The management of food distribution networks is receiving more and more attention, both in practice and in the scientific literature. In this paper, we review quantitative operations management approaches to food distribution management, and relate this to challenges faced by the industry. Here, our main focus is on three aspects: food quality, food safety, and sustainability. We discuss the literature on three decision levels: strategic network design, tactical network planning, and operational transportation planning. For each of these, we survey the research contributions, discuss the state of the art, and identify challenges for future research.
Humanitarian relief logistics is one of the most important elements of a relief operation in disaster management. The present work develops a multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty. In our approach, not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands might arise and the possibility that some of the pre-positioned supplies in the relief distribution center or supplier might be partially destroyed by the disaster. Our multi-objective model attempts to minimize the sum of the expected value and the variance of the total cost of the relief chain while penalizing the solution’s infeasibility due to parameter uncertainty; at the same time the model aims to maximize the affected areas’ satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. We present a case study of our robust stochastic optimization approach for disaster planning for earthquake scenarios in a region of Iran. Our findings show that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.
The current decade sees a considerable growth in worldwide container transportation and with it an indispensable need for optimization. Also the interest in and availability of academic literatures as well as case reports are almost exploding. With this paper an earlier survey which proved to be of utmost importance for the community is updated and extended to provide the current state of the art in container terminal operations and operations research.
Efficiently coordinating the movement of trains on a railway network is a central part of the planning process for a railway company. This paper reviews models and methods that have been proposed in the literature to assist planners in finding train routes. Since the problem of routing trains on a railway network entails allocating the track capacity of the network (or part thereof) over time in a conflict-free manner, all studies that model railway track allocation in some capacity are considered relevant. We hence survey work on the train timetabling, train dispatching, train platforming, and train routing problems, group them by railway network type, and discuss track allocation from a strategic, tactical, and operational level.
The current paper presents a structured overview over the literature on dynamic simultaneous lotsizing and scheduling problems. We introduce a classification scheme, review the historical development of research in this area and identify recent developments. The main contribution of the present review is the discussion of the historical development of the body of knowledge in the field of simultaneous lotsizing and scheduling and the identification of recent trends. This helps to reveal research opportunities, but it can also be helpful in the selection of appropriate models for industrial applications.
In the last four decades the container as an essential part of a unit-load-concept has achieved undoubted importance in international sea freight transportation. With ever increasing containerization the number of seaport container terminals and competition among them have become quite remarkable. Operations are nowadays unthinkable without effective and efficient use of information technology as well as appropriate optimization (operations research) methods. In this paper we describe and classify the main logistics processes and operations in container terminals and present a survey of methods for their optimization.
In this paper, we present a corridor method inspired algorithm for a blocks relocation problem in block stacking systems. Typical applications of such problem are found in the stacking of container terminals in a yard, of pallets and boxes in a warehouse, etc. The proposed algorithm applies a recently proposed metaheuristic. In a method-based neighborhood we define a two-dimensional “corridor” around the incumbent blocks configuration by imposing exogenous constraints on the solution space of the problem and apply a dynamic programming algorithm capturing the state of the system after each block movement for exploring the neighborhoods. Computational results on medium- and large-size problem instances allow to draw conclusions about the effectiveness of the proposed scheme.
This paper presents a review of four decades of research on dynamic lot-sizing with capacity constraints. We discuss both different modeling approaches to the optimization problems and different algorithmic solution approaches. The focus is on research that separates the lot-sizing problem from the detailed sequencing and scheduling problem. Our conceptional point of reference is the multi-level capacitated lot-sizing problem (MLCLSP). We show how different streams of research emerged over time. One result is that many practically important problems are still far from being solved in the sense that they could routinely be solved close to optimality in industrial practice. Our review also shows that currently mathematical programing and the use of metaheuristics are particularly popular among researchers in a vivid and flourishing field of research.
In this paper, we discuss various concepts of robustness for uncertain multi-objective optimization problems. We extend the concepts of flimsily, highly, and lightly robust efficiency and we collect different versions of minmax robust efficiency and concepts based on set order relations from the literature. Altogether, we compare and analyze ten different concepts and point out their relations to each other. Furthermore, we present reduction results for the class of objective-wise uncertain multi-objective optimization problems.
The problem of defining suitable lines in a public transportation system (bus, railway, tram, or underground) is an important real-world problem that has also been well researched in theory. Driven by applications, it often lacks a clear description, but is rather stated in an informal way. This leads to a variety of different published line planning models. In this paper, we introduce some of the basic line planning models, identify their characteristics, and review literature on models, mathematical approaches, and algorithms for line planning. Moreover, we point out related topics as well as current and future directions of research.
When solving decision problems where multiple conflicting criteria are to be considered simultaneously, decision makers must compare several different alternatives and select the most preferred one. The task of comparing multidimensional vectors is very demanding for the decision maker without any support. Different graphical visualization tools can be used to support and help the decision maker in understanding similarities and differences between the alternatives and graphical illustration is a very important part of decision support systems that are used in solving multiple criteria decision making problems. The visualization task is by no means trivial because, on the one hand, the graphics must be easy to comprehend and not too much information should be lost but, on the other hand, no extra unintentional information should be included. In this paper, we survey and analyze different ways of visualizing a small set of discrete alternatives graphically in the context of multiple criteria decision making. Some of the ways discussed are widely used and some others deserve to be brought into a wider awareness. This survey provides a starting point for all those who deal with multiple criteria decision making problems and need information of what kind of visualization techniques could be put to use in order to support the decision maker better.
As a generalization of fuzzy sets, hesitant fuzzy sets constrain the membership degree of an element to be a set of possible values between zero and one. Those sets are considered useful in handling decision problems defined under uncertainties where decision-makers hesitate among several values before expressing their preferences. Motivated by the idea of traditional ELECTRE methods, the dominance relations and the opposition relations for hesitant fuzzy sets are introduced in this paper. In addition, several desirable properties are studied. Then, a novel outranking relation is developed, based on systematic comparison of assessments given to alternatives for each criterion. An outranking approach for multi-criteria decision-making problems with hesitant fuzzy sets, similar to ELECTRE III, is proposed for ranking alternatives. Finally, an example is given to verify the developed approach and demonstrate its validity and feasibility.
Collaborative transportation planning (CTP) within a coalition of small and medium-sized freight carriers can be used as a powerful instrument to improve the operational efficiency of the coalition members. In such coalitions, transportation requests from different carriers are exchanged in order to reduce the total fulfillment costs. In this paper, the CTP for a set of independent carriers exchanging less-than-truckload transportation requests is considered. The realistic restriction that all collaborating partners have only limited capacities in their fleets is included in the consideration. To keep their autonomy, coalition members keep their sensitive information including customer payments and cost structures unexposed during CTP. A new decentralized request exchange mechanism for CTP is proposed while only vehicle routes are considered for exchange. It is tested on some newly generated instances and the CTP solutions are compared with those obtained by isolated planning without collaboration and those obtained by a heuristic approach for the centralized planning problem. The results indicate that our mechanism is very efficient and effective in terms of realizing potential cost-savings by CTP, even when capacity limitations and restrictions on the exposure of information are explicitly considered.
Distributors are faced with loading constraints in their route planning, e.g.,multi-dimensional packing constraints, unloading sequence constraints, stability constraints and axle weight limits. Ignoring these constraints impairs planning and induces last-minute changes resulting in additional costs. Developing vehicle routing models incorporating loading constraints is critical to more efficient route planning. The last couple of years has seen a huge increase of contributions to this field of research with almost 60 % of these being published after 2009. Our contribution is twofold. First, we overview the recent developments in the literature on all vehicle types in which loading constraints play a key role (trucks, airplanes, ships, and automated guided vehicles), using a state-of-the-art classification scheme to identify the loading constraints considered in each article. Second, we identify research gaps and opportunities for future research.
This paper provides the convex hull description for the basic operation of slow- and quick-start units in power-based unit commitment (UC) problems. The basic operating constraints that are modeled for both types of units are (1) generation limits and (2) minimum up and down times. Apart from this, the startup and shutdown processes are also modeled, using (3) startup and shutdown power trajectories for slow-start units, and (4) startup and shutdown capabilities for quick-start units. In the conventional UC problem, power schedules are used to represent the staircase energy schedule; however, this simplification leads to infeasible energy delivery, as stated in the literature. To overcome this drawback, this paper provides a power-based UC formulation drawing a clear distinction between power and energy. The proposed constraints can be used as the core of any power-based UC formulation, thus tightening the final mixed-integer programming UC problem. We provide evidence that dramatic improvements in computational time are obtained by solving different case studies, for self-UC and network-constrained UC problems.
Pre-positioning of emergency supplies is a means for increasing preparedness for natural disasters. Key decisions in pre-positioning are the locations and capacities of emergency distribution centers, as well as allocations of inventories of multiple relief commodities to those distribution locations. The location and allocation decisions are complicated by uncertainty about if, or where, a natural disaster will occur. An earlier paper (Rawls and Turnquist 44:521–534, 2010) describes a stochastic mixed integer programming formulation to minimize expected costs (including penalties for unmet demand) in such a situation. This paper extends that model with additional service quality constraints. The added constraints ensure that the probability of meeting all demand is at least α, and that the demand is met with supplies whose average shipment distance is no greater than a specific limit. A case study using hurricane threats is used to illustrate the model and how the additional constraints modify the pre-positioning strategy.
As well as nearly always belonging to the class of NP-complete problems, university timetabling problems can be further complicated by the often idiosyncratic requirements imposed by the particular institution being considered. It is perhaps due to this characteristic that in the past decade-or-so, metaheuristics have become increasingly popular in the field of automated timetabling. In this paper we carry out an overview of such applications, paying particular attention to the various methods that have been proposed for dealing and differentiating between constraints of varying importance. Our review allows us to classify these algorithms into three general classes, and we make some instructive comments on each of these.
There are numerous practical vehicle routing applications in which vehicles have to stop at certain facilities along their routes to be able to continue their service. At these stops, the vehicles replenish or unload their cargo or they stop to refuel. In this paper, we study the vehicle routing problem with intermediate stops (VRPIS), which considers stopping requirements at intermediate facilities. Service times occur at these stops and may depend on the load level or fuel level on arrival. This is incorporated into the routing model to respect route duration constraints. We develop an adaptive variable neighborhood search (AVNS) to solve the VRPIS. The adaptive mechanism guides the shaking step of the AVNS by favoring the route and vertex selection methods according to their success within the search. The performance of the AVNS is demonstrated on test instances for VRPIS variants available in the literature. Furthermore, we conduct tests on newly generated instances of the electric vehicle routing problem with recharging facilities, which can also be modeled as VRPIS variant. In this problem, battery electric vehicles need to recharge their battery en route at respective recharging facilities.
The scope of this work covers a real case of elective surgery planning in a Lisbon hospital. The aim is to employ more efficiently the resources installed in the surgical suite of the hospital in question besides improving the functioning of its surgical service. Such a planning sets out to schedule elective surgeries from the waiting list on a weekly time horizon with the objective of maximizing the use of the surgical suite. For this purpose, the authors develop an integer linear programming model. The model is tested using real data obtained from the hospital's record. The non-optimal solutions are further improved by developing a custom-made, simple and efficient improvement heuristic. Application of this heuristic effectively improves almost all non-optimal solutions. The results are analyzed and compared with the actual performance of the surgical suite. This analysis reveals that the solutions obtained using this approach comply with the conditions imposed by the hospital and improve the use of the surgical suite. It also shows that in this case study the plans obtained from the proposed approach may be implemented in real life.
At cross docking terminals, shipments from inbound trucks are unloaded, sorted and moved to dispatch points where they are directly loaded onto outbound trucks for an immediate delivery elsewhere in the distribution system. This warehouse management concept aims at realizing economies in transportation cost by consolidating divergent shipments to full truckloads without requiring excessive inventory at the cross dock. The efficient operation of such a system requires an appropriate coordination of inbound and outbound trucks, e.g. by computerized scheduling procedures.This work introduces a base model for scheduling trucks at cross docking terminals, which relies on a set of simplifying assumptions in order to derive fundamental insights into the underlying problem’s structure, i.e. its complexity, and to develop a building block solution procedure, which might be employed to solve more complex real-world truck scheduling problems.