C Rajendran

The berth allocation problem (BAP) involves decisions on how to allocate the berth space and to sequence maritime vessels that are to be loaded and unloaded at a container terminal involved in the maritime logistics. As the berth is a critical resource in a container terminal, an effective use of it is highly essential to have efficient berthing and servicing of vessels, and to optimize the associated costs. This study focuses on the minimum cost berth allocation problem (MCBAP) at a container terminal where the maritime vessels arrive dynamically. The objective comprises the waiting time penalty, tardiness penalty, handling cost and benefit of early service completion of vessels. This paper proposes three computationally efficient mixed integer linear programming (MILP) models for the MCBAP. Through numerical experiments, the proposed MILP models are compared to an existing model in the literature to evaluate their computational performance. The computational study with problem instances of various problem characteristics demonstrates the computational efficiency of the proposed models.

This paper analyzes the value of information sharing, in terms of reduction in the demand variance and average (on-hand) inventory level, in a single product multi-stage (i.e., serial) supply chain with non-zero replenishment lead times. An order-one auto-regressive, AR(1), process characterizes the customer demand. We quantify the reduction in the demand variance and average (on-hand) inventory level in a multi-stage supply chain considering two information sharing scenarios: (1) supply-chain-wide information sharing; and (2) Vendor-Managed Inventory (VMI). In contrast to the related existing literature on a three-stage supply chain with non-zero replenishment lead times, we prove for a multi-stage supply chain that there exists no difference, in terms of the expectation and the variance of total demand over lead time, between supply-chain-wide information sharing and VMI. When there is an instantaneous replenishment across all firms, our analytical results are in agreement with the existing literature on a multi-stage supply chain. Further, we show that the value of information sharing is always greater than or equal to those obtained by an existing study. We also observe that the variance of total demand over lead time is reflected in the variance of the inventory level, derived using inventory balance equation. Furthermore, we carry out a comparative study with respect to the benefits of information sharing under three different supply chain settings, and find that the value of information sharing is more for upstream firms, when demand correlation over a period is high or when lead times are high or both.

This work proposes mathematical models (MMs) for the capacitated lot-sizing problem with production carry-over and set-up splitting, which can handle two scenarios, namely (1) situation/scenario where the set-up costs and holding costs are product dependent and time independent, and with no backorders or lost sales, and (2) situation where the set-up costs and holding costs are product dependent and time dependent, and with no backorders or lost sales. Previously, in an existing study the authors had developed a MM for the same problem and situation where the set-up costs and holding costs are product dependent and time independent, i.e. our Scenario 1. We compare our proposed models with the model in the existing study that appears to be incorrect.

This paper addresses the problem of scheduling n jobs on a single machine and on m identical parallel machines to minimize the completion time variance of jobs. This problem of scheduling jobs on parallel machines is motivated by a case study in an automobile ancillary unit. First, a heuristic to solve the single-machine scheduling problem is proposed. The parallel-machine scheduling problem is solved in two phases: job-allocation phase and job-sequencing phase. Two heuristics are proposed in the job-allocation phase, whereas in the job-scheduling phase, the single-machine scheduling approach is used. In this paper, both versions of parallel-machine scheduling problem (restricted and unrestricted) are considered. A good upper bound is obtained using a genetic algorithm, to evaluate the performance of the proposed heuristics for the parallel-machine scheduling problem. An extensive computation evaluation of the proposed heuristics is presented for both single-machine scheduling problem and the parallel-machine scheduling problem (especially considering the case study), along with the comparison of performances with the existing heuristics in the literature.

The management of inventory in a divergent supply chain involves inventory allocation/rationing in addition to the determination of order policy parameters. In the case of a stock point feeding product(s) to several downstream members, rationing mechanism can be viewed as a special case of the allocation mechanism. In a supply chain with multi-period ordering cycles, a rationing decision ensures that the entire inventory available with the feeder stock point is rationed to downstream members, whereas an allocation decision need not allocate the entire inventory available, and it is at the discretion of the decision maker at the feeder stock point to retain inventory for possible high priority demands in future periods. In any supply chain permitting backordering of demands from downstream members, the clearing of backorders is a matter of concern. This study addresses the said issue by ensuring that the feeder stock point considers the current period demand for fulfilment only after clearing the backorders with respect to the downstream members. Through this study, an attempt is made to develop mathematical models for supply chains operating with installation-specific costs (holding and shortage) and ordering policy (base stock) over a finite time horizon with and without clearing backorders in the case of rationing as well as allocating inventory to downstream members. Specifically, this work appears to be the first comparative study on allocation and rationing mechanisms in association with/without backorder clearing mechanisms in divergent supply chains, and their impact on the total supply chain cost.