Tabu Search Algorithm for Single and Multi-model Line Balancing Problems

This paper deals with the assembly line balancing issue. The considered objective is to minimize the weighted sum of products’ cycle times. The originality of this objective is that it is the generalization of the cycle time minimization used in single-model lines (SALBP) to the multi-model case (MALBP). An optimization algorithm made of a heuristic and a tabu-search method is presented and evaluated through an experimental study carried out on several and various randomly generated instances for both the single and multiproduct cases. The returned solutions are compared to optimal solutions given by a mathematical model from the literature and to a proposed lower bound inspired from the classical SALBP bound. The results show that the algorithm is high performing as the average relative gap between them is quite low for both problems

Lot-Sizing and Scheduling for the Plastic Injection Molding Industry - A Hybrid Optimization Approach

The management of industrial systems is done through different levels, ranging from strategic (designing the system), to tactical (planning the activities and assigning the resources) and operational (scheduling the activities). In this paper, we focus on the latter level by considering a real-world scheduling problem from a plastic injection company, where the production process combines parallel machines and a set of resources. We present a scheduling algorithm that combines a metaheuristic and a list algorithm. Two metaheuristics are tested and compared when used in the proposed scheduling approach: the stochastic descent and the simulated annealing. The method’s performances are analyzed through an experimental study and the obtained results show that its outcomes outperform those of the scheduling policy conducted in a case-study company. Moreover, besides being able to solve large real-world problems in a reasonable amount of time, the proposed approach has a structure that makes it flexible and easily adaptable to several different planning and scheduling problems. Indeed, since it is composed by a reusable generic part, the metaheuristic, it is only required to develop a list algorithm adapted to the objective function and constraints of the new problem to be solved.FEDER Hauts de France and CEA Tec

Economic and ergonomic performance enhancement in assembly process through multiple collaboration modes between human and robot

Collaborative robots have open new ways of designing assembly processes, thanks to their ability to share work space with operators. not only may they support the economical performance, but they can also improve the overall ergonomics. Building on existing work on task allocation problems, the authors study further the collaboration opportunities between operator and robot, namely cooperation phases (type of collaboration where both operator and robot act on the same work piece). This work proposes anewformulation of the related problem, and solutions are sought through heuristics methods, to investigate whether concurrent usage of different collaboration modes delivers better performance. The results indicate that cooperation mode enables higher process performances while controlling ergonomic risks. With a concern for real-life application, it has been applied on a real case study to verify its applicability

Job-shop production scheduling with reverse flows

International audienc

Multi-stage appointment scheduling for outpatient chemotherapy unit: a case study

This paper deals with a multi stage hybrid flow-shop problem (HFSP) that arises in a privately Chemotherapy clinic. It aims to optimize the makespan of the daily chemotherapy activity. Each patient must respect the cyclic nature of chemotherapy treatment plans made by his referent on- cologist while taking into account the high variability in resource requirements (treatment time, nurse time, pharmacy time). The problem requires the assignment of chemotherapy patients to oncologists, pharmacists, chemotherapy beds or chairs and nurses over a 1-day period. We provided a Mixed Integer Program (MIP) to model this issue, which can be considered as a five-stage hybrid flow-shop scheduling problem with additional resources, dedicated machines, and no-wait constraints. Since this problem is known to be NP-hard, we provided a lower bound expression and developed an approximated solving algorithm: a tabu search inspired metaheuristic based on a constructive heuristic that can quickly reach satisfying results. To assess the empirical performance of the proposed approach, we conducted experi- ments on randomly generated instances based on real-world data of a Tunisian private clinic: Clinique Ennasr. Computational experiments show the efficiency of the proposed procedures: The mathematical model provided optimal solutions in reasonable computational time only for small instances (up to 10 patients). Meta-heuristic’s results demonstrate, also, that the proposed approach offers good results in terms of solution quality and computational times with an average relative gap to the MIP solution equal to 3.13% and to the lower bound equal to 5.37% for small instances (up to 15 patients). The same gap to the lower bound increases to 25% for medium and large size instances (20–50 patients)