Hamid Abedinnia

Machine scheduling problems in production

Presents a systematic review of 129 literature reviews on machine scheduling problems in production.Develops a conceptual framework that considers main attributes of machine scheduling problems in production.Presents a comprehensive overview of the state-of-knowledge on machine scheduling problems in production.Derives promising ideas for future research on machine scheduling problems in production from the results of the review. This paper presents the results of a comprehensive and systematic review of 129 literature reviews on machine scheduling problems in production (MSPP). The paper first proposes a conceptual framework that considers the main attributes of MSPP in seven categories and 75 sub-categories. After a descriptive analysis of the sampled papers that give insights into publication patterns for MSPP, a quantitative analysis of the sampled review papers is carried out based on the proposed framework. A synthesis of research findings describes the state-of-knowledge and unveils general deficiencies of literature reviews on MSPP. In addition, the paper provides a comprehensive overview of MSPP, which supports researchers in positioning their own work in the literature and in finding potential innovative research areas. The paper concludes with an outlook on future research opportunities in the area of MSPP.

New simple constructive heuristic algorithms for minimizing total flow-time in the permutation flowshop scheduling problem

This paper develops a set of new simple constructive heuristic algorithms to minimize total flow-time for an n -jobs× m -machines permutation flowshop scheduling problem. We first propose a new iterative algorithm based on the best existing simple heuristic algorithm, and then integrate new indicator variables for weighting jobs into this algorithm. We also propose new decision criteria to select the best partial sequence in each iteration of our algorithm. A comprehensive numerical experiment reveals that our modifications and extensions improve the effectiveness of the best existing simple heuristic without affecting its computational efficiency. This paper extends the well-known NEH heuristic.The paper introduces new priority rules and decision criteria for the insertion phase.The performance of the proposed 103 heuristics is compared to two well-known heuristics.The proposed heuristics outperform the best known simple heuristic.

Scheduling electric vehicles making milk-runs for just-in-time delivery

Abstract Battery-operated electric vehicles are frequently used in in-plant logistics systems to feed parts from a central depot to workcells on the shop floor. These vehicles, often called tow trains, make many milk-run trips during a typical day, with the delivery timetable depending on the production schedule. To operate such a milk-run delivery system efficiently, not only do the timetabled trips need to be assigned to vehicles, it is also important to take the limited battery capacity into consideration. Moreover, since most tow trains in use today are still operated by human drivers, fairness aspects with respect to the division of the workload also need to be considered. In this context, we tackle the following problem we encountered at a large manufacturer of engines for trucks and busses in Germany. Given a fixed schedule of milk-runs (round trips) to be performed during a planning horizon, and a fleet of homogeneous electric vehicles stationed at a depot, which vehicle should set out on which milk-run and when should recharging breaks be scheduled, such that all runs can be completed with the minimum number of vehicles and all vehicles are about equally busy? We investigate the computational complexity of this problem and develop suitable heuristics, which are shown to solve instances of realistic size to near-optimality in a matter of a few minutes. We also offer some insight into how battery technology influences vehicle utilization.

An integrated model to improve ergonomic and economic performance in order picking by rotating pallets

Abstract This paper studies manual order picking activities in a warehouse where items are stored on pallets in two rows one above the other. Items stored on the pallets may be heavy, and manually picking the items may require excessive bending and stretching, which results in high spinal loads on the order picker associated with high injury risks. For this scenario that can frequently be observed in practice, this paper proposes an integrated model that supports the planning of order picking operations and pallet rotations taking account both of the time required for completing a set of orders as well as the spinal load on the order picker and the consequent injury risks. The results of a numerical experiment indicate that selectively rotating pallets may both reduce order picking time as well as the load on the order picker, leading to a quicker and less risky order picking process. The model proposed in this paper supports the decision of which pallet to rotate (and which not to rotate) against the companys cost objectives and its endeavor for worker well-being.