Assembly line balancing problem is an approach of assigning a set of tasks to an ordered
sequence of workstations. This assignment needs to be made in such a way that the
underlying precedence constraints are not violated and efficiency measures are optimized
subject to the restriction of the cycle time constraint. Research works, reported so far, mainly
deal with the minimization of balancing loss, subject to precedence constraints. Lack of
uniqueness in those optimum solutions and pressing demand to include system loss in the
objective function have led to the present work of minimization of both balancing and system
loss. As there is no standard measure for system loss, the current work proposes a measure
for system loss and offers solution to this bi-objective problem.

Traditional assembly lines are still attractive means of mass and large-scale series production. Previous research works mainly dealt with the minimization of balancing loss, subject to precedence constraints. Very recently the focus of attention shifted towards system loss. However, these losses are not the proper indicators of the stability of the system since the efficiency of the assembly line increases normally when the stability of the system increases. In this paper, we introduce a new type of assembly line, which is totally different form the existing types of assembly line studied in the literature. In this work, we define the stability of the system in terms of reliability of the assembly line. The objective is to design an assembly line with a desired level of working stability measured through this reliability measure. This special type of assembly line, where production rate can be increased by minimizing the cycle time subject to a given level of reliability and precedence constraints, extends the decision-making framework available in the literature on assembly line balancing problem and indirectly takes care of both balancing loss and system loss.

In this paper, we address the optimization of an integrated line balancing process with workstation inventory management. While doing so, we have studied the interconnection between line balancing and its conversion process. Almost each and every moderate to large manufacturing industry depends on a long and integrated supply chain, consisting of inbound logistic, conversion process and outbound logistic. In this sense an approach addresses a very general problem of integrated line balancing. Research works reported in the literature so far mainly deals with minimization of cost for inbound and outbound logistic subsystems. In most of the cases conversion process has been ignored. We suggest a generic approach for linking the balancing of the line of production in the conversion area with the customers’ rate of demand in the market and for configuring the related stock chambers. Thus, the main aim of this paper is to translate the underlying problem in the form of mixed nonlinear programming problem and design the optimum supply chain so that the total inventory cost and the cost of balancing loss of the conversion process is jointly minimized and ideal cycle time of the production process is determined along with ideal sizes of the stock chambers. A numerical example has been added to demonstrate the suitability of our approach.

An assembly line is a sequence of workstations, connected together by a material handling system, which is used to assemble components into a final product. The economic importance of assembly as a manufacturing process has led to extensive efforts for designing an assembly line to ensure efficiency and cost effectiveness of assembly operations. These efficiency and effectiveness can be maximized by minimizing balancing loss and system loss. The current work offers a range based measure for System loss and describes a technique for jointly minimizing balancing loss and system loss. To arrive at the optimum solution of the joint minimization problem, the same has been reduced to sequential marginal minimization problems and simulation technique has been installed for obtaining the optimal solution. For demonstration purpose, one example has been worked out to indicate the strength of the suggested method.