Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems.

The processing time of the machine is assumed fixed in several studies. In many real industrial applications, the processing time is affected by learning and forgetting effects. This research proposes a scheduling approach to support a manufacturing system under learning/forgetting effect. The approach is supported by a Multi-Agent System to perform the scheduling activities in a quasi-real-time and in general manufacturing systems. A simulation environment is developed to test the proposed approach and the results are compared with a benchmark model for evaluating several performance measures of the manufacturing system. The simulation results highlight how the proposed approach improves all the performance measures under different conditions of inter-arrival time, learning and forgetting rates. A complete Analysis of the Variance highlights the main effects on the performance measures to support the decision maker of the manufacturing system.

Finding the locations of departments or machines in a workspace is classified as a Facility Layout Problem. Good placement of departments has a relevant influence on manufacturing costs, work in process, lead times and production efficiency. This paper analyses the problem of allocating departments with restrictions in terms of unequal area and rectangular shape within a facility, in order to minimize the sum of material handling costs taking into account the satisfaction of the aspect ratio requested. In particular, we propose for the first time a Firefly Algorithm based on the slicing structure encoding. The proposed method was tested comparing the results obtained from other authors on the same literature instance. The results confirm the effectiveness of the Firefly Algorithm in solving the Facility Layout Problem by generating the best solutions with respect to those provided by previous researches.

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.

This paper aims to develop a maintenance optimization model to maintain a high level of efficiency and reliability of the machinery. The methodological approach is based on preventive maintenance through the partial or total replacement of critical components. Although an intermediate intervention control, the focus is on a particular machine that has stopped several times, reducing its operational availability and resulting in a high cost of non-production. This study uses a Weibull model to analyze and optimize the correct maintenance process of the machinery considered. The failure data are then analyzed and scheduled. The final purpose is to standardize the operators' intervention procedures to reduce the time for the same interventions.

Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

Evaluation of the software usability as concerns software alternatives represents a typical Multi-Criteria Decision-Analysis (MCDA) problem. Such a kind of problem can be successfully faced via structured models and approaches which validity have been widely shown in literature. In this work, a methodological framework based on a MCDA model integrating the DEMATEL method along with the ANP technique is proposed and explained with more detail to address the relevant issue of the software usability evaluation. The applicability of the considered DEMATEL-ANP (DANP) based model in the under analysis contest is demonstrated via a preliminary investigation, where the usability of the two major Mobile Operating Systems (MOSs) is compared and quantitatively prioritized. Results of both performed comparison method and sensitivity analysis confirm the validity of the proposed MCDA model as well robustness of its outcomes. Moreover, results obtained point out about the influenced, independent and core factors for the MOSs usability, as well as strengths, weaknesses and discrepancies in the usability as concerns the two investigated alternatives. Representing these outcomes crucial aspects for driving focused MOSs usability improvement processes and strategies, they give also evidence of the effectiveness of the proposed methodological framework.

A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch|Σ Cj ) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated.