Chair of Operations Management –
Challenging combinatorial problems in interdisciplinary research.
Our academic interests lie primarily in the fields of metaheuristic methodologies, combinatorial optimization problems, as well as the interface between operational research and artificial intelligence. In particular, we value mathematical foundations and emphasize practical applications. The main solution methods, hybrid search approaches, are based on exploiting adaptive memory structures to explore effective trajectories through complex solution spaces. These methods are now well recognized to produce best known results for solving many complex problems. In fact, their development has become the focus of leading societies. We are especially interested in designing, analyzing and implementing novel algorithms for challenging combinatorial problems in interdisciplinary research. Target applications are a wide range of problems in scheduling area, computational biology, supply chains, and human resource rostering.
Get in touch with us –
We look forward to hearing from you.
Recent activities –
Our current engagement in academic community.
Our paper entitled "A Hybrid Memetic Algorithm for the Parallel Machine Scheduling Problem with Job Deteriorating Effects" has just been accepted for publication in the prestigious IEEE Journal series: IEEE Transactions on Emerging Topics in Computational Intelligence.
From April 23-26, 2019 the 30th European Conference on Operational Research (EURO2019) took place in Dublin, where researchers and practitioners from around the globe gathered to engage in scientific exchange. We participated in this event and presented two of our current projects to the research community.
Within the scope of the conference stream “Production Planning and Control for Complex Manufacturing Systems”, a speech on “Exact and Heuristic Approaches for Parallel Machine Scheduling with Machine-dependent Delivery Times” by Prof. Liji Shen and Prof. Lars Mönch was given. Furthermore, our research assistant and doctoral student Söhnke Maecker gave a presentation on “Multi-Objective Unrelated Parallel Machine Scheduling to Minimize Total Tardiness and Energy Cost”.
As one of the most important events in the area of Operations Research, the conference was a great opportunity to learn about the most recent developments in this field, meet new or old colleagues and to discuss new ideas. This was facilitated through a well-organized, accompanying social program featuring the very best of Irish culture.
Our paper entitled “Solving Parallel Machine Problems with Delivery Times and Tardiness Objectives” is recently published in the renowned journal Annals of Operations Research. (DOI 10.1007/s10479-019-03267-2)
This paper studies a classical machine scheduling problem in the context of the new service-oriented manufacturing paradigm “Cloud Manufacturing”, where geographically distributed production resources are centrally managed and accessed by customers via cloud services. Specifically, delivery times depending on the assignment of jobs to resources are integrated into the classical model. We design and compare multiple solution approaches for this problem and develop techniques that exploit structural properties to significantly speed up local search procedures.
Our paper "A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem" has been accepted for presentation at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). This is one of the best ranked conferences in the area of computer science, especially for artificial intelligence. There was especially stiff competition this year because of the number of submissions which reaches a record number of over 7,700, and we are truly proud of our success.
Our paper entitled "Parallel machine scheduling with completion-time-based criteria and sequence-dependent deterioration" is recently published in the highly reputable journal Computers and Operations Research.
This paper presents an ejection chain algorithm (ECA) for solving specific scheduling problems. We first derive some important properties which are consistent when minimizing makespan and total weighted completion time, and help to guide algorithm design. Applied to benchmark problem instances, ECA obtains all optimal solutions with a hit ratio of 100% for small problem instances. Moreover, it improves the previous best known results for 388 large instances, and consumes less computational time.
16th International Conference on Project Management and Scheduling (PMS2018)
From April 17-20, 2018 the 16th International Conference on Project Management and Scheduling (PMS2018) took place in Rome, where researchers from around the globe gathered in the beautiful Residenza di Ripetta to present and discuss current research topics. Our research assistant and doctoral student Söhnke Maecker participated in this event and gave a presentation on “Scheduling Identical Parallel Machines with Delivery Times to Minimize Total Weighted Tardiness”.
The conference, which was hosted by Prof. Caramia of University of Rome Tor Vergata, gave an excellent opportunity to exchange and develop new ideas in a very interdisciplinary environment composed of mathematicians, engineers, computer scientists, and business scientists. With about 70 speeches on both theoretical and practical research projects, a very intimate atmosphere was created which encouraged to engage in discussion about the different topics.
The well organized scientific event was accompanied by a social program including a guided visit of the Colosseum followed by an Aperitif on one of Rome’s beautiful roof gardens with a stunning view over the historical city. Another highlight was a dinner in a historical villa located at a Via Appia Antica outside of Rome. These activities created a great atmosphere to make new contacts and further exchange in an astonishing setting.
The extended abstract submitted to the conference by Söhnke Maecker and Prof. Liji Shen will be published in the conference proceedings book.
In our recent study on the flexible job shop scheduling problem, we have improved current best results for some well-known benchmark problem instances. The FJSP has the highest complexity status among optimization problems, and is deemed one of the most difficult NP-hard problems. We are very proud of our research results.
High-quality and classic research –
Giving organizations a competitive edge.
Parallel machine scheduling with completion-time-based criteria and sequence-dependent deterioration
Solving the Flexible Job Shop Scheduling Problem with Sequence-Dependent Setup Times
Family Scheduling with Batch Availability in Flow Shops
A Simultaneous and Iterative Approach for Parallel Machine Scheduling with Sequence-Dependent Family Setups
Flow Shop Batching and Scheduling with Sequence Dependent Setup Times
A strong technical orientation –
Our teaching at WHU.
Professor Shen teaches in the following courses. Her teaching methods are highly appreciated by students:
- "Professor Shen gave insights into real world problems where the learned methods were applied. Very supportive atmosphere, high level of education."
"The simulation game is a great way of getting some variety into the course, rather than just having lectures. It is also useful to apply concepts learned in class..."
"Most of the concepts and tools learned in the course seem to be of high practical relevance..."
"Finally a class where students do not just have to work hard, but actually have to think, discuss, reflect and work together as a team to tackle the problems. Working with CPLEX was difficult but fun..."
"I enjoyed the course very much, since I like the mathematical focus. Prof. Shen was very knowledgeable in the field and well qualified to teach this course. The course was also well structured. I liked that the assignment exercises do not just make you apply a formula from the slides, but always contain an additional twist that forces you to actually think about the problem..."
"I very much appreciated the effort and the passion that Mrs. Shen brought to each session. Also the idea, that we work on problems on our own at home and then apply what you have learned in class worked very well and helped me to better understand several concepts..."
This course aims to gain a transdisciplinary and integrated view of supply chain-related decision problems. It focuses on the design and analysis of operations encountered in manufacturing and service industries. Students will explore key elements in Operations Management including plant location, facility layout, inventory, as well as production planning and control. The task is to study and critically assess the corresponding stream of scientific literature.
Operations Research was initiated in England during World War II to make scientifically based decisions regarding the utilization of war material. Afterwards, the ideas were adapted to improve efficiency in the civilian sector. This course will familiarize students with the basic methodologies in Operations Research including integer programming, optimization algorithms, and iterative computations. While mathematical modelling is the cornerstone of OR, this course also emphasizes defining and solving practical problems. Students will be presented with a variety of applications through solved examples and fully developed case analyses.
In an era of massive IT advances, computational intelligence is swiftly becoming an integral part of modern businesses. This course first builds a firm quantitative basis in business functional areas including procurement, production, and distribution. Students will master necessary technical skills to successfully apply advanced modelling methods. More importantly, this course will provide students with profound knowledge on core enabling technologies in the context of Internet of Things and cloud background. A significant portion of the course will be entirely dedicated to computer simulations, smart computing applications, as well as virtual enterprise experimentation.
Metaheuristics, in their original form, are guided local improvement procedures to perform a robust search of a solution space. New developments in metaheuristic methods are proved to be so remarkably effective, that they have moved into the spotlight in recent years for solving complex combinatorial problems, particularly those encountered in practice. This course is designed to provide doctoral students with a broad coverage of the concepts and instrumentalities of this important and evolving area of optimization. In doing so, we hope to encourage an even wider adoption of metaheuristics for assisting in various research areas.