The production routing problem (PRP) is a difficult optimization problem which arises in the planning of integrated supply chains. The purpose of solving the PRP is the simultaneous optimization of the production, inventory, distribution, and routing decisions, which typically appears in vendor managed inventory systems. In this study, the classical PRP is extended by considering transshipments, either

When modelling a given problem using integer linear programming techniques several possibilities often exist, each resulting in a different mathematical formulation of the problem. Usually, advantages and disadvantages can be identified in any single formulation. In this paper we consider mixed integer linear programs and propose an approach based on Benders decomposition to exploit the advantages

Real-time path planning for large, dense grid-based automated guided vehicle (AGV) systems, used for example to sort parcels, is challenging. Most approaches described in the literature are not fast enough for real-time control or are not able to avoid congestion. This paper presents a dynamic approach using a graph-representation of the grid system layout with vertex weights that are updated over

In many revenue management applications risk-averse decision-making is crucial. In dynamic settings, however, it is challenging to find the right balance between maximizing expected rewards and minimizing various kinds of risk. In existing approaches utility functions, chance constraints, or (conditional) value at risk considerations are used to influence the distribution of rewards in a preferred

In this work, the online printing shop scheduling problem is considered. This challenging real problem, that appears in the nowadays printing industry, can be seen as a flexible job shop scheduling problem with sequence flexibility in which precedence constraints among operations of a job are given by an arbitrary directed acyclic graph. In addition, several complicating particularities such as periods

The Team Orienteering Problem with Time Windows (TOPTW) is an extension of the well-known Orienteering Problem. Given a set of locations, each one associated with a profit, a service time and a time window, the objective of the TOPTW is to plan a set of routes, over a subset of locations, that maximizes the total collected profit while satisfying travel time limitations and time window constraints

To pick up a container from a container terminal, other containers may need to be relocated to other positions. In practice, these relocation moves are usually done when it is busy at a terminal. However, if the crane is idle for some amount of time, it may be more efficient to execute some pre-processing moves to reduce the number of future relocation moves. In this paper, we propose a model for optimal

The bike repositioning and recycling problem (BRRP) is significant to develop a sustainable bike-sharing system and can effectively reduce the imbalance between demand and supply. This study investigates the static repositioning and recycling problem of a bike-sharing system, which is formulated as an integer linear programming model. The BRRP is a variant of the multi-depot simultaneous pickup and

Natural catastrophes with their widespread damage can overwhelm the financial systems of large communities. Catastrophe insurance is a well-understood financial risk transfer mechanism, aiming to provide resilience in the face of adversity. However, catastrophe insurance has generally a low penetration, mainly due to its high cost or to distrust of the product in providing a fast financial recovery

Taking fairness concerns of retailer into account, we extend the classical newsvendor model to a new type with Nash bargaining preference. Depending on the demand forms, three models are proposed: one benchmark case for deterministic demand, and two stochastic demand cases for multiplicative and additive patterns, respectively. We mathematically prove the existence and uniqueness of the optimal joint

Metaheuristic algorithms offer unique opportunities in problem solving. Although they do not guarantee optimality, it has been shown by numerous publications that they can achieve excellent results in acceptable time. Particularly in real-life production systems, which are mostly comprised of complex discrete optimization problems, the merit should be finding appropriate and efficient solutions in

This paper proposes an interesting variant of the parallel machine scheduling problem with sequence-dependent setup times, where a subset of jobs has to be selected to guarantee a minimum profit level while the total completion time is minimized. The problem is addressed under uncertainty, considering both the setup and the processing times as random parameters. To deal with the uncertainty and to

Freight forwarding companies provide transportation services to shipping companies by organizing shipment of freights from origins to destinations. They typically handle long-haul intermodal transportation requiring synchronization among different transportation legs and modes as well as complex bureaucratic and administrative operations, like customs clearance for international transportation. Because

The Team Orienteering Problem (TOP) is a well-known NP-Hard vehicle routing problem in which one maximizes the collected profits for visiting some nodes. In this paper, we propose a Hybrid Adaptive Large Neighborhood Search (HALNS) to solve this problem. Our algorithm combines the exploration power of ALNS with local search procedures and an optimization stage using a Set Packing Problem to further

Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a Constraint Programming approach capable of automating the short-term scheduling process in a cut-and-fill mine. The approach extends previous work by accounting for fleet travel times, and thus captures an important aspect of the real-world machine scheduling problem. We introduce

This paper proposed a novel approach for a practical version of the cold chain, namely location-routing problem-based low-carbon cold chain (LRPLCCC). In the proposed bi-objective model, the first objective is the total logistics cost, including the fixed costs of the opened depots and leased vehicles, as well as the cost of fuel consumption and carbon emissions, and the second is to minimize the amount

The approximate dynamic programming (ADP) method based on the design and analysis of computer experiments (DACE) approach has been demonstrated as an effective method to solve multistage decision-making problems in the literature. However, this method is still not efficient for infinite-horizon optimization considering the required large volume of sampling in the state space and high-quality value

This paper surveys the state-of-the-art optimization approaches in the civil application of drone operations (DO) and drone-truck combined operations (DTCO) including construction/infrastructure, agriculture, transportation/logistics, security/disaster management, entertainment/media, etc. In particular, this paper reviews ongoing research on various optimization issues related to DO and DTCO including

This paper addresses the problem of scheduling a set of jobs that are released over the time on a set of identical parallel machines, aiming at the minimization of the total weighted completion time. This problem, referred to as P|rj|∑wjCj, is of great importance in practice, because it models a variety of real-life applications. Despite its importance, the P|rj|∑wjCj has not received much attention

Scheduling problems are quite difficult to solve since in many cases no exact algorithms exist which can obtain the optimal solution in a reasonable amount of time. Therefore, these problems are often solved by using various metaheuristic methods, like genetic algorithms. To use these methods, the first step which needs to be performed is to define an encoding scheme that will be used to represent

The stable set problem is a fundamental combinatorial optimisation problem, that is known to be very difficult in both theory and practice. Some of the solution algorithms in the literature are based on 0-1 linear programming formulations. We examine an entire family of such formulations, based on so-called clique and nodal inequalities. As well as proving some theoretical results, we conduct extensive

The utilization of real-time information in production scheduling decisions becomes possible with the help of new developments in Information Technology and Industrial Informatics, such as Industry 4.0. Regardless of the beliefs that the availability of such information will enhance scheduling decisions, several questions and concerns have been reported. One such question is to what extent can the

A production-routing problem with price-dependent demand (PRP-PD) is studied in this paper. Demand follows a general convex, differentiable, continuous and strictly decreasing function in price. The problem is modeled as a mixed integer nonlinear program (MINLP). Two Outer Approximation (OA) based algorithms are developed to solve the PRP-PD. The efficiency of the proposed algorithms in comparison

Achieving cost targets is a major concern for business managers. In this paper, we consider two risk management criteria for a production-inventory system under a cost target: Probability of Loss and Expected Loss. We study two models with stochastic demand and production: The unit stockout cost model and the backlogging cost rate model. We analyze a limited-information setting that is an excellent

Recently developed technologies are changing mobility dramatically. Autonomous and interactive vehicles enable a coordination of the sat-nav devices of traveling vehicles aimed at assigning paths with the goal of eliminating congestion and, more in general, of reducing the total travel time in traffic networks. In this paper we tackle the problem of finding a traffic assignment that minimizes the total

In today's centralized globalized economy, large manufacturing firms operate more than one production center. Therefore, the multifactory production scheduling environment, so-called the distributed scheduling problem, is gaining more and more attention from the authors. In this context, which factory will manufacture which product is an important decision making process. The distributed permutation

University Course Timetabling Problem (UCTP) is one of the most studied timetabling problems. In this work, the problem of finding a feasible solution for the UCTP, i.e., a solution that satisfies all hard constraints, is addressed. To cater for the algorithm design, the problem is firstly reformulated into a version where only one hard constraint needs to be considered in the minimization procedure

Various commodities including blood, pharmaceutical, and agricultural products are perishable in nature and require special storage conditions, such as controlled temperature. However, expanding the space of cold storage is expensive. Therefore, storage capacity frequently restricts the operational efficiency of enterprises in the perishable product industry. Numerous enterprises have adopted the policies

Production scheduling and maintenance planning are two of the most important tasks that managers face before implementing their decisions on the shop floor. Another issue managers have to keep in mind is the proper allocation of various resources for production. These issues create difficulties in the planning process. In this paper, we propose a bi-objective model that integrates the three aforementioned

This paper addresses the multi-period ambulance redeployment planning problem in a two-tiered Emergency Medical System (EMS) where two types of ambulances are used to respond to two categories of emergency calls. In order to account for the uncertainty inherent to both categories of demand, we propose a two-stage stochastic programming model that aims at finding a cost-effective ambulance redeployment

The insertion of randomly-arriving high-priority customers (HCs) into existing queues leads to longer waiting time of regular or scheduled customers. However, given their different levels of priority, not all HCs need to be served immediately. To reduce the waiting time for regular customers without affecting the service quality for HCs, this paper addresses the optimal insertion problem of HCs by

We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another

This paper proposes a novel approach for generating decision rules to exercise flexibility in capacity expansion. The proposed approach differs from other decision rule generation methods by integrating gene expression programming. This approach allows parameters to be automatically selected from a database and optimally combined to form decision rules, allowing both the structure and parameters of

Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually

In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), goods have to be transported from different origins to different destinations, and each customer has both a delivery and a pickup demand to be satisfied simultaneously. The VRPSPD has been around for about 30 years, and significant progress has since been made on this problem and its variants. This paper aims to comprehensively

This paper introduces the Two-Echelon Inventory-Routing Problem with Fleet Management. This problem arises under a two-echelon vendor-managed inventory system when a company must make vehicle routing and inventory management decisions, while renting a fleet subject to short- and mid-term agreements. Different chemical products are transported contaminating the vehicles that may require cleaning activities

In real-world manufacturing environments, the execution of a schedule often encounters uncertain events, which will bring the risks of performance deterioration and production system instability. This study addresses the optimization of risks both in performance and stability for the job shop scheduling under random machine breakdowns, in which three objectives: makespan, makespan risk and stability

Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong resemblance to order batching in the warehousing

In this study, we investigate the lower bound and dominance conditions of the N-job and M-machine permutation flowshop scheduling problem to minimize the makespan based on machine idle times. We have developed and validated three dominance conditions and a lower bound based on a new approach that minimizes the machine idle times in a flowshop to facilitate the efficiency of the branch and bound approach

The resource-constrained project scheduling problem (RCPSP) is one of the most studied problems in the project scheduling literature, and aims at constructing a project schedule with a minimum makespan that satisfies both the precedence relations of the network and the limited availability of the renewable resources. The problem has attracted attention due to its NP hardness status, and different algorithms

The purpose of this paper is to develop a data-driven matheuristic for the Commodity-Split Multi-Compartment Capacitated Arc Routing Problem (CSMC-CARP). This problem arises in curbside waste collection, where there are different recyclable waste types called fractions. The CSMC-CARP is defined on an undirected graph with a limited heterogeneous fleet of multi-compartment vehicle types based at a depot

The Traveling Salesperson Problem with Hotel Selection (TSPHS) is a realistic extension of the classic Traveling Salesperson Problem recently introduced to the literature. In the TSPHS, there is a time limit that restricts the visits that can be performed in a single day. Therefore, several days may be necessary to visit all clients. The salesperson has to spend the night in one of the available hotels

The flexible job shop scheduling problem (FJSP) is a typical scheduling problem in practical production and has been proven to be a NP-hard problem. The study of FJSP is important to remarkably direct actual manufacturing processes. The paper proposes an improved particle swarm optimization (PSO) algorithm for solving FJSP and obtains beneficial solutions by improvement on encoding/decoding scheme

Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such systems is to minimize the number of servers required to process a given set of tasks under server capacity constraints. This objective leads to the well-known bin packing problem. In this study, we consider a generalization of this problem with

This paper focuses on two components of idle time and waiting time, namely core idle time, ∑CITi, and core waiting time, ∑CWTj. Both measures are relevant indicators of the efficiency of production systems, since they are directly related to machine utilization and to the flow of jobs, and they can be used as scheduling criteria if no-idle and no-wait constraints do not have to be strictly enforced

The shortest path problem arises in several contexts like transportation, telecommunication or data analysis. New requirements in solving practical problems (e.g., efficient content delivery for information-centric networks, urban passenger transport system or social network) impose that more than one criterion should be considered. Since the objectives are in conflict, the solution is not unique,

Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it has received renewed attention recently as it has shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information

The issue of fairness has received attention from researchers in many fields, including combinatorial optimisation. One way to drive the solution toward fairness is to use a modified objective function that involves so-called ℓp-norms. If done in a naive way, this approach leads to large and symmetric mixed-integer nonlinear programs (MINLPs), that may be difficult to solve. We show that, for some

We consider a non-linear version of the Generalized Assignment Problem, a well-known strongly NP-hard combinatorial optimization problem. We assume that the variables are continuous and that objective function and constraints are defined by non-linear functions of the variables. A mathematical model is introduced and used to derive upper bounds on the optimal solution value. We present constructive

Personnel scheduling consists of determining least-cost employee work schedules to cover the demand of one or several jobs in each period of a time horizon. During the operations, several minor disruptions caused, for example, by a late employee may occur and must be addressed in real time by re-scheduling certain employees. In this paper, we develop a fast re-scheduling heuristic that can be used

Typically, Graph Coloring Problem (GCP) is one of the key features for graph stamping in graph theory. The general approach is to paint at least edges, vertices, or the surface of the graph with some colors. In the simplest case, a kind of coloring is preferable in which two vertices are not adjacent to the same color. Similarly, the two edges in the same joint should not have the same color. In addition

This work analyzes an inventory system with stock-dependent demand and non-linear holding cost. It presents a new approach to maximize the return on investment, that is, the profit/cost ratio. When an inventory manager can invest in different projects and the resources are limited, it seems sensible to select those projects that provide a higher return on investment. Thus, the goal of the manager will

Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem

This paper addresses the joint optimization of production capacity and safety stocks in supply chains under the guaranteed service approach (GSA). The integrated problem is formulated as a mixed integer nonlinear program (MINLP) and solution procedures are proposed in the cases of general acyclic and spanning tree networks. For general acyclic supply chains, the integrated problem is solved using a

Many combinatorial problems addressed in the literature are modeled using binary matrices. It is often of interest to verify whether these matrices hold the consecutive ones property (C1P), which implies that there exists a permutation of the columns of the matrix such that all nonzero elements can be placed contiguously, forming a unique 1-block in every row. The minimization of the number of 1-blocks

A heuristic method based on column generation is presented for the nurse rostering problem. The method differs significantly from an exact column generation approach or a branch and price algorithm because it performs an incomplete search which quickly produces good solutions but does not provide valid lower bounds. It is effective on large instances for which it has produced best known solutions on

The Quota Travelling Salesman Problem with Passengers, Incomplete Ride, and Collection Time is a new version of the Quota Travelling Salesman Problem. In this problem, the salesman uses a flexible ridesharing system to minimize travel costs while visiting some vertices to satisfy a pre-established quota. We consider operational constraints regarding vehicle capacity, travel time, passenger limitations

The scheduling of an Agile Earth Observation Satellite (AEOS) consists of selecting and scheduling a subset of possible targets for observation in order to maximize the collected profit related to the images while satisfying its operational constraints. In this problem, a set of candidate targets for observation is given, each with a time-dependent profit and a visible time window. The exact profit

In this paper, we extend, analyze and evaluate the exponential multi-insertion neighborhood for the vehicle routing problem with unit demands, originally proposed by Angel et al. (2008). In this neighborhood, a neighbor solution is obtained by the removal of a set of mobile nodes from a solution and a subsequent best reinsertion. At first, we examine theoretical properties of the neighborhood, such