Discrete lot sizing and scheduling on parallel machines: description of a column generation approach

In this work, we study the discrete lot sizing and scheduling problem (DSLP) in identical parallel resources with (sequence-independent) setup costs and inventory holding costs. We propose a Dantzig-Wolfe decomposition of a known formulation and describe a branch-and-price and column generation procedure to solve the problem to optimality. Preliminary results show that the lower bounds provided by the reformulated model are stronger than the lower bounds provided by the linear programming relaxation of the original model

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing. Aiming at the job shop scheduling problem, a hybrid algorithm based on comprehensive search mechanisms (HACSM) is proposed to optimize the maximum completion time. HACSM combines three search methods with different optimization scales, including fireworks algorithm (FW), extended Akers graphical method (LS1+_AKERS_EXT), and tabu search algorithm (TS). FW realizes global search through information interaction and resource allocation, ensuring the diversity of the population. LS1+_AKERS_EXT realizes compound movement with Akers graphical method, so it has advanced global and local search capabilities. In LS1+_AKERS_EXT, the shortest path is the core of the algorithm, which directly affects the encoding and decoding of scheduling. In order to find the shortest path, an effective node expansion method is designed to improve the node expansion efficiency. In the part of centralized search, TS based on the neighborhood structure is used. Finally, the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature

Optimisasi Penjadwalan Mata Kuliah di Teknik Industri UMS dengan Goal Programming dan BRKGA

The course timetabling problem is one of the most notorious scheduling problems, both theoretically and practically. The objective of this research is how to make an optimal course schedule to schedule courses with limits on the number of lecturers, room capacity and time slots so as to minimize the penalties that are charged if the limits are not met. Because course scheduling is a Non Polynomial Hard (NP-Hard) problem, designing an optimal solution search algorithm that is effective and efficient is one of the biggest challenges that must be resolved. In this study, the Biased Random Key Genetic Algorithm (BRKGA) was designed and coded with Matlab to solve course scheduling problems in UMS Industrial Engineering. The results obtained indicate that the BRKGA algorithm can produce an optimal course schedule solution by fulfilling all the hard constraints with the required computation time is relatively fast

Forward VNS, Reverse VNS, and Multi-VNS Algorithms for Job-Shop Scheduling Problem

This paper proposes a number of forward VNS and reverse VNS algorithms for job-shop scheduling problem. The forward VNS algorithms are the variable neighborhood search algorithms applied to the original problem (i.e., the problem instance with the original precedence constraints). The reverse VNS algorithms are the variable neighborhood search algorithms applied to the reversed problem (i.e., the problem instance with the reversed precedence constraints). This paper also proposes a multi-VNS algorithm which assigns an identical initial solution-representing permutation to the selected VNS algorithms, runs these VNS algorithms, and then uses the best solution among the final solutions of all selected VNS algorithms as its final result. The aim of the multi-VNS algorithm is to utilize each single initial solution-representing permutation most efficiently and thus receive its best result in return

Optimization of Production Planning

The challenge proposed by PRIMAVERA BSS software company was to find an effective scheduling algorithm that can add new features to their production planning software, with a good performance (being able to run in less than 10 minutes), and that can be su�ciently generic and adaptable to be used by different industries (metal, furniture, wood, textile, and food industry). The requirements configured a NP-hard problem known in the literature as the Flexible Job Shop Scheduling Problem (FJSSP), for which sophisticated mathematical models and heuristic methods are widely available. The wherein proposed approaches consider sequence dependent setup times and different priorities for the operations. Two approaches are considered in the present report. First, two mathematical models were created to address this problem and give insight to the structure of the problem and its constraints. A second approach proposed the use of heuristics. A constructive heuristic used to find initial solutions is followed by the use of an improvement heuristic, which allows to obtain better solutions at reasonable computational costs. The solutions obtained by the heuristics can be used to warm start the optimal solving procedure using mathematical models with commercial solvers

XVI Congresso da Associação Portuguesa de Investigação Operacional: livro de resumos

Fundação para a Ciência e a Tecnologia - FC