In this article, a hybrid algorithm is proposed to solve the Vehicle Scheduling Problem with Multiple Depots. The proposed methodology uses a genetic algorithm, initialized with three specialized constructive procedures. The solution generated by this first approach is then refined by means of a Set Partitioning (SP) model, whose variables (columns) correspond to the current itineraries of the final population. The SP approach possibly improves the incumbent solution which is then provided as an initial point to a well-known MDVSP model. Both the SP and MDVSP models are solved with the help of a mixed integer programming (MIP) solver. The algorithm is tested in benchmark instances consisting of 2, 3 and 5 depots, and a service load ranging from 100 to 500. The results obtained showed that the proposed algorithm was capable of finding the optimal solution in most cases when considering a time limit of 500 seconds. The methodology is also applied to solve a real-life instance that arises in the transportation system in Colombia (2 depots and 719 services), resulting in a decrease of the required fleet size and a balanced allocation of services, thus reducing deadhead trips.

This paper assesses the effectiveness in scheduling independent jobs with earliness/tardiness costs and variable setup times applying the Overall Equipment Effectiveness (OEE). The OEE is a common metric for measuring the manufacturing productivity. We defined a mixed-integer linear programming formulation of the parallel machine scheduling problem with four different objective functions in order to compare different scheduling configurations. Real data, from a plastic container manufacturing company located in the Basque Country (Spain), were used to validate this approach. A sensitivity analysis was performed with different production capacities and earliness/tardiness costs in order to evaluate the trade-offs between economic performance (i.e., costs) and the partial rates of OEE (i.e., quality, performance and availability). The objective of this study is to propose a guideline to help management make decisions regarding the measurement and improvement of scheduling effectiveness through contemplating earliness, tardiness and variable setup times.

In the context of city logistics, freight transportation is one of the prominent causes of traffic congestion, high levels of pollution, and safety concerns. To decrease the negative impact of these issues, different methods have been traditionally implemented. On the one hand, the location of urban consolidation Centers (UCCs) near a city can be used to consolidate freight delivery services. Therefore, the number of trucks moving in urban areas can be reduced. On the other hand, Horizontal Cooperation can also help to reduce environmental impact while increasing service level. This paper combines both strategies, that is, we deal with the location of UCCs and, simultaneously, we analyze different scenarios where the players of different supply chain processes exhibit various levels of cooperation. Thus, different levels of cooperations regarding routing and UCCs-location decisions are considered in the following scenarios: (a) non-cooperative case, in which all decisions are decentralized (i.e., each enterprise solves its own vehicle routing problem); (b) low-cooperative case, where depot capacities are shared but the customers are still being served by each company's fleet of vehicles; (c) semi-cooperative case, based on centralized route planning decisions (i.e. facilities and fleets are shared among participating enterprises); and (d) fully cooperative scenario, where the routing plans and facility-location decisions are taken by consensus amongst all the participants. In order to estimate the benefits of both strategies, we propose a flexible metaheuristic algorithm to deal with the combined location and routing problem under the different cooperative scenarios. Our results show impressive benefits of the proposed approach.

Automated Storage and Retrieval Systems (AS/RSs) effectively support warehouse operations in order to increase production and logistics efficiency. Literature about travel time computation in multi-shuttle AS/RSs still needs to be enhanced since most of the existing contributions rely on the same formulation, namely the Meller and Mungwattana’s equation. Based on well-established theoretical assumptions and on a simulation model, the present work puts forward a revised version of the Meller and Mungwattana’s formula for dual shuttle systems. In particular, the constant factor multiplying the travel between time is replaced by a coefficient depending on the rack configuration and on the input and output points of the storage system. The new equation is tested against widely applied models for AS/RS travel time calculation and proves to result in shorter times than the original Meller and Mungwattana’s equation. A linear regression analysis is completed in order to find a numerical formulation of the proposed coefficient. Taking into account some key physical characteristics of a warehouse while estimating travel times allows improving the design and management of storage areas. Future research will focus on deepening multi-shuttle travel time calculation by addressing crane acceleration and deceleration, different rack and crane configurations, as well as class-based storage.

With increase in differentiation and decreasing batch size of products, feeding the assembly line at regular intervals is considered to be a critical problem in today’s manufacturing sector. Yet no clear solution has been developed for this problem; therefore, the main focus of this research is to discuss the different aspects of line feeding, the latest trend in literature, and to propose an innovative method to support solving the problem. A discrete event simulation model is developed and a mathematical model based on particle swarm optimization is used to support the simulation. The hybrid model is finally applied to practical situations. Results show how different settings of kanban influence the performance of the assembly line feeding system. The biggest novelty item is certainly the recognition of the trade-off between kanban size and number of kanban and the importance of investigating its behaviour during the design of the system.

Facing stiff competition in worldwide markets, capability of meeting timely demands of multiproduct and satisfying customer’s desired product quality are essential to present-day manufacturers. Motivated by achieving the aforementioned goals, this research intends to find most economic common cycle length for a multiproduct finite production rate (FPR)-based inventory system, wherein, imperfect production process with expedited fabrication rate and random scrap is assumed. Extra setup and unit costs are associated with the adjusted rate, and imperfect products are screened and scrapped. A mathematical model is cautiously constructed to examine and resolve the problem. A numerical illustration is employed to exhibit the applicability of the proposed method. Except finding the most economic common cycle time for the problem, core contribution of this study also is associated with the individual and combined impact(s) of important factors to the problem, and hence, enabling management of manufacturing firms to make efficient/cost-effective decision and gain competitive advantages.

Concepts for aggregate modeling and simulation are presented and applied for the setting of the parameters of a CONWIP flow control system within the steel products cell at Grand Rapids Chair, a high-mix, low-volume manufacturing environment. Aggregation was accomplished by combining the over 100 types of products into one. This resulted in a single arrival process, routing modeled as random movement through workstations, and a single operation time distribution for each production operation. Aggregation reduced the effort required for model development, data analysis, validation, and simulation. The CONWIP control was shown to be effective in managing the work-in-process. A CONWIP parameter of 50 resulted in an equivalent throughput to that achieved with no limit on work-in-process. Lead time can be reduced by approximately 20% by increasing the CONWIP parameter to 58.

In recent years, many studies on scheduling problems with dedicated machines have been carried out. But, few of them have considered the case of more than two stages. This paper aims at filling this gap by addressing the three-stage hybrid flow shop scheduling problem with two dedicated machines in stage 3. Each job must be processed, consecutively, on the single machines of stages 1 and 2, and depending on its type, it will be further processed on one of the two dedicated machines of stage 3. The objective is to find an optimal schedule that minimizes the maximum completion time (makespan). Since this problem is strongly NP-hard, we first provide some basic results including solutions for several variations of the problem. Then, for the general case we adapt a set of lower bounds from the literature and propose a heuristic approach that is based on the dynamic programming technique, which uses a local search procedure. Finally, various experimentations on several problems with different sizes are conducted and the computational results of the heuristic show that the mean percentage deviation value from the lower bound was lower than 0.8 percent for some instances with 40 to 200 jobs in size.

This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

The storage of goods plays a crucial role in warehouse management systems. Although this operation is often considered simple, it is in fact complex, due to the volume of products, the uncertainty in demands, and the rapid customer service response that the market requires. Moreover, the optimization of logistical resources to maintain an efficient operational flow, such as the allocation of storage spaces, often requires complex decisions. In this paper, we analyze academic contributions on the storage location assignment problem published between 2005 and 2017. The literature is classified according to the solution methods, objectives, and related considerations. Suggestions are also provided for future research.