A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
Solving binary cutting stock problems by column generation and branch-and-bound
Computational Optimization and Applications
Scheduling a batch processing machine with incompatible job families
Computers and Industrial Engineering
Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure
Computers and Operations Research
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Solving Parallel Machine Scheduling Problems by Column Generation
INFORMS Journal on Computing
Time-Indexed Formulations for Machine Scheduling Problems: Column Generation
INFORMS Journal on Computing
A Set-Covering-Based Heuristic Approach for Bin-Packing Problems
INFORMS Journal on Computing
Computers and Operations Research
An exact algorithm for IP column generation
Operations Research Letters
Minimizing makespan on a single batching machine with release times and non-identical job sizes
Operations Research Letters
The train delivery problem: vehicle routing meets bin packing
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Tactical fixed job scheduling with spread-time constraints
Computers and Operations Research
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In this paper, we consider the scheduling problem on a single batch processing machine with non-identical job sizes; in which the machine has a limited capacity and can process a group of jobs simultaneously as a batch. The processing time of a batch is the longest processing time of all jobs in the batch. The objective is to minimize the makespan. We formulate the problem using Dantzig-Wolfe decomposition as a set partitioning problem. Based on the set partitioning formulation, we present a tight lower bound using column generation method. A heuristic algorithm is also developed to generate the basic solution in the column generation method. A branch and price algorithm which combines the column generation technique with branch and bound method is then presented to obtain the optimal solution of the problem. The efficiency of the proposed branch and price algorithm is ultimately compared to the branch and bound algorithm from the literature, based on the generated sample problems.