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This chapter proposes a hybrid heuristic method combining a clustering search (CS) metaheuristic with an exact algorithm to solve a two-stage capacitated facility location problem (TSCFLP). The TSCFLP consists of defining the optimal locations of plants and depots and the product flow from plants to depots (first stage) and from depots to customers (second stage). The problem deals commonly with cargo transportation in which products must be transported from a set of plants to meet customers’ demands passing out by intermediate depots. The main decisions to be made are related to define which plants and depots must be opened from a given set of potential locations, which customer to assign to each one of the opened depots, and the amount of product flow from the plants to the depots and from the depots to the customers. The objective is to minimize costs satisfying demand and capacity constraints. Computational results demonstrate that our method was able to find good solutions when comparing it directly with a commercial solver and a genetic algorithm (GA) reported in a recent chapter found in the literature, requiring less than 1.5% and 41% of the computational time performed by these methods, respectively. Thus, our hybrid method combining CS with an exact algorithm can be considered as a new matheuristic to solve the TSCFLP.

This paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)?

This paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers.

The computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.

The results provide managerial insights for express enterprises from the perspective of operation research to make decisions.

This paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.

Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited.

This systematic review used Web of Science and Scopus as search engines, which are extensive databases in Operations Research and Management Science. Ninety-five articles regarding food bank operations were deeply analyzed to contribute to this literature review.

Through a systematic literature review, this paper identifies the challenges faced by food banks from an operations management perspective and positions the scientific contributions proposed to address these challenges.

This study makes three main contributions to the current literature. First, this study provides new researchers with an overview of the key features of food bank operations. Second, this study identifies and classifies the proposed optimization models to support food bank managers with decision-making. Finally, this study discusses the challenges of food bank operations and proposes promising future research avenues.

The purpose of this paper is to investigate the application of the dynamic vehicle routing problem for last mile distribution during disaster response. The authors explore a model that involves limited heterogeneous vehicles, multiple trips, locations with different accessibilities, uncertain demands, and anticipating new locations that are expected to build responsive last mile distribution systems.

The modified simulated annealing algorithm with variable neighborhood search for local search is used to solve the last mile distribution model based on the criterion of total travel time. A dynamic simulator that accommodates new requests from demand nodes and a sample average estimator was added to the framework to deal with the stochastic and dynamicity of the problem.

This study illustrates some practical complexities in last mile distribution during disaster response and shows the benefits of flexible vehicle routing by considering stochastic and dynamic situations.

This study only focuses day-to-day distribution on road/land transportation for distribution, and additional transportation modes need to be considered further.

The proposed model offers operational insights for government disaster agencies by highlighting the dynamic model concept for supporting relief distribution decisions. The result suggests that different characteristics and complexities of affected areas might require different distribution strategies.

This study modifies the concept of the truck and trailer routing problem to model locations with different accessibilities while anticipating the information gap for demand size and locations. The results show the importance of flexible distribution systems during a disaster for minimizing the disaster risks.

The purpose of this paper is to develop a mathematical model for the design of robust layout for unequal area-dynamic facility layout problem with flexible bay structure (UA-DFLP with FBS) and test the suitability of generated robust layout in a dynamic environment.

This research adopts formulation of a mathematical model for generating a single layout for unequal area facility layout problems with flexible bay structure under dynamic environment. The formulated model for the robust layout formation is solved by developing a simulated annealing algorithm. The proposed robust approach model for UA-DFLP with FBS is validated by conducting numerical experiments on standard UA-DFLPs reported in the literature. The suitability of the generated robust layout in a dynamic environment is tested with total penalty cost criteria.

The proposed model has given a better solution for some UA-DFLPs with FBS in comparison with the adaptive approach’s solution reported in the literature. The total penalty cost is within the specified limit given in the literature, for most of the layouts generated for UA-DFLPs with FBS. In the proposed model, there is no rearrangement of facilities in various periods of planning horizon and thus no disruptions in operations.

The present work has limitations that when the area and aspect ratio of the facilities are required to change from one period to another, then it is not possible to make application of the robust approach-based formulation to the dynamic environment facility layout problems.

Rearrangement of facilities in adaptive approach disrupts the operations whereas in the proposed approach no disruption of production. The FBS approach is more suitable for layout planning where proper aisle structure is required. The solution of the proposed approach helps to create a proper aisle structure in the detailed layout plan. Thus, easy interaction of the material handling equipment, men and materials is possible.

This paper proposes a mathematical formulation for the design of robust layout for UA-FLPs with FBS in a dynamic environment and an efficient simulated annealing algorithm as its solution procedure. The proposed robust approach generates a single layout for the entire planning horizon. This approach is more useful for facilities which are difficult/sensitive to relocate in various periods of the planning horizon.

The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.

The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.

The results of the study present the researchers’ tendencies and preferences when designing their BSC network models.

The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.

The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

Drugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.

A mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.

The model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.

The VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.