Extant literature has increased our understanding of specific aspects of digital transformation, however we lack a comprehensive portrait of its nature and implications. Through a review of 282 works, we inductively build a framework of digital transformation articulated across eight building blocks. Our framework foregrounds digital transformation as a process where digital technologies create disruptions triggering strategic responses from organizations that seek to alter their value creation paths while managing the structural changes and organizational barriers that affect the positive and negative outcomes of this process. Building on this framework, we elaborate a research agenda that proposes [1] examining the role of dynamic capabilities, and [2] accounting for ethical issues as important avenues for future strategic IS research on digital transformation.

Understanding digital transformation: A review and a research agenda

Facility location and supply chain management - A review

Introduction to stochastic programming

The focus of this book is on bilevel programming which combines elements of hierarchical optimization and game theory. The basic model addresses the problem where two decision-makers, each with their individual objectives, act and react in a noncooperative manner. The actions of one affect the choices and payoffs available to the other but neither player can completely dominate the other in the traditional sense. Over the last 20 years there has been a steady growth in research related to theory and solution methodologies for bilevel programming. This interest stems from the inherent complexity and consequent challenge of the underlying mathematics, as well as the applicability of the bilevel model to many real-world situations. The primary aim of this book is to provide a historical perspective on algorithmic development and to highlight those implementations that have proved to be the most efficient in their class. A corollary aim is to provide a sampling of applications in order to demonstrate the versatility of the basic model and the limitations of current technology. What is unique about this book is its comprehensive and integrated treatment of theory, algorithms and implementation issues. It is the first text that offers researchers and practitioners an elementary understanding of how to solve bilevel programs and a perspective on what success has been achieved in the field. Audience: Includes management scientists, operations researchers, industrial engineers, mathematicians and economists.

Practical Bilevel Optimization: Algorithms and Applications

Personnel scheduling: A literature review

A time–space network based exact optimization model for multi-depot bus scheduling

This paper discusses the integrated vehicle-and crew-scheduling problem in public transit with multiple depots. It is well known that the integration of both planning steps discloses additional flexibility that can lead to gains in efficiency, compared to sequential planning. We present a new modeling approach that is based on a time-space network representation of the underlying vehicle-scheduling problem. The integrated problem is solved with column generation in combination with Lagrangian relaxation. The column generation subproblem is modeled as a resource-constrained shortest-path problem based on a novel time-space network formulation. Feasible solutions are generated by a heuristic branch-and-price method that involves fixing service trips to depots. Numerical results show that our approach outperforms other methods from the literature for well-known test problems.

A Time-Space Network Approach for the Integrated Vehicle-and Crew-Scheduling Problem with Multiple Depots

This paper discusses the computation of LU factorizations for large sparse matrices with emphasis on large-scale linear programming bases. We present new implementation techniques which reduce the computation times significantly. Numerical experiments with large-scale real life test problems were conducted. The software is compared with the basis factorization of MPSX/370, IBM's commercial LP system. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Computing Sparse LU Factorizations for Large-Scale Linear Programming Bases

We consider the multiple-depot multiple-vehicle-type scheduling problem (MDVSP) which arises in public transport bus companies and aims to assign buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities. An optimal schedule is characterized by minimal fleet size and minimal operational costs including costs for empty movements and waiting time. It is well-known that the MDVSP is NP-hard. Although progress has recently been made in solving large practical MDVSP to optimality with time-space network models, current optimization technology sets limits to the model size that can be solved. In order to approach very large practical instances we propose a two-phase method which produces close to optimal solutions. This modeling approach enables us to solve real-world problem instances with thousands of scheduled trips by direct application of standard optimization software. Furthermore, we introduce the concept of depot groups for the case that a bus may return in the evening into another depot than where it started in the morning.

Solving large multiple-depot multiple-vehicle-type bus scheduling problems in practice

Stability describes the grade of the ability of a plan to remain feasible and cost efficient under variations of the operating environment without major modifications to the plan. In airline traffic delays often lead to additional delays, because of interdependencies between different resources such as ai rcraft, crews and airport facilities. In this paper we focus on reactionary delays that result from crews changing aircraft. Based on a stochastic model for delay propagation we propose an indicator for stability of airline crew and aircraft schedules. This indicator is used to generate more robust aircraft and crew schedules. An integrated formulation for the aircraft and crew scheduling problem results in a non-linear stochastic recourse function. We decompose this formulation into separate linear problems connected by the objective function. The decomposed stochastic problem may be solved using a heuristic iterative approach based on column generation and dynamic programming for the recourse functions. In order to evaluate the robustness of the generated schedules and to compare with a deterministic approach for robust scheduling we use an also presented simulation model.

Leena Suhl

Papers

A time–space network based exact optimization model for multi-depot bus scheduling

A Time-Space Network Approach for the Integrated Vehicle-and Crew-Scheduling Problem with Multiple Depots

Computing Sparse LU Factorizations for Large-Scale Linear Programming Bases

Solving large multiple-depot multiple-vehicle-type bus scheduling problems in practice

Increasing stability of crew and aircraft schedules