A threestep scheduling based on geographic information system for workforceplanning problem Routing workforce planning problem is a difficult problem, requiring a combination of the vehicle routing problem and the staff scheduling problem. This problem associates with a lot of constraints, even when having only a single criterion and homogeneous staff skill. This paper introduces threestep schedulinga generic scheduling procedure for solving the workforce planning problems which incorporates between an optimisation tool, fuzzy inference system and geographic information system technology. The scheduling problem is hierarchically divided into subproblems and then solved with the proposed scheduling framework. A simulated case study of the electricity meter installation service problems is employed to verify the proposed framework. In this study, we are focusing on the route scheduling which is the first step of the threestep scheduling. The total of travel distance is defined as an objective function. Particular case requirements such as lunch and parking time are also included in the model for optimisation. Our empirical study reveals that the proposed route scheduling is capable of exploring the optimal solutions for the case study with the shortest service path, compared to the original procedure.

Job Shop Scheduling and Its Fuzzification Based on Operations and Disjunctive Graph Representations In this paper, several approaches to job shop scheduling problem are presented. A mixed integer programming model is derived and, based on this result, it is implemented in the GAMS optimisation software tool. Since the problem is NPhard, this approach may not be used for large instances, its reasonable bounds are indicated using benchmarks from ORLibrary. Here, strategies based on operations and disjunctive graph representations are applied and their time complexities are derived. These representations are also used to heuristically generate feasible solutions from their searching space. Finally, a modification is described of the problem using uncertain processing times of jobs based on fuzzy numbers. Methods for comparing fuzzy numbers are then discussed.