A Dynamic Heuristic for the Stochastic Unrelated Parallel Machine Scheduling Problem

This paper addresses the problem of batch scheduling in an unrelated parallel machine environment with sequence dependent setup times and an objective of minimizing the total weighted mean completion time. The jobs\u27 processing times and setup times are stochastic for better depiction of the real world. This is a NP-hard problem and in this paper, new heuristics are developed and compared to existing ones using simulation. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm PMWP over the other algorithms presented

Scheduling reentrant jobs on parallel machines with a remote server

This paper explores a specific combinatorial problem relating to re-entrant jobs on parallel primary machines, with a remote server machine. A middle operation is required by each job on the server before it returns to its primary processing machine. The problem is inspired by the logistics of a semi-automated micro-biology laboratory. The testing programme in the laboratory corresponds roughly to a hybrid flowshop, whose bottleneck stage is the subject of study. We demonstrate the NP-hard nature of the problem, and provide various structural features. A heuristic is developed and tested on randomly generated benchmark data. Results indicate solutions reliably within 1.5% of optimum. We also provide a greedy 2-approximation algorithm. Test on real-life data from the microbiology laboratory indicate a 20% saving relative to current practice, which is more than can be achieved currently with 3 instead of 2 people staffing the primary machines

Parameterized complexity of machine scheduling: 15 open problems

Machine scheduling problems are a long-time key domain of algorithms and complexity research. A novel approach to machine scheduling problems are fixed-parameter algorithms. To stimulate this thriving research direction, we propose 15 open questions in this area whose resolution we expect to lead to the discovery of new approaches and techniques both in scheduling and parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc

Capacity Planning and Leadtime management

In this paper we discuss a framework for capacity planning and lead time management in manufacturing companies, with an emphasis on the machine shop. First we show how queueing models can be used to find approximations of the mean and the variance of manufacturing shop lead times. These quantities often serve as a basis to set a fixed planned lead time in an MRP-controlled environment. A major drawback of a fixed planned lead time is the ignorance of the correlation between actual work loads and the lead times that can be realized under a limited capacity flexibility. To overcome this problem, we develop a method that determines the earliest possible completion time of any arriving job, without sacrificing the delivery performance of any other job in the shop. This earliest completion time is then taken to be the delivery date and thereby determines a workload-dependent planned lead time. We compare this capacity planning procedure with a fixed planned lead time approach (as in MRP), with a procedure in which lead times are estimated based on the amount of work in the shop, and with a workload-oriented release procedure. Numerical experiments so far show an excellent performance of the capacity planning procedure

The lockmaster's problem.

Inland waterways form a natural network that is an existing, congestion free infrastructure with capacity for more traffic.Transportation of goods by ship is widely promoted as it is a reliable, efficient and environmental friendly way of transport. A bottleneck for transportation over water are the locks that manage the water level. The lockmaster's problem concerns the optimal strategy for operating such a lock. In the lockmaster's problem we are given a lock, a set of ships coming from downstream that want to go upstream, and another set of ships coming from upstream that want to go downstream. We are given the arrival times of the ships and a constant lockage time; the goal is to minimize total waiting time of the ships. In this paper a dynamic programming algorithm (DP) is proposed that solves the lockmaster's problem in polynomial time. We extend this DP to different generalizations that consider weights, water usage, capacity, and (a fixed number of) multiple chambers. Finally, we prove that the problem becomes strongly NP-hard when the number of chambers is part of the input.Lock scheduling; Batch scheduling; Dynamic programming; Complexity;

A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry

Abstract This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry. The production process consists of three production stages from spinning, weaving, and dyeing. All stages have one processor. Setup time between two consecutive jobs with different color is considered. This paper also proposes attribute’s decomposition of a single job to classify available jobs to be processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm is proposed in which the permutation of set of jobs with same attribute and the permutation among set of jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution, then is compared with other known optimal methods