Economic lot size scheduling problem (ELSP) for a multi-product single machine system is a classical problem. This paper considers ELSP with budgetary constraint as an important aspect of such systems. In the real world situations the available funds for investment in inventory is limited. By adopting the common cycle time approach to ELSP, we obtain the optimal common cycle which minimizes the total inventory ordering and holding costs for the case of nonzero setup times. One aspect of the scheduling is to decide what should be the sequence of production runs and how the Idle times shall be distributed in the common cycle time. For such a sequencing problem, we consider two cases: a) the common cycle time is given, and b) the common cycle time is a decision variable. In the literature, scheduling rules are introduced for both cases, which assume that the total Idle time is located at the end of each cycle. This paper relaxes this assumption and provides: i) a rule to optimize the production sequence and the length of Idle times before (or after) producing each item, for both cases (a) and (b), and ii) the optimal common cycle for case (b). The presented rule is interestingly general, simple and easy-to-apply.

Assembly Line production is one of the widely used basic principles in production system. The problem of Assembly Line Balancing deals with the distribution of activities among the workstations so that there will be maximum utilization of human resources and facilities without disturbing the work sequence. Research works reported in the literature mainly deals with minimization of Idle time i.e. balancing, subject to precedence constraints. Lack of uniqueness in their optimum solutions has led to the present work where minimization of both balancing loss and system loss has been envisaged under the usual precedence constraints. The researchers suggest a generic approach for designing of an assembly line where, with a given number of workstations, one can efficiently arrive at the desired solution under different methods of search like simulation, heuristic etc. Thus, the main aim of this paper is to redefine the objective of the Assembly Line Balancing Problem and sequentially handle Balancing Loss and System Loss. A numerical example has been added to demonstrate the generic nature of the researcher's approach and improvement in the solution set when compared with different standard line balancing methods available so far in the literature.

Although several papers have studied no-Idle scheduling problems, they all focused on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-Idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics, based on variable neighborhood search and genetic algorithms, are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.

This paper represents linkage network of queues consisting of biserial and parallel servers linked to a common server in series with a flow shop scheduling system consisting of two machines. The significant transportation time of the jobs from one machine to another is also considered. Further, the completion time of jobs/customers (waiting time + service time) in the queue network is the setup time for the first machine. The objective of the paper is of two folds, on one hand it minimizes the total waiting time and service time of jobs/customers in the queue network, and on the other hand it minimizes the Idle time of the machines for the optimal sequence of jobs/customers in a given queue flowshop linkage model. A computer programme followed by a numerical illustration is given to justify the proposed algorithm.

Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs. In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine Idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined. Genetic algorithm, imperialist competitive algorithm and hybrid of these algorithms are applied to solve the problem and the performance of the proposed algorithms are evaluated by many test problems. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding the best solution in reasonable computational time.

In the design of a cellular manufacturing system (CMS), one of the important problems is the cell formation in the form of machine grouping and parts family. This paper investigates an allocation of parts to common and specific cells; in such a way that each common cell is able to process all required parts. Further, this paper presents a mathematical programming model comprising such constraints as available time for common and special cells in each time horizon, and such variables as excess time required by each cell to process parts in each period. The objective of the model is to minimize the total tardiness in production of goods and sum of Idle times of machines in each cell. To obtain good solutions, a simulated annealing (SA) method has been used. To verify the quality and efficiency of the SA algorithm, a number of test problems with different sizes are solved to show the efficiency of the proposed algorithm. Finally, results are compared with solutions obtained by Lingo 6 in terms of objective function values and computational time.