Usha Mohan

In recent days, both collaboration and sustainability have become an integral part of many global supply chains to achieve business excellence. Although previous literature and actual practices confirmed the successful implementation of sustainability practices through supply chain collaborations, it is not clear how collaborating partners can support financial and environmental performance, and hence strengthen the partnership performance in the global supply chains. To address this practice-based research question, we test the theoretical underpinning of suppliers and logistics partners in relation to required skills selection. We capture the depth of interdependencies in collaborations for routine operations and sustainability, through empirical evidence. We used case study observations from three global companies to develop a conceptual model and also conducted a questionnaire survey to test the conceptual model. The results of case analysis confirmed two dimensions of collaborations that could strengthen relationship; namely, partners’ selection and sustainability team formation. Data analysis strongly support business collaborations having careful choice of supply chain partners and logistics operators who are ready to maintain green operations with transparent information sharing. Results of this study also inform managers about the importance of commitment from collaborating partners to achieve sustainability in their global supply chains. It is clear from the results that both the business and financial performances will be strengthened by environmental positioning (green objectives) of the companies.

A key operational decision faced by a multi-facility health care diagnostic centre serving different patient categories (for example: Health Check-up Patient (HCP), Out-Patient (OP), Emergency Patient (EP), or In-Patient) is whom to serve next at a particular facility. In this paper, we model random arrival of these patients belonging to different categories and priorities at multiple diagnostic facilities over a finite planning horizon. We formulate a mathematical model for sequential decision-making under uncertainty using Markov Decision Process (MDP) with the objective of maximising net revenue and use dynamic programming (DP) to solve it. To address dimensionality and scalability issue of MDP, we provide a decentralised MDP (D_MDP) formulation. We develop simulation-based neighbourhood search algorithm to improve DP solution for D_MDP. We compare these solutions with three other rule-based heuristics using simulation.

Coopetition is defined as the existence of simultaneous competition and cooperation between the same set of players, leading to entanglement of payoffs and actions of the players. This paper provides insights into the game theoretical application of quantum games to model simultaneity and entanglement that occur in coopetition. Modeling as a quantum game also allows for a larger action space and hence new equilibrium that may not have existed earlier. The impact of the level of entanglement on the equilibrium of the game can also be studied. We demonstrate the same through an example of two players who currently compete in the domestic market and are considering cooperating simultaneously in the international market. They need to determine the equilibrium strategy to adopt under coopetition that maximizes their payoffs. We also arrive at how to ensure that the quantum strategy is the equilibrium strategy for both players, namely, how to design the quantum strategy and how to define the unitary operator.

In this paper, we study the approximability of a variant of the Traveling Salesman Problem called the Biased-TSP. In the Biased-TSP, the edge cost function violates the triangle inequality in a “controlled” manner. We give a 7/2-factor approximation algorithm for this problem by a suitable modification of the double-tree heuristic.

In this paper, we study the path version of the Traveling Salesman Problem with the edge cost function satisfying a “relaxed” form of triangle inequality called the biased triangle inequality. We denote this problem as the Biased-TSP-Path. In this paper, we prove that the Biased-TSP-Path is approximable within a constant factor. Specifically, we design a 4-approximation algorithm by a suitable modification of the double-tree algorithm using effective shortcutting procedures.

We develop a novel online real-time scheduling algorithm with applications for healthcare diagnostic centers to deal with walk-in patients based on a set of constraints on the sequence of tests and resources. The problem is especially significant at healthcare centers in developing and emerging nations, such as India, where appointment schedules do not work. Within this realistic context, our objective is to improve patient satisfaction by reducing waiting time and improve diagnostic center performance through better utilization of the constrained resources. We propose a Mixed Integer Linear Programming (MILP) formulation to represent diagnostic centers as a Flow and Open Shop, to capture the system dynamics of the Flexible Hybrid Shop Scheduling Problem. We then develop a novel Online Genetic Algorithm (OGA) capable of solving real life large scale problems, as Open Shop scheduling problems are NP-hard. The developed OGA is first validated for small instances against a theoretical lower bound and the MILP model using CPLEX solver for flow time and makespan. The OGA is then empirically validated with data collected from two diagnostic centers of different sizes and configurations. For both centers, the developed OGA shows significant improvement compared to the simulation model. This research offers an important contribution to both literature and practice as it is one of the first to model the patient scheduling problem as an online real-time process. Implementing the developed OGA would help diagnostic centers significantly improve time estimates, thus reducing actual patient time and improving the efficiency of the system. Most importantly, the OGA is generalizable beyond healthcare to a broad range of environments that share Hybrid Shop characteristics.

Natural disasters and man-made calamities are a significant threat in many geographical locations worldwide. The administrative mechanism must plan and ensure the safety as well-being of the humans and other living creatures (or other essential resources) by being prepared during the disaster. This work presents a multi-period shelter location and allocation model that determines when and where to open shelters on the planning horizon. In our work, we define location and network vulnerabilities to measure the impact of a disaster in each geography. We use these vulnerabilities to formulate a Mixed Integer Linear Programming Model (MILP) for the shelter location and allocation problem. We present an extended linear relaxation heuristic and solve the problem using the real-life case data obtained from the major flooding event in and around the Chennai Metropolitan Development Area.

We formulate the shelter location-allocation problem considering the vulnerability of the demand locations and their network connectivities with the shelter locations for disaster management’s preparedness and response phase. We propose a scenario-based stochastic model that assigns the set of candidate locations evaluating operational, budgetary limitations, and service level expectations. The solution presents an evacuee-allocation plan considering the best collection of less vulnerable network connectivities between the demand areas and the shelter locations. We present a linear relaxation heuristic and compare the heuristic performance with the scenario-based formulation solved using CPLEX 12.8 optimisation solver for various problem sizes. We finally apply and solve the problem using real-life case data obtained during the major flooding event in and around the Chennai Metropolitan Development Area during 2015 to present our model’s applicability and emergency response requirements. [Submitted: 12 November 2020; Accepted: 19 April 2021]

Purpose: Increasing environmental uncertainty, more demanding customers, rapid technological growth and rising capital costs have all forced firms to evolve from collaborating with buyers and suppliers to collaborating with their competitors and that is called coopetition. The purpose of this paper is to better understand the antecedents and outcomes associated with coopetition. Design/methodology/approach: Building from the existing literature and three theoretical foundations, resource-based theory, resource dependence theory and game theory, the authors develop a model showing the antecedents and outcomes of coopetition and associated propositions of coopetition. Using a semi-structured interview process of 21 industry executives, the authors offer empirical support for the proposed coopetition model and propositions. Findings: Firms are increasingly dependent on the knowledge and expertise in external organizations to innovate, solve problems and improve supply chain performance. This research suggests that there is a value for firms to consider coopetition as a part of their inter-firm strategies. Research limitations/implications: The semi-structured interview process used in this research provided a wealth of information and executive experiences in coopetition. The interviews, however, only provide a single perspective of collaborative engagements with competitors. Multiple perspectives of each project would add value to this research. Originality/value: Collaboration among buyers and suppliers have been well researched; however, there has not been as much research on coopetition. This research provides a new area for future research for academics and offers suggestions for managers to improve the effectiveness and efficiency of their coopetition projects.

As most of the supply chains in practice are decentralized, a key issue in supply chain management is to study the coordination of supply chain. The interactions among players are typically of two types, viz. vertical competition and horizontal competition. We use a game theoretic framework to analyze a two-stage supply chain. The supply chain is modeled as a Stackelberg game with a manufacturer as leader and multiple retailers as followers. We consider multiple retailers competing on quantities (Cournot competition) and study its impact on manufacturer's equilibrium decisions and profits. We show that the wholesale price contract does not achieve Nash equilibrium when retailers offer uniform pricing. On the other hand, in case of perfect price discrimination we prove the existence of unique Nash equilibrium in both deterministic and stochastic demand set-up. Under stochastic demand set-up, we consider information asymmetry between the manufacturer and retailers with respect to demand signal. Furthermore, we show the retailers' selling effort boost up the sales. As variance of sales can hurt the manufacturer's production decisions badly, we conjecture that the manufacturer can offer a retailer incentive contract which can manipulate retailers' equilibrium decision. © 2013 World Scientific Publishing Company.