Tow Trains Research Articles

Collaborative optimization of route planning and just-in-time scheduling for mixed-model assembly lines

With increasingly diversified consumer demands, mixed-model assembly lines have been increasingly adopted by manufacturing enterprises. In recent years, more and more manufacturers adopted material supermarkets to enable a flexible and reliable Just-in-Time part supply of their mixed-model assembly lines. However, it is still a crucial challenge to ensure the implementation of Just-in-time part supply, and few research studies on the problem. Therefore, this article proposes the problem of collaborative optimizing route planning and material distribution scheduling with just-in-time principle. A mixed integer linear programming model is established with the objective of minimizing the total costs. Moreover, a dynamic programming based heuristic algorithm is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test this algorithm. The computational results reveal the feasibility and effectiveness of the proposed algorithm. And considering different capacity of tow trains, results show the change of capacity affects the total cost by affecting the number of paths divided, which provides guidance for manufacturing enterprises.

Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines

With the increase in product varieties, the combination of supermarkets and tow trains is being adopted by more automobile manufacturers for part feeding, especially in mixed-flow assembly lines. This paper focuses on the routing, scheduling, and loading problems of a single towed train that transports parts from one supermarket to the workstation buffer in a mixed-flow assembly line and aims to optimize the loading of the tow train, the optimal delivery schedule and route, and the appropriate departure time to minimize shipping and line inventory costs. To enable part feeding in line with the just-in-time (JIT) principle, a new mixed-integer mathematical model from nonlinearity to linearity and a novel artificial immune genetic algorithm-based heuristic are proposed. Both methods can provide reasonable solutions compared by minimizing the route length and inventory level in terms of speed, and the genetic algorithm shows better performance on a large scale.

Proactive in-house part-feeding for mixed-model assembly systems with dynamics

The rich data collected from smart shop floors necessitate proactive decision-making to relieve system nervousness in a dynamic environment. This proactivity takes real-time actions based on disruption-driven prediction but not on disruption-created negative effects. This paper focuses on investigating a proactive in-house part-feeding (PIP) approach by integrating real-time data collected from shop floors with the physical properties of a mixed-model assembly system. An event-driven proactive prediction for replenishments is first implemented after coupling such data as disruptions, job rescheduling, part re-releasing, and real-time lineside inventories with a widely used reorder-point-based replenishment policy. This prediction can provide sufficient prior information about replenishments and, in turn, trigger tow-train rerouting to simultaneously minimize the costs of distribution and lineside inventories. In terms of limited tow train capacity and the just-in-time part-feeding goal, this rerouting problem should address various constraints on multiple trips, time windows, and the real-time working status of tow trains. An adaptive large neighbourhood search (ALNS) is developed to obtain the best solution for rerouting by designing customized removal and insertion operators, as well as local search heuristics. A case study of a real-life car seat assembly system is presented to verify the efficiency of the PIP. Several rerouting benchmark instances are obtained from the case study to evaluate the performance of ALNS, and the computational results indicate that the proposed ALNS has a satisfactory performance in solving the rerouting instances compared with some of the formerly proposed and published methods. The results of the practical case study illustrate that the PIP may increase the lineside inventory cost slightly but can significantly reduce the distribution cost compared with prevailing reactive approaches

The impact of transportation optimisation on assembly line feeding

In the era of mass customisation, feeding parts to mixed-model assembly lines has proven to be a complex task since customers increasingly demand personalised end products. Consequently, the number of parts required at a single assembly line is sharply increasing. On the one hand, part supply must be done with the aim of avoiding excessive logistical handling activities while managing space at the border of line carefully. Hence, different line feeding policies can be exploited. On the other hand, shortages in parts supply, which may result in line stoppage, must be avoided. To this end, different vehicle types such as forklifts, automated guided vehicles and tow trains must be orchestrated carefully. This study is the first to propose a mixed integer programming model that efficiently assigns each part at the same time to a feeding policy and a vehicle type, with the goal to minimise total feeding costs. To accurately quantify costs, the model selects specific routes and determines the fleet size of every vehicle type used. The model is complemented by valid inequalities and validated by solving artificial problem instances. Within the analysis, we demonstrate that optimal selection of vehicle types is superior to heuristic approaches and show that this optimisation-based approach is around 8% cheaper than the industrial standard.

A dynamic scheduling mechanism of part feeding for mixed-model assembly lines based on the modified neural network and knowledge base

Inspired by the manufacturing costs proportion of part feeding in automotive mixed-model assembly lines (MMALs) being up to 20–35%, this paper takes the dynamic scheduling of part feeding for automotive MMALs as a crucial and complex problem. Therefore, a dynamic scheduling mechanism basing on the knowledge base (KB) and fruit fly optimization algorithm (FOA) with variable step sizes and logistic chaos (VSCFOA)-enhanced general regression neural network (VSCFOA-GRNN) is proposed to tackle the real-time part feeding scheduling problem of tow trains under the dynamic manufacturing system. A mathematical model is developed to illustrate the problem, where the throughput of the assembly line and the material delivery distance are determined as components of the objective function. Subsequently, samples of the MMAL are generated by the plant simulation software and used to train the VSCFOA-GRNN model off-line. Afterward, the trained model and KB are adopted in the real-time scheduling process to determine the optimal scheduling rule combination. Finally, the effectiveness, feasibility and accuracy of the novel scheduling mechanism are validated by computational results, especially in dynamic scheduling processes. It can cope well with changes in the dynamic environment, thus effectively realizing the higher productivity of assembly lines and better system performance.

Scheduling Just-in-Time Transport Vehicles to Feed Parts for Mixed Model Assembly Lines

In order to solve the problem of vehicle scheduling to feed parts at automobile assembly line, this study proposes a just-in-time delivery method combined with the mode of material supermarket. A mixed integer linear programming model with the primary objective of using the least number of tow trains is constructed by considering capacity of vehicle and inventory levels of line. On the basis of the minimum number of tow trains, the schedule of each tour is reasonably planned to minimize inventory of assembly line, which is the secondary objective of the part supply problem. Additionally, a heuristic algorithm which can obtain a satisfactory solution in a short time is designed to solve large-scale problems after considering continuity and complexity of modern automobile production. Furthermore, some cases are analyzed and compared with the widely used periodic delivery strategy, and the feasibility of just-in-time model and algorithm is verified. The results reveal that just-in-time delivery strategy has more advantages in reducing inventory level than periodic delivery strategy.

Optimally scheduling and loading tow trains of in-plant milk-run delivery for mixed-model assembly lines

PurposeThis paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.Design/methodology/approachAn immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.FindingsThe modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.Research limitations/implicationsThis study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.Originality/valueThe capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

Loading tow trains ergonomically for just-in-time part supply

Faced with an aging workforce, many manufacturing companies consider alleviating the ergonomic strain of material handling on their workers increasingly important. This is one of the reasons why frequent small-lot deliveries of parts to the assembly stations on the shop floor via small electric delivery vehicles – so-called tow trains – have become widespread in many industries. Deploying tow trains, however, does not automatically ease the ergonomic burden on logistics workers, but requires careful stowage planning in addition. In this paper, we consider the following problem. Given a set of bins of differing weight to be carried by tow train to a given set of stations on the shop floor, where should each bin be stowed on the tow train such that it can be unloaded efficiently from an economic perspective while also minimizing the ergonomic strain during loading and unloading? We investigate the physiological stress of handling bins on different levels of a tow train wagon by applying an established ergonomic evaluation method from the human factors engineering literature. We model the ensuing optimization problem as a special type of assignment problem and propose suitable exact and heuristic solution methods. In a computational study, our approaches are shown to perform well, delivering optimal solutions for instances of realistic size within fractions of a second in many cases. We show that optimal stowage plans can significantly ease the physiological burden on the workforce without compromising economic efficiency. We also derive some insights into the ideal layout of the tow train from an ergonomics perspective.

Sequencing assembly lines to facilitate synchronized just-in-time part supply

The problem of sequencing assembly lines consists of determining the order in which a given set of products is launched down the line. Since individual products may require different parts in different quantities, the production sequence has a big influence on line-side inventory. Classically, sequences are often optimized with the goal of attaining level schedules, i.e., the part demand should be smooth during the planning horizon. However, this approach does not necessarily work well if parts are delivered at discrete points in time in bulk quantities. In this paper, we consider a production system where bins of parts are delivered periodically by a tow train from a central depot at fixed times. Due to the limited space at the assembly line, the maximum number of bins in stock at any time at any station should be minimal. We propose an exact solution method based on combinatorial Benders decomposition as well as bounding procedures and heuristics for this problem. The algorithms are shown to perform well both on instances from the literature and on new data sets. We also investigate whether classic level scheduling methods are effective at reducing line-side stock in an assembly system supplied by tow train, and to what degree line-side stock can be traded off for more frequent deliveries.

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

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.

Energy-efficient planning for supplying assembly lines with vehicles

This paper focuses on minimizing the energy consumed by vehicles for parts supplying to workstations in mixed-model assembly lines. The parts are assumed to be packed in bins (or pallets). The bins are shipped from a so-called “supermarket” and delivered periodically to the workstations using tow trains with wagons. For each time period, depending on the production plan, a workstation has a specific demand of parts expressed as a portion of a bin. The problem is to define the sequence of workstations to be visited by the tow train at each time period, as well as the number of bins to be delivered at each stop, so as to avoid any material shortage. This paper provides an analysis of the computational complexity related to this problem and proposes two original mixed integer linear programming formulations. We also provide computational analysis and experiments that put the efficiency of one particular formulation into evidence and show the conditions under which energy-efficient supplying is not in contradiction with economic efficiency.

Just-In-Time Vehicle Routing for In-House Part Feeding to Assembly Lines

This paper deals with the problem of routing in-house transport vehicles that feed parts to workstations in assembly plants or workshops just in time. The capacitated vehicles, typically so-called tow trains, perform their assigned route cyclically without break and provide each station with the exact quantity of parts required until the next arrival of the vehicle. Hence, the demand of each station depends on the duration of the route serving the station: The longer the route duration, the less frequently the station is visited and the higher its demand. The goal is to minimize first the number of vehicles and second the total route duration, while respecting given minimum service frequencies at the stations. We provide a mathematical formulation of this novel problem and address it by means of a large neighborhood search. The algorithm is able to solve realistic instances in acceptable time and vastly outperforms a default solver. We discuss two variants of the problem, one in which split deliveries to stations are allowed and another assuming that all stations lie on a straight line. Finally, we investigate the extent to which assuming constant demand rates may lead to problems during the day-to-day operations of the part-feeding system, where demands are not necessarily constant.

A Model to Optimize the Reference Storage Assignment in a Supermarket to Expedite the Part Feeding Activities

In many industrial environments, picking operations are performed with tow trains in order to efficiently feed a production or an assembly system. This operation is often time-consuming, especially for the automotive environment, which employs the supermarket concept very often to manage thousands of different parts. This research aims at improving the picking operations by optimizing the storage assignment of the references in a supermarket. This is a process in which the references are placed in specific racks of the warehouse. A linear programming formulation is provided so that the picking operations can be performed in the minimum amount of time. The model considers some constraints linked to the specific kind of equipment used in a warehouse, and the ergonomic energy expenditure. This approach has been implemented in a numerical case inspired by a real industrial application.

Scheduling in-house transport vehicles to feed parts to automotive assembly lines

Due to exorbitant product variety, very limited space, and other factors, organizing efficient and timely deliveries of parts and subassemblies to final assembly within the factory is one of the most pressing problems of modern mixed-model assembly production. Many automobile producers have implemented the so-called “supermarket” concept to transfer material to the assembly line frequently and in small lots. Supermarkets are decentralized logistics areas on the shop floor where parts are intermediately stored for nearby assembly cells, to be ferried there by small transport vehicles (called tow trains or tuggers). This paper tackles the operational problem of drawing up schedules for these tow trains, such that the assembly line never starves for parts while also minimizing in-process inventory, thus satisfying just-in-time goals. We prove strong NP-completeness of the problem and present exact and heuristic solution methods. In a computational study, the procedures are shown to perform very well, solving realistic instances to (near-)optimality in a matter of minutes, clearly outperforming the simple cyclic schedules commonly used in industrial practice. We also provide some managerial insight into the right degree of automation for such a part feeding system.

Part supply method for mixed-model assembly lines with decentralized supermarkets

In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production sequence and line layout, the proposed strategy schedules tow train routing and delivery problems jointly to minimize the number of employed town trains and the traveling time, while ensuring that stations never run out of parts. To solve this problem, a mathematical formulation is proposed for each sub-problem aiming at minimizing supply cost. Then, a dynamic programming algorithm for routing and a greedy algorithm for delivery are developed, both of which are of polynomial runtime. Finally, a computational study is implemented to validate the effectiveness of the strategy, and to investigate the effects of the delivery capacity of tow trains and storage capacity of stations on supply cost.

A dynamic delivery strategy for material handling in mixed-model assembly lines using decentralized supermarkets

Material handling has become one of the major challenges in modern production management. Consequently, this paper intends to investigate the part delivery of mixed-model assembly lines with decentralized supermarkets and tow trains. Besides, uncertain exception disturbances, including tow train failures and adjustments of the production sequence, are also considered. To solve this problem, a heuristic-based dynamic delivery strategy is proposed, which dynamically schedules the route, departure time, quantities and types of loaded parts for each tour. To evaluate the performance of this strategy, it is used to solve an instance in comparison with the periodic delivery strategy, experimental results are reported and their performances are compared under different metrics. Moreover, a multi-scenario analysis is employed to determinate the long-term decisions, including the number of tow trains and the route layout. Finally, the critical storage is suggested to be set for each station to avoid part starvation resulting from disturbances, and its effect on the delivery performance is investigated.

Cyclic inventory routing in a line-shaped network

The inventory routing problem (IRP) is a very challenging optimization task that couples two of the most important components of supply chain management, i.e., inventory control and transportation. Routes of vehicles are to be determined to repeatedly resupply multiple customers with constant demand rates from a single depot. We alter this basic IRP setting by two aspects: (i) only cyclic tours are allowed, i.e., each vehicle continuously tours its dedicated route, and (ii) all customers are located along a line. Both characteristics occur, for instance, in liner shipping (when feeder ships service inland ports along a stream) and in facility logistics (when tow trains deliver part bins to the stations of an assembly line). We formalize the resulting problem setting, identify NP-hard as well as polynomially solvable cases, and develop suited solution procedures.

How to Supply Assembly Liines

Great efforts are currently dedicated to cost engineering in industry. The lean production approach is a good way to lower production costs. Material flows and treatment should be planned and controlled neither too early nor too late, because early production leads to an increase of stocks of materials and late production to a backlog of orders. This task is especially important in serial production, e.g. in the automotive industry. A buffer stock could be connected with assembly lines in many ways, such as handcarts, fork lift trucks or special cableways. A modern method of material supply in an assembly workshop is the usage of tow trains consisting of a tow truck and several carts. The number of carts in a train can change but it cannot be high because of turnings and crossings. The task is to find and optimize transport routes. This task is considered to be NP-hard. The paper describes several approaches to solving the task.

A genetic algorithm for supermarket location problem

Purpose – This purpose of this paper is to investigate the location problem of supermarkets, feeding by material the mixed model assembly lines using tow trains. It determines the number and the locations of these supermarkets to minimize transportation and inventory fixed costs of the system. Design/methodology/approach – This is done using integer programming model and real genetic algorithm (RGA) in which custom chromosomes representation, two custom mating and two custom mutation operators were proposed. Findings – The performance of RGA is very good since it gives results that are very close or identical to the optimal ones in reasonable CPU time. Research limitations/implications – The study is applicable only if a group of supermarkets feed the same assembly line. Originality/value – For the first time in supermarket location problem, limitation on availability of some areas for possible supermarkets ' locations and capacity of the supermarkets were taken into consideration.

Efficient material flow in mixed model assembly lines

In this study, material flow from decentralized supermarkets to stations in mixed model assembly lines using tow (tugger) trains is investigated. Train routing, scheduling, and loading problems are investigated in parallel to minimize the number of trains, variability in loading and in routes lengths, and line-side inventory holding costs. The general framework for solving these problems in parallel contains analytical equations, Dynamic Programming (DP), and Mixed Integer Programming (MIP). Matlab in conjunction with LP-solve software was used to formulate the problem. An example was presented to explain the idea. Results which were obtained in very short CPU time showed the effect of using time buffer among routes on the feasible space and on the optimal solution. Results also showed the effect of the objective, concerning reducing the variability in loading, on the results of routing, scheduling, and loading. Moreover, results showed the importance of considering the maximum line-side inventory beside the capacity of the train in the same time in finding the optimal solution.