The relationship between supply chain factors and adoption of e-Collaboration tools: An empirical examination

We consider minimum-cost scheduling of different vehicle types on a predetermined set of one-way trips. Trips have predetermined ready times, deadlines and associated demands. All trips must be performed. The total time of operations on any vehicle is limited. We develop a mixed integer model to find the optimal number of vehicles at a minimum cost. Based on the hard nature of the problem, we propose six heuristics. Computational results reveal that heuristics return exceptionally good solutions for problem instances with up to 100 jobs in very small computation times, and are likely to perform well for larger instances.     

Hybrid flow shop scheduling with parallel batching

In this research the problem of parallel batch scheduling in a hybrid flow shop environment with minimizing C_max is studied. In parallel batching it is assumed that machines in some stages are able to process a number of operations simultaneously. Since the problem is NP-hard, three heuristic algorithms are developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed heuristics. Several test problems have been solved using these heuristics and results compared. To further enhance the solution quality, a three dimensional genetic algorithm (3DGA) is also developed. Several test problems have been solved using 3DGA and the results indicate its superiority to the other heuristics.     

Scheduling a capacitated single machine with time deteriorating job values

We address the scheduling problem with the following characteristics: (i) there is a single machine available, (ii) the machine has limited capacity, and (iii) job value deteriorates with time. The problem is motivated from several real world situations, such as, downloading process of web pages, and scheduling of multiplexes. Since the problem is NP-hard, we propose new heuristics based on a multiplicative piece-wise metric as an approximation of the slope of job value deterioration. Computational results show that the proposed heuristics perform better than other existing heuristics for similar types of problems.     

The storage location assignment problem for outbound containers in a maritime terminal

This paper addresses the storage location assignment problem for outbound containers. The problem is decomposed into two stages. The yard bays and the amount of locations in each yard bay, which will be assigned to the containers bounded for different ships, are determined in the first stage. The exact storage location for each container is determined in the second stage. The problem in the first stage is solved by a mixed integer programming model, while a hybrid sequence stacking algorithm is applied to solve the problem in the second stage. Experimental results show that the proposed approach is effective and efficient in solving the storage location assignment problem for outbound containers.     

A local search algorithm for the optimization of the stochastic economic lot scheduling problem

This paper describes a model for the stochastic economic lot scheduling problem (SELSP) and a Local Search heuristic to find close to optimal solutions to this model. The SELSP considers multiple products, which have to be scheduled on a single facility with limited capacity and significant setup times and costs. The demand is modeled as a stationary compound renewal process. The objective is to find a schedule that minimizes the long-run average costs for setups and inventories while satisfying a given fill rate. We use a cyclic scheduling approach in which the individual cycle time of each product is a multiple of some basic period (fundamental cycle).For the deterministic version of the SELSP, efficient heuristics have been developed which guarantee the feasibility of the solution by adding an additional constraint to the problem. In our case this is not sufficient, because for the calculation of the average inventory levels and fill rates we need to develop a schedule with detailed timing of the lots. We present an efficient heuristic for this scheduling problem, which can also be used to check the feasibility of the solution. Thereby, the most time-consuming step (the calculation of average inventory levels and fill rates) is only performed for a limited set of candidates.The algorithm was tested on deterministic benchmark problems from literature and on a large set of stochastic instances. We report on the performance of the heuristic in both cases and try to identify the main factors influencing the objective.     

Tabu search for minimizing total tardiness in a job shop

This paper presents a tabu search approach to minimize total tardiness for the job shop scheduling problem. The method uses dispatching rules to obtain an initial solution and searches for new solutions in a neighborhood based on the critical paths of the jobs. Diversification and intensification strategies are suggested. For small problems the solutions’ quality is evaluated against optimal solution values and for large problems the tabu search performance is compared with two heuristics proposed in the literature.     

Common due-window assignment and scheduling of linear time-dependent deteriorating jobs and a deteriorating maintenance activity

Due-window assignment and production scheduling are important issues in operations management. In this study we investigate the problem of common due-window assignment and scheduling of deteriorating jobs and a maintenance activity simultaneously on a single-machine. We assume that the maintenance duration depends on its starting time. We provide polynomial time solutions for the problem and some of its special cases, where the objective is to simultaneously minimize the earliness, tardiness, due-window starting time, and due-window size costs.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.     

A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine flow-shop scheduling system

We analyze a two-machine flow-shop scheduling problem in which the job processing times are controllable by the allocation of resources to the job operations and the resources can be used in discrete quantities. We provide a bicriteria analysis of the problem where the first criterion is to maximize the weighted number of just-in-time jobs and the second criterion is to minimize the total resource consumption cost. We prove that although the problem is known to be NP-hard even for constant processing times, a pseudo-polynomial time algorithm for its solution exists. In addition, we show how the pseudo-polynomial time algorithm can be converted into a two-dimensional fully polynomial approximation scheme for finding an approximate Pareto solution.     

New batch construction heuristics to optimise the performance of order picking systems

Two new batch construction heuristics called K-means Batching (KMB) and Self-organisation Map Batching (SOMB) are developed and verified by simulation experiments. Both KMB and SOMB have a preferment of superior performance in total travel distance and average picking vehicle utility, and even a conspicuous improvement in total CPU running time. Besides, this paper investigates the overall performance of order picking systems integrating storage assignment, order batching and picker routing to find the optimal policy combinations under different order types. The sensitivity analysis is performed to distinguish the relative importance of the various strategies to enhance the performance of operations management.     

Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation

We present a guideline for selecting the inventory management policy for a specialty chemical plant that produces products with limited shelf life, by producing multiple grades of bulk chemical and packaging them into a large variety of bottles. The policy for each product was selected based on a cost-benefit analysis, using the multiple objectives of maximizing the on time order fulfillment, minimizing the production (cleaning and shelf life expiration) and inventory costs. To generate realistic cost estimates, we developed a discrete event simulation model that included demand variability, planning and scheduling policies, and operational details. We evaluated three different policies: make-to-stock (MTS), postponement, and combined MTS/postponement. We also evaluated different storage media and optimized the number of storage units for products selected for postponement. A combined MTS/postponement policy, in which postponement is applied to products with low to medium demand and high expiration provided lowest costs without significantly impacting the customer on time order fulfillment. For all other products, a MTS policy is applied to maximize order fulfillment on time, reduce cleaning costs and to reduce number of storage units.     

Two-machine flow shop scheduling of polyurethane foam production

This paper studies a two-machine flow shop scheduling problem with a supporting precedence relation. The model originates from a real production context of a chemical factory that produces foam-rubber products. We extend the traditional two-machine flow shop by dividing the operations into two categories: supporting tasks and regular jobs. In the model, several different compositions of foam rubber can be mixed at the foam blooming stage, and products are processed at the manufacturing stage. Each job (product) on the second machine cannot start until its supporting tasks (parts) on the first machine are all finished and the second machine is not occupied. The objective is to find a schedule that minimizes the total job completion time. The studied problem is strongly NP-hard. In this paper, we propose a branch-and-bound algorithm incorporating a lower bound and two dominance rules. We also design a simple heuristic and an iterated local search (ILS) algorithm to derive approximate solutions. The performances of the proposed algorithms are examined through computational experiments.     

Synergistic valuations and efficiency in spectrum auctions

In spectrum auctions, bidders typically have synergistic values for combinations of licenses. This has been the key argument for the use of combinatorial auctions in the recent years. Considering synergistic valuations turns the allocation problem into a computationally hard optimization problem that generally cannot be approximated to a constant factor in polynomial time. Ascending auction designs such as the Simultaneous Multiple Round Auction (SMRA) and the single-stage or two-stage Combinatorial Clock Auction (CCA) can be seen as simple heuristic algorithms to solve this problem. Such heuristics do not necessarily compute the optimal solution, even if bidders are truthful. We study the average efficiency loss that can be attributed to the simplicity of the auction algorithm with different levels of synergies. Our simulations are based on realistic instances of bidder valuations we inferred from bid data from the 2014 Canadian 700 MHz auction. The goal of the paper is not to reproduce the results of the Canadian auction but rather to perform “out-of-sample” counterfactuals comparing SMRA and CCA under different synergy conditions when bidders maximize payoff in each round. With “linear” synergies, a bidder's marginal value for a license grows linearly with the total number of licenses won, while with the “extreme national” synergies, this marginal value is independent of the number of licenses won unless the bidder wins all licenses in a national package. We find that with the extreme national synergy model, the CCA is indeed more efficient than SMRA. However, for the more realistic case of linear synergies, SMRA outperforms various versions of CCA that have been implemented in the field including the one used in the Canadian 700 MHz auction. Overall, the efficiency loss of all ascending auction algorithms is small even with high synergies, which is remarkable given the simplicity of the algorithms.     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

A hybrid artificial bee colony algorithm for the job shop scheduling problem

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.     

Analyzing operational real options in metal mining investments with a system dynamic model

This article presents a detailed system dynamic (SD) model of a metal mining investment that is usable in ex-ante profitability and operations management analysis. We show how the SD model can be used to analyze the profitability effect of three operational real options: the option to temporarily close production, the option to abandon production, and the option to increase production through cutoff grade change. The SD model allows for intuitive modeling of the multiple interactive real options and arriving at results that are difficult, or impossible, to reach with commonly used spreadsheet software. We also analyze the effect of mining project debt ratio to the project value and show that correctly choosing the debt ratio affects project profitability. The effect on the project value of using three different future metal price scenarios with two different stochastic processes is illustrated to highlight the importance of correct process selection in modeling future metal price paths. A realistic case of a high-cost nickel (Ni) metal mine is used as a basis for the presented numerical illustration of the model.     

Tying the pieces together: A normative framework for integrating sales and project operations

Synchronizing sales efforts with project operations, such that (1) there is a steady flow of work that can be completed in a non-chaotic project environment, (2) resources maintain high utilization, and (3) desired deliverables reach customers within the promised lead-time, is extremely difficult. This paper proposes a normative approach to uniting the sales process with project operations capacity by coordinating movement of potential customers through the sales funnel with the company's internal project capacity. It also addresses contingencies with respect to company throughput as a result of changes in managing the market, the sales funnel, and project operations, while taking into consideration variation in scheduling as well as in managing project task and duration uncertainty.     

Sensitivity results considering rescheduling by AND/OR graphs

Closely connected with scheduling are also rescheduling considerations which can obviously be addressed by sensitivity analysis. We discuss this question by using AND/OR graphs and show how sensitivity analysis can be performed in this context. We show that AND/OR graphs can be used to model job shop problems and alternative process scheduling, hence sensitivity analysis can be performed in these contexts as well.     

Heuristics in customer‐supplier interaction

In customer-supplier relationships individuals representing respective companies interact to solve a number of adaptive problems and how the relationships will develop depends on the solutions they jointly devise. Their interactive meetings are characterized by conditions of limited time, information, and processing capacity that render extensive information gathering and analytical cognitive elaboration impracticable and lead the individual actors to adopt behaviors based on rules and heuristics. In this paper, we review the extant literature on heuristics in management and present the findings of an exploratory study on the use of heuristics in interactions in customer-supplier relationships. We found that preparing for meetings and during them actors use six sets of heuristics. Heuristics used in interaction during the meetings concern the degree of adaptation to the specific counterpart, how to react to unexpected developments, and a general code of conduct. In preparing for meetings, heuristics are used to define the information to collect, its sources, and how to use it. Our study suggests heuristics used are personal, originate in both organizational norms and personal experience, and are seldom shared with others.