C Rajendran

In this paper, we consider a serial supply chain (SC) operating with deterministic and known customer demands and costs of review or orders, holding, and backlog at every installation over a finite planning horizon. We present an evaluation of two order policies: Periodic-review order-up-to S policy (i.e., (T, S) policy), and (s, S) policy. We first present a mathematical programming model to determine optimal re-order point and base-stock for every member in the SC. By virtue of the computational complexity associated with the mathematical model, we present genetic algorithms (GAs) to determine the order policy parameters, s and S for every stage. We compare the performances of GAs (for obtaining installation s and S) with the mathematical model for the periodic-review order-up-to (T, S) policy that obtains in its class optimal review periods and order-up-to levels. It is observed that the (s, S) policy emerges to be mostly better than the (T, S) policy.

In this paper, we study the problem of transforming a job-shop layout into a flow-shop layout by introducing additional machines, so that all job-related operations can be processed in a flow-shop layout. The objective is to find the shortest sequence of machines, so that the overhead of introducing additional machines can be reduced. This transformation of job-shop layout into flow-shop layout has the advantage of automating the flow-line, which is an important step in digital manufacturing. The study first focuses on a special case (which is studied generally in the literature) where all the jobs would have the same and equal number of operations to be performed in a job-shop, but each job has a different machine routing when compared to other jobs. We propose a Mixed Integer Liner Programming (MILP) model for solving this special case. Further, in order to evaluate the performance of the proposed MILP model, we compare the same with an existing model in literature. From the results, we confirm that the proposed model is superior in terms of the CPU time, in solving the problem instances considered for the study. The study also extends this special case, and considers the generalized case where jobs could have different number of operations, and the study proposes a comprehensive MILP model for solving the generalized case.

This book examines the Capacitated Lot Sizing Problem (CLSP) in process industries. In almost all process industries, there are situations where products have short/long setup times, and the setup of the product and its subsequent production are carried over, across consecutive periods. The setup of a product is carried over across more than one successive period in the case of products having long setup times. A product having short setup has its setup time less than the capacity of the period in which it is setup. The setup is immediately followed by its production of the product and it may also be carried over, across successive time period(s). Many process industries require production of a product to occur immediately after its setup (without the presence of idle time between the setup and production of the product), and they also require the product to be continuously produced without any interruption. This book considers a single-machine, single-level and multiple-item CLSP problem. This book introduces the Capacitated Lot Sizing Problem with Production Carryover and Setup Crossover across periods (CLSP-PCSC). Mathematical models are proposed which are all encompassing that they can handle continuous manufacturing (as in process industries), and also situations where the setup costs and holding costs are product dependent and time independent/time dependent, with possible backorders, and with other appropriate adaptations. Comprehensive heuristics are proposed based on these mathematical models to solve the CLSP-PCSC. The performance of the proposed models and heuristics are evaluated using problem instances of various sizes. This book also covers mathematical models developed for the Capacitated Lot Sizing Problem with Production Carryover and Setup Crossover across periods, and with Sequence-Dependent Setup Times and Setup Costs (CLSP-SD-PCSC). These models allow the presence of backorders and also address real-life situations present in process industries such as production of a product starting immediately after its setup and its uninterrupted production carryover across periods, along with the presence of short/long setup times. Heuristics proposed for the CLSP-PCSC can be extended to address the CLSP problem with sequence dependent setup costs and setup times. All the models and heuristics proposed in this book address some real-life considerations present in process industries.