The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

The article describes our solution approach for the coach trip with shuttle service problem, a passenger transportation problem introduced in the context of the VeRoLog Solver Challenge 2015, an implementation competition of the EURO Working Group on Vehicle Routing and Logistics Optimization. Our algorithm applies concepts known from Variable Neighborhood Search and Iterated Local Search. On a lower level, we consider the fast construction and modification of tree data structures, as those structures may serve as appropriate representations of (feasible) alternatives. Experiments are carried out, and a comparison to other approaches is given. We also make the source code of our approach (i.e., its computer implementation) available with this article: https://doi.org/10.17632/662mtv6sd8.1 . © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 69(3), 329–345 2017

The approach introduced in this paper depicts the topic of identification and evaluation of stockout consequences, commonly denoted as stockout cost quantification. Our work is motivated by the limited number of approaches dealing with this problem and, primarily in the field of inventory management, a subsequent need for applicable methods providing reliable stockout cost parameters. We focus on the problem of estimating opportunity costs of stockouts as the most difficult cost component to be determined. Therefore, a method to elicit information by confronting relevant decision makers with representative stockout cases (a priori) is presented. Subsequently, a Genetic Programming (GP) approach for learning opportunity cost functions from these case-based decisions is introduced. It is shown on exemplary tests instances that solutions can be generated which converge to structurally similar opportunity cost functions for representative stockout items. Based on a comparison to benchmarks generated by Neural Networks, it can be concluded that the quality of solutions from the GP algorithm is satisfying.

The article presents an adaptive hybrid planning system or the interactive solution of multi-objective vehicle routing problems. A general framework was built, being able to handle various components of general vehicle routing problems, e.g. the simultaneous consideration of six optimization criteria. Solutions are constructed and improved in real time allowing the user to adapt his articulated preference information interactively. Results simulating different types of decision makers are reported, focusing on the adaptability of the system and the quality of the obtained solutions. In brief, we are able to observe and demonstrate the suitability of the system to different types of decision makers, focussing on cost- or service-oriented criteria, or maintaining a balanced view on the two areas.

The evaluation of alternatives plays a central role in optimization problems as the quality of solutions is judged depending on the outcomes of the chosen optimality criteria. Based on the relevant evaluation functions, optimization methods aim to identify solutions that are optimal, implying that these are considered to be favorable to the decision maker in a particular situation. In the current paper, existing approaches of evaluating alternatives are reviewed in the context of scheduling in manufacturing environments. Mono-criterion, multi-objective, as well as fuzzy approaches are reviewed. It is possible to observe limitations of traditional techniques that complicate the interpretation of the quality of solutions. To overcome the identified problems, a new evaluation concept is presented and discussed. It uses fuzzy set theory and fuzzy logic to derive a linguistic description of the qualitative characteristics of the alternatives, allowing the decision maker a close investigation of the solution properties in comparison to existing techniques. Although a specific problem domain is being targeted here, the implications are of general use for a broad range of optimization problems like course/ examination timetabling, vehicle routing, etc.