH based heuristics of the same complexity reported in literature.

In recent years, many studies on scheduling problems with dedicated machines have been carried out. But, few of them have considered the case of more than two stages. This paper aims at filling this gap by addressing the three-stage hybrid flow shop scheduling problem with two dedicated machines in stage 3. Each job must be processed, consecutively, on the single machines of stages 1 and 2, and depending on its type, it will be further processed on one of the two dedicated machines of stage 3. The objective is to find an optimal schedule that minimizes the maximum completion time (makespan). Since this problem is strongly NP-hard, we first provide some basic results including solutions for several variations of the problem. Then, for the general case we adapt a set of lower bounds from the literature and propose a heuristic approach that is based on the dynamic programming technique, which uses a local search procedure. Finally, various experimentations on several problems with different sizes are conducted and the computational results of the heuristic show that the mean percentage deviation value from the lower bound was lower than 0.8 percent for some instances with 40 to 200 jobs in size.

This paper discusses an integrated model of batch production and maintenance scheduling on flow shop with two deteriorating machines producing single item to be delivered at a due date. The model describes the trade-off between production and maintenance costs as the production run length increases on two machines. The objective function of the model is to minimize the total cost consisting of in process and completed part inventory costs, setup costs, preventive & corrective maintenance costs and rework costs on two machines. The step-wise optimization algorithm is developed to solve a mixed integer quadratic programming. Comparison with the practice and the model sensitivity analysis are demonstrated to clarify how the algorithm works.

This paper addresses the minimization of makespan for the permutation flow shop scheduling problem with blocking and sequence and machine dependent setup times, a problem not yet studied in previous studies. The 14 best known heuristics for the permutation flow shop problem with blocking and no setup times are pre-sented and then adapted to the problem in two different ways; resulting in 28 different heuristics. The heuristics are then compared using the Taillard database. As there is no other work that addresses the problem with blocking and sequence and ma-chine dependent setup times, a database for the setup times was created. The setup time value was uniformly distributed between 1% and 10%, 50%, 100% and 125% of the processing time value. Computational tests are then presented for each of the 28 heuristics, comparing the mean relative deviation of the makespan, the computational time and the percentage of successes of each method. Results show that the heuristics were capable of providing interesting results.

Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed.

Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a bi-objective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality.