C Rajendran

In this paper, we develop branch-and-bound algorithms for objectives such as sum of weighted flowtime, weighted tardiness and weighted earliness of jobs, for an m- machine no-wait (continuous) flowshop. We believe that there has been no prior work on exact algorithms for this problem setup with a variety of objective functions. For the interest of space, we confine our discussion to a subset of certain combination of these objectives and the extension to other objective combinations is quite straight-forward. We explore the active nodes of a branch-and-bound tree by deriving an assignment-matrix based lower bound, that ensures one-to-one correspondence of a job with its due date and weight. This idea is based on our earlier paper on general m- machine permutation flowshop (Madhushini et al. in J Oper Res Soc 60(7):991–1004, 2009) and here we exploit the intricate features of a no-wait flowshop to develop efficient lower bounds. Finally, we conclude our paper with the numerical evaluation of our branch-and-bound algorithms.

Corporate Social Performances (CSP) has a determining role in the Environmental, Social, and Governance (ESG) scores of firms. Corporate Social Responsibility (CSR) strategy scores in the ESG ratings can provide a measure of the CSP concerts of firms. We observe in this research, the mean differences in the CSR strategy scores and the ESG scores of firms in select developed economies such as; US, UK, Japan, and Australia, representing different geographical regions globally. Thomson Reuters ESG scores based on ten major parameters and over 400 company level indicators are used to empirically evidence the study. The initial data of average performances on ESG indicators of 939 firms considered for a period of five years from 2014 to 2018 is analyzed. The results imply that the mean differences in the CSR strategy scores are not significant, considering the developed economies, deliberated in the study. Along with that, we observe a significant mean difference in the ESG scores of Australian firms considered for the study, in comparison with the firms from other developed economies. And the study confirm that the CSR strategy scores are significant predictors of ESG performance scores of firms in the developed economies considered for study.

Convoy movement problem (CMP) is an important optimisation problem, especially occurring in military logistics, which focuses on routing convoys concurrently in a network. The CMP is subject to constraints for preventing certain interactions between convoys such that the objectives related to travel times of them are minimised. We propose a Lagrangian relaxation (LR) approach to efficiently obtain a lower bound on the optimal solution. Our LR approach does not have integrality property, and hence the lower bound obtained is always tigher than the lower bound obtained using the LP relaxation. As CMP is an NP-complete problem, lower bounds are useful to evaluate the solutions obtained by a heuristic. From our computational experimentation, it is found that the proposed LR approach is superior to the existing LR approach.

This paper focuses on developing the optimal solution or a lower bound for N-job, M-machinePermutation Flowshop Scheduling (PFS) problem in a manufacturing system with the objective of minimizing the makespan using Lagrangian Relaxation (LR) technique. Even though LR technique is considered, in general, as a good method to obtain a lower bound, research in this direction with respect to our problem under study appears scarce. We address this gap by developing two MILP based Lagrangian Relaxation models, namely, Lagrangian Relaxation Method 1 (called Proposed Lagrangian Lower Bound Program (PLLBP)) and Alternate Lagrangian Relaxation Method 1 (called ALR) to find the optimal solution or a lower bound on the makespan. Basically, we develop these LR methods to overcome the possible limitation of the general LR procedure involving the sub-gradient approach. Benchmark PFS problem instances are used to evaluate the performance of these methods. It is observed that the PLLBP outperforms the ALR, and it provides better lower bounds than the lower bounds (in most instances) reported in the literature. Even though the PLLBP is superior in terms of solution quality, it has a limitation in that it cannot execute problem instances beyond 500 jobs due to the associated computational effort.

In this paper, bounding strategies for determining a lower bound on the completion time of a job sequenced in each position in the permutation sequence on each machine in permutation flowshop scheduling problem with minimisation of total flowtime of jobs as objective are discussed. Basically, the bounding strategies are machine-based bounding strategies used for determining the lower bound on total flowtime of jobs for all the small-sized and large-sized benchmark flowshop scheduling problem instances proposed by Vallada et al. (2015). The lower bound matrix can be pruned as tightening constraints into the mixed integer linear programming (MILP) model with objective of minimisation of total flowtime of jobs. Since the flowshop scheduling problem with total flowtime objective is difficult, two kinds of linear programming (LP) relaxation methods are used for determining an LP-based lower bound on total flowtime of jobs for some benchmark problem instances proposed by Vallada et al. (2015).

The integration of drones into the conventional truck delivery system has gained substantial attention in the business and academic communities. Most existing works restrict the launch and recovery of unmanned aerial vehicles (UAVs) to customer locations (or nodes) in the delivery network. Nevertheless, emerging technological advances can allow drones to autonomously launch/land from a moving vehicle. In addition, majority of the current literature assumes multiple UAVs to be deployed and/or recovered simultaneously, thereby ignoring the associated scheduling decisions, which are essential to ensure safe, collision-free operations. This research introduces the single truck multi-drone routing and scheduling problem with en route operations for last-mile parcel delivery. A mixed integer linear programming (MILP) model is developed to minimize the delivery completion time. In addition, a variant is introduced to minimize the total delivery cost. Since the problem under consideration is NP-hard, a relax-and-fix with re-couple-refine-and-optimize (RF-RRO) heuristic approach is proposed, where the associated decisions (truck routing and drone scheduling) are decomposed into two stages and solved sequentially. Besides, a deep learning-based clustering procedure is developed to establish an initial solution and accelerate the convergence speed of the RF-RRO heuristic. Notably, the proposed approach is extended to solve a multi-truck multi-drone variant using a deep learning-based cluster-first route-second heuristic. Our numerical results show that the proposed MILP model is able to solve problem instances with up to 20 customers optimally in a reasonable time. The proposed RF-RRO heuristic can achieve optimal (or near-optimal) solutions for small instances and is computationally efficient for large cases. Extensive experimental analysis shows 30% average savings in delivery completion time, and an average drone utilization of 62% if en route drone operations are considered. In addition, numerical results provide insights on the impact of heterogeneous drone fleet and customer density.

This case focuses on how a lifestyle enterprise has used strategies to survive amidst the COVID-19 crisis. It explores how a lifestyle enterprise, ‘The Square Inch’, a quilting studio, (a) conducts teaching workshops, (b) stitches and sells quilts and (c) sells raw materials and accessories, including sewing machines required for quilting. The case provides a detailed account of the risks, uncertainties and crises faced by a lifestyle enterprise and its entrepreneur during its founding and growth. This case also highlights how resilience, entrepreneurial orientation and innovation can be used as survival strategies to overcome challenges. The case engages students to understand the survival strategies employed by a lifestyle entrepreneur in times of crisis.

Most time series forecasting tasks using Artificial Neural Networks (ANNs) relegate trend-cycle modeling to a simple preprocessing step. In this work, we propose an automatic feature engineering framework for modeling the trend-cycle (tofee-tree) in time series forecasting. The first stage of the framework automatically creates over 286 deterministic linear and nonlinear engineered features to model the trend-cycle. These features are based only on the time of observation and length of the time series, making them domain-agnostic. In the second stage of the framework, a SHapley Additive exPlanations (SHAP)—based feature selection procedure using Light Gradient Boosted Machine (LightGBM) selects the most relevant features. These relevant features can be used for forecasting with ANNs in addition to the auto-regressive lags. Two popular ANNs—Multi-Layer Perceptron (MLP) and Long Short Term Memory network (LSTM) are used to evaluate our proposed tofee-tree framework. Comparisons against two empirical studies using the M3 competition dataset show that the proposed framework improved the overall Symmetric Mean Absolute Percentage Error (SMAPE) in the one-step, medium- and long-term. The relative improvement in one-step SMAPE is 3% for MLP and 23% for LSTM. We also show that the residual seasonality left after deseasonalization can be modeled using the tofee-tree framework.

Purpose: This paper aims to analyse various failures that Indian innovative start-ups (ISs) are exposed to and proposes interventions from management accounting tools (MATs) that can tackle their failure-causing risks. This paper justifies the applicability of contingency theory (CT) for applying MATs for failure prevention and risk management. Design/methodology/approach: This paper uses multimethod research while undertaking two sequential studies. The methods include a Survey via semi-structured interviews of 51 specialists and media reports and the Delphi method. Findings: Reasons for the failures of Indian ISs have been identified and grouped based on eight broad underlying risk factors. Appropriate MATs relevant to ISs have been identified and examined by relating them with the risk factors underlying failure. Applicability of CT is shown while using the MATs for failure prevention and risk management of ISs. Research limitations/implications: This study is limited to the Indian context. Empirical validation of the applicability of MATs for each type of failure along the lifecycle stages of ISs needs to be undertaken. Practical implications: Founders/owners of ISs can use this conceptual framework to tackle the risks underlying the failure of their firms. Policymakers can introduce appropriate policies to enhance the survival of ISs. Researchers can further explore the application of CT for failure prevention and risk management of ISs. Originality/value: A conceptual framework has been developed relating failure-causing risk factors relevant to ISs and appropriate MATs, which justifies the applicability of CT.